4416 lines (4415 with data), 252.5 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n",
" warnings.warn(msg, DataConversionWarning)\n",
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n",
" warnings.warn(msg, DataConversionWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9440567436313729\n",
"0.7222222222222222\n",
"0.8\n"
]
}
],
"source": [
"import torch \n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import torch.optim as optim\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import math\n",
"import sklearn.preprocessing as sk\n",
"import seaborn as sns\n",
"from sklearn import metrics\n",
"from sklearn.feature_selection import VarianceThreshold\n",
"from sklearn.model_selection import train_test_split\n",
"from utils import AllTripletSelector,HardestNegativeTripletSelector, RandomNegativeTripletSelector, SemihardNegativeTripletSelector # Strategies for selecting triplets within a minibatch\n",
"from metrics import AverageNonzeroTripletsMetric\n",
"from torch.utils.data.sampler import WeightedRandomSampler\n",
"from sklearn.metrics import roc_auc_score\n",
"from sklearn.metrics import average_precision_score\n",
"import random\n",
"from random import randint\n",
"from sklearn.model_selection import StratifiedKFold\n",
"\n",
"save_results_to = '/home/hnoghabi/EGFR/'\n",
"torch.manual_seed(42)\n",
"random.seed(42)\n",
"\n",
"GDSCE = pd.read_csv(\"GDSC_exprs.z.EGFRi.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \",\")\n",
"GDSCE = pd.DataFrame.transpose(GDSCE)\n",
"\n",
"GDSCM = pd.read_csv(\"GDSC_mutations.EGFRi.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"GDSCM = pd.DataFrame.transpose(GDSCM)\n",
"GDSCM = GDSCM.loc[:,~GDSCM.columns.duplicated()]\n",
"\n",
"GDSCC = pd.read_csv(\"GDSC_CNA.EGFRi.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"GDSCC.drop_duplicates(keep='last')\n",
"GDSCC = pd.DataFrame.transpose(GDSCC)\n",
"GDSCC = GDSCC.loc[:,~GDSCC.columns.duplicated()]\n",
"\n",
"PDXEerlo = pd.read_csv(\"PDX_exprs.Erlotinib.eb_with.GDSC_exprs.Erlotinib.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \",\")\n",
"PDXEerlo = pd.DataFrame.transpose(PDXEerlo)\n",
"PDXMerlo = pd.read_csv(\"PDX_mutations.Erlotinib.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \",\")\n",
"PDXMerlo = pd.DataFrame.transpose(PDXMerlo)\n",
"PDXCerlo = pd.read_csv(\"PDX_CNA.Erlotinib.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \",\")\n",
"PDXCerlo.drop_duplicates(keep='last')\n",
"PDXCerlo = pd.DataFrame.transpose(PDXCerlo)\n",
"PDXCerlo = PDXCerlo.loc[:,~PDXCerlo.columns.duplicated()]\n",
"\n",
"PDXEcet = pd.read_csv(\"PDX_exprs.Cetuximab.eb_with.GDSC_exprs.Cetuximab.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \",\")\n",
"PDXEcet = pd.DataFrame.transpose(PDXEcet)\n",
"PDXMcet = pd.read_csv(\"PDX_mutations.Cetuximab.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \",\")\n",
"PDXMcet = pd.DataFrame.transpose(PDXMcet)\n",
"PDXCcet = pd.read_csv(\"PDX_CNA.Cetuximab.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \",\")\n",
"PDXCcet.drop_duplicates(keep='last')\n",
"PDXCcet = pd.DataFrame.transpose(PDXCcet)\n",
"PDXCcet = PDXCcet.loc[:,~PDXCcet.columns.duplicated()]\n",
"\n",
"selector = VarianceThreshold(0.05)\n",
"selector.fit_transform(GDSCE)\n",
"GDSCE = GDSCE[GDSCE.columns[selector.get_support(indices=True)]]\n",
"\n",
"GDSCM = GDSCM.fillna(0)\n",
"GDSCM[GDSCM != 0.0] = 1\n",
"GDSCC = GDSCC.fillna(0)\n",
"GDSCC[GDSCC != 0.0] = 1\n",
"\n",
"ls = GDSCE.columns.intersection(GDSCM.columns)\n",
"ls = ls.intersection(GDSCC.columns)\n",
"ls = ls.intersection(PDXEerlo.columns)\n",
"ls = ls.intersection(PDXMerlo.columns)\n",
"ls = ls.intersection(PDXCerlo.columns)\n",
"ls = ls.intersection(PDXEcet.columns)\n",
"ls = ls.intersection(PDXMcet.columns)\n",
"ls = ls.intersection(PDXCcet.columns)\n",
"ls2 = GDSCE.index.intersection(GDSCM.index)\n",
"ls2 = ls2.intersection(GDSCC.index)\n",
"ls3 = PDXEerlo.index.intersection(PDXMerlo.index)\n",
"ls3 = ls3.intersection(PDXCerlo.index)\n",
"ls4 = PDXEcet.index.intersection(PDXMcet.index)\n",
"ls4 = ls4.intersection(PDXCcet.index)\n",
"ls = pd.unique(ls)\n",
"\n",
"PDXEerlo = PDXEerlo.loc[ls3,ls]\n",
"PDXMerlo = PDXMerlo.loc[ls3,ls]\n",
"PDXCerlo = PDXCerlo.loc[ls3,ls]\n",
"PDXEcet = PDXEcet.loc[ls4,ls]\n",
"PDXMcet = PDXMcet.loc[ls4,ls]\n",
"PDXCcet = PDXCcet.loc[ls4,ls]\n",
"GDSCE = GDSCE.loc[:,ls]\n",
"GDSCM = GDSCM.loc[:,ls]\n",
"GDSCC = GDSCC.loc[:,ls]\n",
"\n",
"GDSCR = pd.read_csv(\"GDSC_response.EGFRi.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \",\")\n",
"\n",
"GDSCR.rename(mapper = str, axis = 'index', inplace = True)\n",
"\n",
"d = {\"R\":0,\"S\":1}\n",
"GDSCR[\"response\"] = GDSCR.loc[:,\"response\"].apply(lambda x: d[x])\n",
"\n",
"responses = GDSCR\n",
"drugs = set(responses[\"drug\"].values)\n",
"exprs_z = GDSCE\n",
"cna = GDSCC\n",
"mut = GDSCM\n",
"expression_zscores = []\n",
"CNA=[]\n",
"mutations = []\n",
"for drug in drugs:\n",
" samples = responses.loc[responses[\"drug\"]==drug,:].index.values\n",
" e_z = exprs_z.loc[samples,:]\n",
" c = cna.loc[samples,:]\n",
" m = mut.loc[samples,:]\n",
" m = mut.loc[samples,:]\n",
" # next 3 rows if you want non-unique sample names\n",
" e_z.rename(lambda x : str(x)+\"_\"+drug, axis = \"index\", inplace=True)\n",
" c.rename(lambda x : str(x)+\"_\"+drug, axis = \"index\", inplace=True)\n",
" m.rename(lambda x : str(x)+\"_\"+drug, axis = \"index\", inplace=True)\n",
" expression_zscores.append(e_z)\n",
" CNA.append(c)\n",
" mutations.append(m)\n",
"responses.index = responses.index.values +\"_\"+responses[\"drug\"].values\n",
"GDSCEv2 = pd.concat(expression_zscores, axis =0 )\n",
"GDSCCv2 = pd.concat(CNA, axis =0 )\n",
"GDSCMv2 = pd.concat(mutations, axis =0 )\n",
"GDSCRv2 = responses\n",
"\n",
"ls2 = GDSCEv2.index.intersection(GDSCMv2.index)\n",
"ls2 = ls2.intersection(GDSCCv2.index)\n",
"GDSCEv2 = GDSCEv2.loc[ls2,:]\n",
"GDSCMv2 = GDSCMv2.loc[ls2,:]\n",
"GDSCCv2 = GDSCCv2.loc[ls2,:]\n",
"GDSCRv2 = GDSCRv2.loc[ls2,:]\n",
"\n",
"Y = GDSCRv2['response'].values\n",
"\n",
"PDXRcet = pd.read_csv(\"PDX_response.Cetuximab.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \",\")\n",
"PDXRcet.loc[PDXRcet.iloc[:,0] == 'R'] = 0\n",
"PDXRcet.loc[PDXRcet.iloc[:,0] == 'S'] = 1\n",
"PDXRcet = PDXRcet.loc[ls4,:]\n",
"Ytscet = PDXRcet['response'].values \n",
"\n",
"PDXRerlo = pd.read_csv(\"PDX_response.Erlotinib.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \",\")\n",
"PDXRerlo.loc[PDXRerlo.iloc[:,0] == 'R'] = 0\n",
"PDXRerlo.loc[PDXRerlo.iloc[:,0] == 'S'] = 1\n",
"PDXRerlo = PDXRerlo.loc[ls3,:]\n",
"Ytserlo = PDXRerlo['response'].values \n",
"\n",
"hdm1 = 32\n",
"hdm2 = 16\n",
"hdm3 = 256\n",
"rate1 = 0.5\n",
"rate2 = 0.8\n",
"rate3 = 0.5\n",
"rate4 = 0.3\n",
"\n",
"scalerGDSC = sk.StandardScaler()\n",
"scalerGDSC.fit(GDSCEv2.values)\n",
"X_trainE = scalerGDSC.transform(GDSCEv2.values)\n",
"X_testEerlo = scalerGDSC.transform(PDXEerlo.values) \n",
"X_testEcet = scalerGDSC.transform(PDXEcet.values) \n",
"\n",
"X_trainM = np.nan_to_num(GDSCMv2.values)\n",
"X_trainC = np.nan_to_num(GDSCCv2.values)\n",
"X_testMerlo = np.nan_to_num(PDXMerlo.values)\n",
"X_testCerlo = np.nan_to_num(PDXCerlo.values)\n",
"X_testMcet = np.nan_to_num(PDXMcet.values)\n",
"X_testCcet = np.nan_to_num(PDXCcet.values)\n",
"\n",
"TX_testEerlo = torch.FloatTensor(X_testEerlo)\n",
"TX_testMerlo = torch.FloatTensor(X_testMerlo)\n",
"TX_testCerlo = torch.FloatTensor(X_testCerlo)\n",
"ty_testEerlo = torch.FloatTensor(Ytserlo.astype(int))\n",
"\n",
"TX_testEcet = torch.FloatTensor(X_testEcet)\n",
"TX_testMcet = torch.FloatTensor(X_testMcet)\n",
"TX_testCcet = torch.FloatTensor(X_testCcet)\n",
"ty_testEcet = torch.FloatTensor(Ytscet.astype(int))\n",
"\n",
"n_sampE, IE_dim = X_trainE.shape\n",
"n_sampM, IM_dim = X_trainM.shape\n",
"n_sampC, IC_dim = X_trainC.shape\n",
"\n",
"h_dim1 = hdm1\n",
"h_dim2 = hdm2\n",
"h_dim3 = hdm3 \n",
"Z_in = h_dim1 + h_dim2 + h_dim3\n",
"\n",
"costtr = []\n",
"auctr = []\n",
"costts = []\n",
"aucts = []\n",
"\n",
"class AEE(nn.Module):\n",
" def __init__(self):\n",
" super(AEE, self).__init__()\n",
" self.EnE = torch.nn.Sequential(\n",
" nn.Linear(IE_dim, h_dim1),\n",
" nn.BatchNorm1d(h_dim1),\n",
" nn.ReLU(),\n",
" nn.Dropout(rate1))\n",
" def forward(self, x):\n",
" output = self.EnE(x)\n",
" return output\n",
"\n",
"class AEM(nn.Module):\n",
" def __init__(self):\n",
" super(AEM, self).__init__()\n",
" self.EnM = torch.nn.Sequential(\n",
" nn.Linear(IM_dim, h_dim2),\n",
" nn.BatchNorm1d(h_dim2),\n",
" nn.ReLU(),\n",
" nn.Dropout(rate2))\n",
" def forward(self, x):\n",
" output = self.EnM(x)\n",
" return output \n",
"\n",
"\n",
"class AEC(nn.Module):\n",
" def __init__(self):\n",
" super(AEC, self).__init__()\n",
" self.EnC = torch.nn.Sequential(\n",
" nn.Linear(IM_dim, h_dim3),\n",
" nn.BatchNorm1d(h_dim3),\n",
" nn.ReLU(),\n",
" nn.Dropout(rate3))\n",
" def forward(self, x):\n",
" output = self.EnC(x)\n",
" return output \n",
"\n",
"class Classifier(nn.Module):\n",
" def __init__(self):\n",
" super(Classifier, self).__init__()\n",
" self.FC = torch.nn.Sequential(\n",
" nn.Linear(Z_in, 1),\n",
" nn.Dropout(rate4),\n",
" nn.Sigmoid())\n",
" def forward(self, x):\n",
" return self.FC(x)\n",
"\n",
"torch.cuda.manual_seed_all(42)\n",
"\n",
"AutoencoderE = torch.load('EGFRv2Exprs.pt')\n",
"AutoencoderM = torch.load('EGFRv2Mut.pt')\n",
"AutoencoderC = torch.load('EGFRv2CNA.pt')\n",
"\n",
"Clas = torch.load('EGFRv2Class.pt')\n",
"\n",
"AutoencoderE.eval()\n",
"AutoencoderM.eval()\n",
"AutoencoderC.eval()\n",
"Clas.eval()\n",
"\n",
"ZEX = AutoencoderE(torch.FloatTensor(X_trainE))\n",
"ZMX = AutoencoderM(torch.FloatTensor(X_trainM))\n",
"ZCX = AutoencoderC(torch.FloatTensor(X_trainC))\n",
"ZTX = torch.cat((ZEX, ZMX, ZCX), 1)\n",
"ZTX = F.normalize(ZTX, p=2, dim=0)\n",
"PredX = Clas(ZTX)\n",
"AUCt = roc_auc_score(Y, PredX.detach().numpy())\n",
"print(AUCt)\n",
"\n",
"ZETerlo = AutoencoderE(TX_testEerlo)\n",
"ZMTerlo = AutoencoderM(TX_testMerlo)\n",
"ZCTerlo = AutoencoderC(TX_testCerlo)\n",
"ZTTerlo = torch.cat((ZETerlo, ZMTerlo, ZCTerlo), 1)\n",
"ZTTerlo = F.normalize(ZTTerlo, p=2, dim=0)\n",
"PredTerlo = Clas(ZTTerlo)\n",
"AUCterlo = roc_auc_score(Ytserlo, PredTerlo.detach().numpy())\n",
"print(AUCterlo)\n",
"\n",
"ZETcet = AutoencoderE(TX_testEcet)\n",
"ZMTcet = AutoencoderM(TX_testMcet)\n",
"ZCTcet = AutoencoderC(TX_testCcet)\n",
"ZTTcet = torch.cat((ZETcet, ZMTcet, ZCTcet), 1)\n",
"ZTTcet = F.normalize(ZTTcet, p=2, dim=0)\n",
"PredTcet = Clas(ZTTcet)\n",
"AUCtcet = roc_auc_score(Ytscet, PredTcet.detach().numpy())\n",
"print(AUCtcet)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:28: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:29: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:30: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(492, 13081)\n",
"(492, 13081)\n",
"(492, 13081)\n"
]
}
],
"source": [
"PRADE = pd.read_csv(\"TCGA-PRAD_exprs.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"PRADE = pd.DataFrame.transpose(PRADE)\n",
"\n",
"PRADM = pd.read_csv(\"TCGA-PRAD_mutations.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"PRADM = pd.DataFrame.transpose(PRADM)\n",
"PRADM = PRADM.loc[:,~PRADM.columns.duplicated()]\n",
"\n",
"PRADC = pd.read_csv(\"TCGA-PRAD_CNA.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"PRADC = pd.DataFrame.transpose(PRADC)\n",
"PRADC = PRADC.loc[:,~PRADC.columns.duplicated()]\n",
"\n",
"PRADM = PRADM.fillna(0)\n",
"PRADM[PRADM != 0.0] = 1\n",
"PRADC = PRADC.fillna(0)\n",
"PRADC[PRADC != 0.0] = 1\n",
"\n",
"#PRADE.rename(lambda x : x[0:11], axis = \"index\", inplace=True) \n",
"#PRADM.rename(lambda x : x[0:11], axis = \"index\", inplace=True) \n",
"#PRADC.rename(lambda x : x[0:11], axis = \"index\", inplace=True) \n",
"\n",
"lsPRAD = PRADE.index.intersection(PRADM.index)\n",
"lsPRAD = lsPRAD.intersection(PRADC.index)\n",
"lsPRAD = pd.unique(lsPRAD)\n",
"\n",
"PRADE = PRADE.loc[lsPRAD,ls]\n",
"PRADM = PRADM.loc[lsPRAD,ls]\n",
"PRADC = PRADC.loc[lsPRAD,ls]\n",
"\n",
"print(PRADE.shape)\n",
"print(PRADM.shape)\n",
"print(PRADC.shape)\n",
"\n",
"AutoencoderE.eval()\n",
"AutoencoderM.eval()\n",
"AutoencoderC.eval()\n",
"Clas.eval()\n",
"\n",
"PRADE2 = np.nan_to_num(PRADE.values)\n",
"PRADM2 = np.nan_to_num(PRADM.values)\n",
"PRADC2 = np.nan_to_num(PRADC.values)\n",
"\n",
"NPRADE2 = scalerGDSC.transform(PRADE2) \n",
"\n",
"PRADexprs = torch.FloatTensor(NPRADE2)\n",
"PRADmut = torch.FloatTensor(PRADM2)\n",
"PRADcna = torch.FloatTensor(PRADC2)\n",
"\n",
"PRADZE = AutoencoderE(PRADexprs)\n",
"PRADZM = AutoencoderM(PRADmut)\n",
"PRADZC = AutoencoderC(PRADcna)\n",
"\n",
"PRADZT = torch.cat((PRADZE, PRADZM, PRADZC), 1)\n",
"PRADZTX = F.normalize(PRADZT, p=2, dim=0)\n",
"PredPRAD = Clas(PRADZTX)\n",
"\n",
"#print(PredPRAD.detach().numpy())"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:28: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:29: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:30: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(161, 13081)\n",
"(161, 13081)\n",
"(161, 13081)\n"
]
}
],
"source": [
"KIRPE = pd.read_csv(\"TCGA-KIRP_exprs.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"KIRPE = pd.DataFrame.transpose(KIRPE)\n",
"\n",
"KIRPM = pd.read_csv(\"TCGA-KIRP_mutations.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"KIRPM = pd.DataFrame.transpose(KIRPM)\n",
"KIRPM = KIRPM.loc[:,~KIRPM.columns.duplicated()]\n",
"\n",
"KIRPC = pd.read_csv(\"TCGA-KIRP_CNA.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"KIRPC = pd.DataFrame.transpose(KIRPC)\n",
"KIRPC = KIRPC.loc[:,~KIRPC.columns.duplicated()]\n",
"\n",
"KIRPM = KIRPM.fillna(0)\n",
"KIRPM[KIRPM != 0.0] = 1\n",
"KIRPC = KIRPC.fillna(0)\n",
"KIRPC[KIRPC != 0.0] = 1\n",
"\n",
"#KIRPE.rename(lambda x : x[0:11], axis = \"index\", inplace=True) \n",
"#KIRPM.rename(lambda x : x[0:11], axis = \"index\", inplace=True) \n",
"#KIRPC.rename(lambda x : x[0:11], axis = \"index\", inplace=True) \n",
"\n",
"lsKIRP = KIRPE.index.intersection(KIRPM.index)\n",
"lsKIRP = lsKIRP.intersection(KIRPC.index)\n",
"lsKIRP = pd.unique(lsKIRP)\n",
"\n",
"KIRPE = KIRPE.loc[lsKIRP,ls]\n",
"KIRPM = KIRPM.loc[lsKIRP,ls]\n",
"KIRPC = KIRPC.loc[lsKIRP,ls]\n",
"\n",
"print(KIRPE.shape)\n",
"print(KIRPM.shape)\n",
"print(KIRPC.shape)\n",
"\n",
"AutoencoderE.eval()\n",
"AutoencoderM.eval()\n",
"AutoencoderC.eval()\n",
"Clas.eval()\n",
"\n",
"KIRPE2 = np.nan_to_num(KIRPE.values)\n",
"KIRPM2 = np.nan_to_num(KIRPM.values)\n",
"KIRPC2 = np.nan_to_num(KIRPC.values)\n",
"\n",
"NKIRPE2 = scalerGDSC.transform(KIRPE2) \n",
"\n",
"KIRPexprs = torch.FloatTensor(NKIRPE2)\n",
"KIRPmut = torch.FloatTensor(KIRPM2)\n",
"KIRPcna = torch.FloatTensor(KIRPC2)\n",
"\n",
"KIRPZE = AutoencoderE(KIRPexprs)\n",
"KIRPZM = AutoencoderM(KIRPmut)\n",
"KIRPZC = AutoencoderC(KIRPcna)\n",
"\n",
"KIRPZT = torch.cat((KIRPZE, KIRPZM, KIRPZC), 1)\n",
"KIRPZTX = F.normalize(KIRPZT, p=2, dim=0)\n",
"PredKIRP = Clas(KIRPZTX)\n",
"\n",
"#print(PredKIRP.detach().numpy())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:28: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:29: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:30: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(123, 13081)\n",
"(123, 13081)\n",
"(123, 13081)\n"
]
}
],
"source": [
"BLCAE = pd.read_csv(\"TCGA-BLCA_exprs.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"BLCAE = pd.DataFrame.transpose(BLCAE)\n",
"\n",
"BLCAM = pd.read_csv(\"TCGA-BLCA_mutations.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"BLCAM = pd.DataFrame.transpose(BLCAM)\n",
"BLCAM = BLCAM.loc[:,~BLCAM.columns.duplicated()]\n",
"\n",
"BLCAC = pd.read_csv(\"TCGA-BLCA_CNA.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"BLCAC = pd.DataFrame.transpose(BLCAC)\n",
"BLCAC = BLCAC.loc[:,~BLCAC.columns.duplicated()]\n",
"\n",
"BLCAM = BLCAM.fillna(0)\n",
"BLCAM[BLCAM != 0.0] = 1\n",
"BLCAC = BLCAC.fillna(0)\n",
"BLCAC[BLCAC != 0.0] = 1\n",
"\n",
"#BLCAE.rename(lambda x : x[0:11], axis = \"index\", inplace=True) \n",
"#BLCAM.rename(lambda x : x[0:11], axis = \"index\", inplace=True) \n",
"#BLCAC.rename(lambda x : x[0:11], axis = \"index\", inplace=True) \n",
"\n",
"lsBLCA = BLCAE.index.intersection(BLCAM.index)\n",
"lsBLCA = lsBLCA.intersection(BLCAC.index)\n",
"lsBLCA = pd.unique(lsBLCA)\n",
"\n",
"BLCAE = BLCAE.loc[lsBLCA,ls]\n",
"BLCAM = BLCAM.loc[lsBLCA,ls]\n",
"BLCAC = BLCAC.loc[lsBLCA,ls]\n",
"\n",
"print(BLCAE.shape)\n",
"print(BLCAM.shape)\n",
"print(BLCAC.shape)\n",
"\n",
"AutoencoderE.eval()\n",
"AutoencoderM.eval()\n",
"AutoencoderC.eval()\n",
"Clas.eval()\n",
"\n",
"BLCAE2 = np.nan_to_num(BLCAE.values)\n",
"BLCAM2 = np.nan_to_num(BLCAM.values)\n",
"BLCAC2 = np.nan_to_num(BLCAC.values)\n",
"\n",
"NBLCAE2 = scalerGDSC.transform(BLCAE2) \n",
"\n",
"BLCAexprs = torch.FloatTensor(NBLCAE2)\n",
"BLCAmut = torch.FloatTensor(BLCAM2)\n",
"BLCAcna = torch.FloatTensor(BLCAC2)\n",
"\n",
"BLCAZE = AutoencoderE(BLCAexprs)\n",
"BLCAZM = AutoencoderM(BLCAmut)\n",
"BLCAZC = AutoencoderC(BLCAcna)\n",
"\n",
"BLCAZT = torch.cat((BLCAZE, BLCAZM, BLCAZC), 1)\n",
"BLCAZTX = F.normalize(BLCAZT, p=2, dim=0)\n",
"PredBLCA = Clas(BLCAZTX)\n",
"\n",
"#print(PredBLCA.detach().numpy())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:28: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:29: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:30: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(921, 13081)\n",
"(921, 13081)\n",
"(921, 13081)\n"
]
}
],
"source": [
"BRCAE = pd.read_csv(\"TCGA-BRCA_exprs.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"BRCAE = pd.DataFrame.transpose(BRCAE)\n",
"\n",
"BRCAM = pd.read_csv(\"TCGA-BRCA_mutations.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"BRCAM = pd.DataFrame.transpose(BRCAM)\n",
"BRCAM = BRCAM.loc[:,~BRCAM.columns.duplicated()]\n",
"\n",
"BRCAC = pd.read_csv(\"TCGA-BRCA_CNA.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"BRCAC = pd.DataFrame.transpose(BRCAC)\n",
"BRCAC = BRCAC.loc[:,~BRCAC.columns.duplicated()]\n",
"\n",
"BRCAM = BRCAM.fillna(0)\n",
"BRCAM[BRCAM != 0.0] = 1\n",
"BRCAC = BRCAC.fillna(0)\n",
"BRCAC[BRCAC != 0.0] = 1\n",
"\n",
"#BRCAE.rename(lambda x : x[0:11], axis = \"index\", inplace=True) \n",
"#BRCAM.rename(lambda x : x[0:11], axis = \"index\", inplace=True) \n",
"#BRCAC.rename(lambda x : x[0:11], axis = \"index\", inplace=True) \n",
"\n",
"lsBRCA = BRCAE.index.intersection(BRCAM.index)\n",
"lsBRCA = lsBRCA.intersection(BRCAC.index)\n",
"lsBRCA = pd.unique(lsBRCA)\n",
"\n",
"BRCAE = BRCAE.loc[lsBRCA,ls]\n",
"BRCAM = BRCAM.loc[lsBRCA,ls]\n",
"BRCAC = BRCAC.loc[lsBRCA,ls]\n",
"\n",
"print(BRCAE.shape)\n",
"print(BRCAM.shape)\n",
"print(BRCAC.shape)\n",
"\n",
"AutoencoderE.eval()\n",
"AutoencoderM.eval()\n",
"AutoencoderC.eval()\n",
"Clas.eval()\n",
"\n",
"BRCAE2 = np.nan_to_num(BRCAE.values)\n",
"BRCAM2 = np.nan_to_num(BRCAM.values)\n",
"BRCAC2 = np.nan_to_num(BRCAC.values)\n",
"\n",
"NBRCAE2 = scalerGDSC.transform(BRCAE2) \n",
"\n",
"BRCAexprs = torch.FloatTensor(NBRCAE2)\n",
"BRCAmut = torch.FloatTensor(BRCAM2)\n",
"BRCAcna = torch.FloatTensor(BRCAC2)\n",
"\n",
"BRCAZE = AutoencoderE(BRCAexprs)\n",
"BRCAZM = AutoencoderM(BRCAmut)\n",
"BRCAZC = AutoencoderC(BRCAcna)\n",
"\n",
"BRCAZT = torch.cat((BRCAZE, BRCAZM, BRCAZC), 1)\n",
"BRCAZTX = F.normalize(BRCAZT, p=2, dim=0)\n",
"PredBRCA = Clas(BRCAZTX)\n",
"\n",
"#print(PredBRCA.detach().numpy())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(130, 13081)\n",
"(130, 13081)\n",
"(130, 13081)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:28: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:29: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:30: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n"
]
}
],
"source": [
"PAADE = pd.read_csv(\"TCGA-PAAD_exprs.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"PAADE = pd.DataFrame.transpose(PAADE)\n",
"\n",
"PAADM = pd.read_csv(\"TCGA-PAAD_mutations.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"PAADM = pd.DataFrame.transpose(PAADM)\n",
"PAADM = PAADM.loc[:,~PAADM.columns.duplicated()]\n",
"\n",
"PAADC = pd.read_csv(\"TCGA-PAAD_CNA.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"PAADC = pd.DataFrame.transpose(PAADC)\n",
"PAADC = PAADC.loc[:,~PAADC.columns.duplicated()]\n",
"\n",
"PAADM = PAADM.fillna(0)\n",
"PAADM[PAADM != 0.0] = 1\n",
"PAADC = PAADC.fillna(0)\n",
"PAADC[PAADC != 0.0] = 1\n",
"\n",
"#PAADE.rename(lambda x : x[0:11], axis = \"index\", inplace=True) \n",
"#PAADM.rename(lambda x : x[0:11], axis = \"index\", inplace=True) \n",
"#PAADC.rename(lambda x : x[0:11], axis = \"index\", inplace=True) \n",
"\n",
"lsPAAD = PAADE.index.intersection(PAADM.index)\n",
"lsPAAD = lsPAAD.intersection(PAADC.index)\n",
"lsPAAD = pd.unique(lsPAAD)\n",
"\n",
"PAADE = PAADE.loc[lsPAAD,ls]\n",
"PAADM = PAADM.loc[lsPAAD,ls]\n",
"PAADC = PAADC.loc[lsPAAD,ls]\n",
"\n",
"print(PAADE.shape)\n",
"print(PAADM.shape)\n",
"print(PAADC.shape)\n",
"\n",
"AutoencoderE.eval()\n",
"AutoencoderM.eval()\n",
"AutoencoderC.eval()\n",
"Clas.eval()\n",
"\n",
"PAADE2 = np.nan_to_num(PAADE.values)\n",
"PAADM2 = np.nan_to_num(PAADM.values)\n",
"PAADC2 = np.nan_to_num(PAADC.values)\n",
"\n",
"NPAADE2 = scalerGDSC.transform(PAADE2) \n",
"\n",
"PAADexprs = torch.FloatTensor(NPAADE2)\n",
"PAADmut = torch.FloatTensor(PAADM2)\n",
"PAADcna = torch.FloatTensor(PAADC2)\n",
"\n",
"PAADZE = AutoencoderE(PAADexprs)\n",
"PAADZM = AutoencoderM(PAADmut)\n",
"PAADZC = AutoencoderC(PAADcna)\n",
"\n",
"PAADZT = torch.cat((PAADZE, PAADZM, PAADZC), 1)\n",
"PAADZTX = F.normalize(PAADZT, p=2, dim=0)\n",
"PredPAAD = Clas(PAADZTX)\n",
"\n",
"#print(PredPAAD.detach().numpy())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:28: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:29: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
"/home/hnoghabi/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:30: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(475, 13081)\n",
"(475, 13081)\n",
"(475, 13081)\n"
]
}
],
"source": [
"LUADE = pd.read_csv(\"TCGA-LUAD_exprs.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"LUADE = pd.DataFrame.transpose(LUADE)\n",
"\n",
"LUADM = pd.read_csv(\"TCGA-LUAD_mutations.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"LUADM = pd.DataFrame.transpose(LUADM)\n",
"LUADM = LUADM.loc[:,~LUADM.columns.duplicated()]\n",
"\n",
"LUADC = pd.read_csv(\"TCGA-LUAD_CNA.tsv\", \n",
" sep = \"\\t\", index_col=0, decimal = \".\")\n",
"LUADC = pd.DataFrame.transpose(LUADC)\n",
"LUADC = LUADC.loc[:,~LUADC.columns.duplicated()]\n",
"\n",
"LUADM = LUADM.fillna(0)\n",
"LUADM[LUADM != 0.0] = 1\n",
"LUADC = LUADC.fillna(0)\n",
"LUADC[LUADC != 0.0] = 1\n",
"\n",
"#LUADE.rename(lambda x : x[0:11], axis = \"index\", inplace=True) \n",
"#LUADM.rename(lambda x : x[0:11], axis = \"index\", inplace=True) \n",
"#LUADC.rename(lambda x : x[0:11], axis = \"index\", inplace=True) \n",
"\n",
"lsLUAD = LUADE.index.intersection(LUADM.index)\n",
"lsLUAD = lsLUAD.intersection(LUADC.index)\n",
"lsLUAD = pd.unique(lsLUAD)\n",
"\n",
"LUADE = LUADE.loc[lsLUAD,ls]\n",
"LUADM = LUADM.loc[lsLUAD,ls]\n",
"LUADC = LUADC.loc[lsLUAD,ls]\n",
"\n",
"print(LUADE.shape)\n",
"print(LUADM.shape)\n",
"print(LUADC.shape)\n",
"\n",
"AutoencoderE.eval()\n",
"AutoencoderM.eval()\n",
"AutoencoderC.eval()\n",
"Clas.eval()\n",
"\n",
"LUADE2 = np.nan_to_num(LUADE.values)\n",
"LUADM2 = np.nan_to_num(LUADM.values)\n",
"LUADC2 = np.nan_to_num(LUADC.values)\n",
"\n",
"NLUADE2 = scalerGDSC.transform(LUADE2) \n",
"\n",
"LUADexprs = torch.FloatTensor(NLUADE2)\n",
"LUADmut = torch.FloatTensor(LUADM2)\n",
"LUADcna = torch.FloatTensor(LUADC2)\n",
"\n",
"LUADZE = AutoencoderE(LUADexprs)\n",
"LUADZM = AutoencoderM(LUADmut)\n",
"LUADZC = AutoencoderC(LUADcna)\n",
"\n",
"LUADZT = torch.cat((LUADZE, LUADZM, LUADZC), 1)\n",
"LUADZTX = F.normalize(LUADZT, p=2, dim=0)\n",
"PredLUAD = Clas(LUADZTX)\n",
"\n",
"#print(PredLUAD.detach().numpy())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from scipy.stats.stats import pearsonr\n",
"from scipy.stats import spearmanr\n",
"import statsmodels.api as sm\n",
"from mne.stats import bonferroni_correction\n",
"\n",
"lsEGFR = [10000, 102, 10252, 10253,10254,1026,1027,107,108,109,111,11140,112,113,114,1147,115,117145,1173,1175,1211,1213,1385,1445,156,160,161,163,1950,1956,196883,2060,207,208,2308,2309,23239,2475,253260,2549,26018,2885,2931,29924,30011,3164,3265,3320,3709,3710,3845,4193,4303,4893,5136,5153,5170,5290,5295,5335,5566,5567,5568,5573,5575,5576,5577,5578,5580,5581,5582,55824,5594,5595,5604,5605,572,5728,57761,58513,5894,6199,6233,64223,6456,6464,6654,6714,6868,7249,728590,729120,730418,7311,731292,7529,79109,801,8027,8038,805,808,814,842,84335,867,9146,983,998]\n",
"\n",
"#lsEGFR = [10000,1026,1027,10298,10718,1398,1399,145957,1839,1950,1956,1978,2002,2064,2065,2066,2069,207,208,23533,23642,2475,25,2549,25759,27,2885,2932,3084,3265,369,3725,374,3845,399694,4609,4690,4893,5058,5062,5063,5290,5291,5293,5294,5295,5296,5335,53358,5336,5578,5579,5582,5594,5595,5599,5601,5602,5604,5605,5609,56924,57144,572,5747,5894,6198,6199,6416,6464,6654,6655,6714,673,6776,6777,685,7039,815,816,817,818,8440,8503,867,868,9542]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"listEGFR = PRADE.columns.intersection(lsEGFR)\n",
"PRADEEGFR = PRADE[listEGFR]\n",
"PRADMEGFR = PRADM[listEGFR]\n",
"PRADCEGFR = PRADC[listEGFR]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>y</td> <th> R-squared: </th> <td> 0.999</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.999</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 3331.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 12 Jan 2019</td> <th> Prob (F-statistic):</th> <td> 0.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>18:16:35</td> <th> Log-Likelihood: </th> <td> 1340.8</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 492</td> <th> AIC: </th> <td> -2480.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 391</td> <th> BIC: </th> <td> -2056.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 101</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>102</th> <td> 0.0038</td> <td> 0.003</td> <td> 1.171</td> <td> 0.242</td> <td> -0.003</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>107</th> <td> -0.0015</td> <td> 0.001</td> <td> -0.982</td> <td> 0.327</td> <td> -0.004</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>108</th> <td> -0.0006</td> <td> 0.002</td> <td> -0.377</td> <td> 0.706</td> <td> -0.004</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>109</th> <td> -0.0021</td> <td> 0.004</td> <td> -0.585</td> <td> 0.559</td> <td> -0.009</td> <td> 0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>111</th> <td> -0.0038</td> <td> 0.003</td> <td> -1.432</td> <td> 0.153</td> <td> -0.009</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>112</th> <td> -0.0092</td> <td> 0.004</td> <td> -2.553</td> <td> 0.011</td> <td> -0.016</td> <td> -0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>113</th> <td> 0.0046</td> <td> 0.003</td> <td> 1.325</td> <td> 0.186</td> <td> -0.002</td> <td> 0.011</td>\n",
"</tr>\n",
"<tr>\n",
" <th>114</th> <td> 0.0184</td> <td> 0.021</td> <td> 0.868</td> <td> 0.386</td> <td> -0.023</td> <td> 0.060</td>\n",
"</tr>\n",
"<tr>\n",
" <th>115</th> <td> -0.0002</td> <td> 0.004</td> <td> -0.047</td> <td> 0.963</td> <td> -0.007</td> <td> 0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>160</th> <td> 0.0020</td> <td> 0.004</td> <td> 0.564</td> <td> 0.573</td> <td> -0.005</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>161</th> <td> 0.0081</td> <td> 0.006</td> <td> 1.424</td> <td> 0.155</td> <td> -0.003</td> <td> 0.019</td>\n",
"</tr>\n",
"<tr>\n",
" <th>163</th> <td> 0.0006</td> <td> 0.004</td> <td> 0.142</td> <td> 0.887</td> <td> -0.008</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>207</th> <td> 0.0157</td> <td> 0.004</td> <td> 3.698</td> <td> 0.000</td> <td> 0.007</td> <td> 0.024</td>\n",
"</tr>\n",
"<tr>\n",
" <th>208</th> <td> 0.0044</td> <td> 0.006</td> <td> 0.792</td> <td> 0.429</td> <td> -0.007</td> <td> 0.015</td>\n",
"</tr>\n",
"<tr>\n",
" <th>572</th> <td> 0.0089</td> <td> 0.004</td> <td> 1.975</td> <td> 0.049</td> <td> 3.82e-05</td> <td> 0.018</td>\n",
"</tr>\n",
"<tr>\n",
" <th>801</th> <td> -0.0057</td> <td> 0.003</td> <td> -1.772</td> <td> 0.077</td> <td> -0.012</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>805</th> <td> 0.0053</td> <td> 0.004</td> <td> 1.280</td> <td> 0.201</td> <td> -0.003</td> <td> 0.013</td>\n",
"</tr>\n",
"<tr>\n",
" <th>808</th> <td> 0.0055</td> <td> 0.005</td> <td> 1.141</td> <td> 0.255</td> <td> -0.004</td> <td> 0.015</td>\n",
"</tr>\n",
"<tr>\n",
" <th>814</th> <td> -0.0012</td> <td> 0.003</td> <td> -0.477</td> <td> 0.633</td> <td> -0.006</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>842</th> <td> -0.0072</td> <td> 0.004</td> <td> -1.804</td> <td> 0.072</td> <td> -0.015</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>867</th> <td> 0.0034</td> <td> 0.005</td> <td> 0.666</td> <td> 0.506</td> <td> -0.007</td> <td> 0.014</td>\n",
"</tr>\n",
"<tr>\n",
" <th>983</th> <td> 0.0062</td> <td> 0.002</td> <td> 3.919</td> <td> 0.000</td> <td> 0.003</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>998</th> <td> -0.0097</td> <td> 0.005</td> <td> -1.854</td> <td> 0.064</td> <td> -0.020</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1026</th> <td> 0.0025</td> <td> 0.001</td> <td> 1.717</td> <td> 0.087</td> <td> -0.000</td> <td> 0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1027</th> <td> -0.0033</td> <td> 0.003</td> <td> -1.224</td> <td> 0.222</td> <td> -0.009</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1147</th> <td> -0.0025</td> <td> 0.004</td> <td> -0.635</td> <td> 0.526</td> <td> -0.010</td> <td> 0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1173</th> <td> 0.0166</td> <td> 0.005</td> <td> 3.472</td> <td> 0.001</td> <td> 0.007</td> <td> 0.026</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1175</th> <td> -0.0029</td> <td> 0.003</td> <td> -0.927</td> <td> 0.355</td> <td> -0.009</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1211</th> <td> 0.0137</td> <td> 0.004</td> <td> 3.271</td> <td> 0.001</td> <td> 0.005</td> <td> 0.022</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1213</th> <td> -0.0176</td> <td> 0.005</td> <td> -3.238</td> <td> 0.001</td> <td> -0.028</td> <td> -0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1385</th> <td> -0.0272</td> <td> 0.006</td> <td> -4.807</td> <td> 0.000</td> <td> -0.038</td> <td> -0.016</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1445</th> <td> -0.0190</td> <td> 0.005</td> <td> -3.692</td> <td> 0.000</td> <td> -0.029</td> <td> -0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1950</th> <td> 0.0015</td> <td> 0.001</td> <td> 1.262</td> <td> 0.208</td> <td> -0.001</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1956</th> <td> 0.0091</td> <td> 0.003</td> <td> 3.223</td> <td> 0.001</td> <td> 0.004</td> <td> 0.015</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2060</th> <td> -0.0084</td> <td> 0.005</td> <td> -1.800</td> <td> 0.073</td> <td> -0.018</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2308</th> <td> -0.0026</td> <td> 0.004</td> <td> -0.730</td> <td> 0.466</td> <td> -0.010</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2309</th> <td> 0.0015</td> <td> 0.002</td> <td> 0.613</td> <td> 0.540</td> <td> -0.003</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2475</th> <td> 0.0009</td> <td> 0.003</td> <td> 0.326</td> <td> 0.744</td> <td> -0.005</td> <td> 0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2549</th> <td> 0.0126</td> <td> 0.004</td> <td> 2.997</td> <td> 0.003</td> <td> 0.004</td> <td> 0.021</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2885</th> <td> 0.0143</td> <td> 0.006</td> <td> 2.293</td> <td> 0.022</td> <td> 0.002</td> <td> 0.027</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2931</th> <td> 0.0105</td> <td> 0.003</td> <td> 3.025</td> <td> 0.003</td> <td> 0.004</td> <td> 0.017</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3164</th> <td> 0.0011</td> <td> 0.001</td> <td> 0.984</td> <td> 0.325</td> <td> -0.001</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3265</th> <td> 0.0007</td> <td> 0.004</td> <td> 0.162</td> <td> 0.872</td> <td> -0.008</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3320</th> <td> 0.0039</td> <td> 0.003</td> <td> 1.407</td> <td> 0.160</td> <td> -0.002</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3709</th> <td> -0.0004</td> <td> 0.002</td> <td> -0.168</td> <td> 0.867</td> <td> -0.005</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3710</th> <td> 0.0032</td> <td> 0.002</td> <td> 1.278</td> <td> 0.202</td> <td> -0.002</td> <td> 0.008</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3845</th> <td> 0.0043</td> <td> 0.003</td> <td> 1.285</td> <td> 0.200</td> <td> -0.002</td> <td> 0.011</td>\n",
"</tr>\n",
"<tr>\n",
" <th>4193</th> <td> 0.0049</td> <td> 0.003</td> <td> 1.479</td> <td> 0.140</td> <td> -0.002</td> <td> 0.011</td>\n",
"</tr>\n",
"<tr>\n",
" <th>4303</th> <td> -0.0028</td> <td> 0.004</td> <td> -0.781</td> <td> 0.435</td> <td> -0.010</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>4893</th> <td> -0.0048</td> <td> 0.003</td> <td> -1.744</td> <td> 0.082</td> <td> -0.010</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5136</th> <td> 0.0006</td> <td> 0.002</td> <td> 0.346</td> <td> 0.729</td> <td> -0.003</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5170</th> <td> 0.0062</td> <td> 0.005</td> <td> 1.193</td> <td> 0.234</td> <td> -0.004</td> <td> 0.016</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5290</th> <td> 0.0012</td> <td> 0.004</td> <td> 0.276</td> <td> 0.782</td> <td> -0.007</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5295</th> <td> -0.0067</td> <td> 0.003</td> <td> -2.376</td> <td> 0.018</td> <td> -0.012</td> <td> -0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5335</th> <td> 0.0026</td> <td> 0.004</td> <td> 0.607</td> <td> 0.544</td> <td> -0.006</td> <td> 0.011</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5566</th> <td> -0.0074</td> <td> 0.005</td> <td> -1.360</td> <td> 0.175</td> <td> -0.018</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5567</th> <td> -0.0004</td> <td> 0.002</td> <td> -0.188</td> <td> 0.851</td> <td> -0.004</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5573</th> <td> -0.0023</td> <td> 0.005</td> <td> -0.451</td> <td> 0.652</td> <td> -0.012</td> <td> 0.008</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5575</th> <td> 0.0043</td> <td> 0.002</td> <td> 1.820</td> <td> 0.070</td> <td> -0.000</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5576</th> <td> -0.0015</td> <td> 0.004</td> <td> -0.396</td> <td> 0.692</td> <td> -0.009</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5577</th> <td> 0.0034</td> <td> 0.002</td> <td> 1.822</td> <td> 0.069</td> <td> -0.000</td> <td> 0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5578</th> <td> 0.0009</td> <td> 0.002</td> <td> 0.418</td> <td> 0.676</td> <td> -0.003</td> <td> 0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5580</th> <td> -0.0008</td> <td> 0.003</td> <td> -0.276</td> <td> 0.783</td> <td> -0.007</td> <td> 0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5581</th> <td> -0.0089</td> <td> 0.005</td> <td> -1.857</td> <td> 0.064</td> <td> -0.018</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5582</th> <td> 0.0085</td> <td> 0.017</td> <td> 0.491</td> <td> 0.624</td> <td> -0.026</td> <td> 0.043</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5594</th> <td> 0.0003</td> <td> 0.006</td> <td> 0.055</td> <td> 0.956</td> <td> -0.012</td> <td> 0.012</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5595</th> <td> 0.0037</td> <td> 0.004</td> <td> 0.935</td> <td> 0.350</td> <td> -0.004</td> <td> 0.011</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5604</th> <td> -0.0115</td> <td> 0.004</td> <td> -3.119</td> <td> 0.002</td> <td> -0.019</td> <td> -0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5605</th> <td> 0.0087</td> <td> 0.005</td> <td> 1.745</td> <td> 0.082</td> <td> -0.001</td> <td> 0.018</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5728</th> <td>-2.957e-05</td> <td> 0.002</td> <td> -0.016</td> <td> 0.987</td> <td> -0.004</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5894</th> <td> 0.0119</td> <td> 0.006</td> <td> 2.114</td> <td> 0.035</td> <td> 0.001</td> <td> 0.023</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6199</th> <td> -0.0020</td> <td> 0.004</td> <td> -0.486</td> <td> 0.627</td> <td> -0.010</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6456</th> <td> -0.0035</td> <td> 0.001</td> <td> -2.815</td> <td> 0.005</td> <td> -0.006</td> <td> -0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6464</th> <td> 0.0086</td> <td> 0.005</td> <td> 1.901</td> <td> 0.058</td> <td> -0.000</td> <td> 0.018</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6654</th> <td> -0.0153</td> <td> 0.006</td> <td> -2.782</td> <td> 0.006</td> <td> -0.026</td> <td> -0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6714</th> <td> 0.0003</td> <td> 0.003</td> <td> 0.118</td> <td> 0.906</td> <td> -0.005</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6868</th> <td> -0.0008</td> <td> 0.005</td> <td> -0.152</td> <td> 0.880</td> <td> -0.011</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>7249</th> <td> 0.0062</td> <td> 0.006</td> <td> 1.129</td> <td> 0.259</td> <td> -0.005</td> <td> 0.017</td>\n",
"</tr>\n",
"<tr>\n",
" <th>7311</th> <td> 0.0123</td> <td> 0.004</td> <td> 2.798</td> <td> 0.005</td> <td> 0.004</td> <td> 0.021</td>\n",
"</tr>\n",
"<tr>\n",
" <th>7529</th> <td> 0.0052</td> <td> 0.005</td> <td> 1.055</td> <td> 0.292</td> <td> -0.004</td> <td> 0.015</td>\n",
"</tr>\n",
"<tr>\n",
" <th>8027</th> <td> 0.0059</td> <td> 0.005</td> <td> 1.259</td> <td> 0.209</td> <td> -0.003</td> <td> 0.015</td>\n",
"</tr>\n",
"<tr>\n",
" <th>8038</th> <td> -0.0037</td> <td> 0.003</td> <td> -1.379</td> <td> 0.169</td> <td> -0.009</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>9146</th> <td> -0.0085</td> <td> 0.006</td> <td> -1.370</td> <td> 0.172</td> <td> -0.021</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10000</th> <td> -0.0011</td> <td> 0.003</td> <td> -0.442</td> <td> 0.659</td> <td> -0.006</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10252</th> <td> 0.0025</td> <td> 0.002</td> <td> 1.088</td> <td> 0.277</td> <td> -0.002</td> <td> 0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10253</th> <td> 0.0016</td> <td> 0.003</td> <td> 0.595</td> <td> 0.552</td> <td> -0.004</td> <td> 0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10254</th> <td> 0.0036</td> <td> 0.004</td> <td> 0.861</td> <td> 0.390</td> <td> -0.005</td> <td> 0.012</td>\n",
"</tr>\n",
"<tr>\n",
" <th>11140</th> <td> 0.0068</td> <td> 0.006</td> <td> 1.064</td> <td> 0.288</td> <td> -0.006</td> <td> 0.019</td>\n",
"</tr>\n",
"<tr>\n",
" <th>23239</th> <td> 0.0050</td> <td> 0.003</td> <td> 1.664</td> <td> 0.097</td> <td> -0.001</td> <td> 0.011</td>\n",
"</tr>\n",
"<tr>\n",
" <th>26018</th> <td> 0.0061</td> <td> 0.003</td> <td> 2.397</td> <td> 0.017</td> <td> 0.001</td> <td> 0.011</td>\n",
"</tr>\n",
"<tr>\n",
" <th>29924</th> <td> -0.0105</td> <td> 0.005</td> <td> -2.092</td> <td> 0.037</td> <td> -0.020</td> <td> -0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>30011</th> <td> -0.0037</td> <td> 0.003</td> <td> -1.088</td> <td> 0.277</td> <td> -0.010</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>55824</th> <td> 0.0007</td> <td> 0.003</td> <td> 0.277</td> <td> 0.782</td> <td> -0.004</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>57761</th> <td> -0.0018</td> <td> 0.002</td> <td> -0.844</td> <td> 0.399</td> <td> -0.006</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>58513</th> <td> -0.0083</td> <td> 0.007</td> <td> -1.252</td> <td> 0.211</td> <td> -0.021</td> <td> 0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>64223</th> <td> -0.0173</td> <td> 0.006</td> <td> -3.144</td> <td> 0.002</td> <td> -0.028</td> <td> -0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>79109</th> <td> 0.0100</td> <td> 0.006</td> <td> 1.691</td> <td> 0.092</td> <td> -0.002</td> <td> 0.022</td>\n",
"</tr>\n",
"<tr>\n",
" <th>84335</th> <td> -0.0074</td> <td> 0.005</td> <td> -1.453</td> <td> 0.147</td> <td> -0.018</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>117145</th> <td> 0.0006</td> <td> 0.003</td> <td> 0.199</td> <td> 0.843</td> <td> -0.006</td> <td> 0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>196883</th> <td> -0.0055</td> <td> 0.003</td> <td> -1.924</td> <td> 0.055</td> <td> -0.011</td> <td> 0.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>253260</th> <td> 0.0202</td> <td> 0.005</td> <td> 3.858</td> <td> 0.000</td> <td> 0.010</td> <td> 0.030</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>63.897</td> <th> Durbin-Watson: </th> <td> 1.907</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 218.126</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.560</td> <th> Prob(JB): </th> <td>4.31e-48</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 6.063</td> <th> Cond. No. </th> <td>1.34e+03</td>\n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.34e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.999\n",
"Model: OLS Adj. R-squared: 0.999\n",
"Method: Least Squares F-statistic: 3331.\n",
"Date: Sat, 12 Jan 2019 Prob (F-statistic): 0.00\n",
"Time: 18:16:35 Log-Likelihood: 1340.8\n",
"No. Observations: 492 AIC: -2480.\n",
"Df Residuals: 391 BIC: -2056.\n",
"Df Model: 101 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"102 0.0038 0.003 1.171 0.242 -0.003 0.010\n",
"107 -0.0015 0.001 -0.982 0.327 -0.004 0.001\n",
"108 -0.0006 0.002 -0.377 0.706 -0.004 0.003\n",
"109 -0.0021 0.004 -0.585 0.559 -0.009 0.005\n",
"111 -0.0038 0.003 -1.432 0.153 -0.009 0.001\n",
"112 -0.0092 0.004 -2.553 0.011 -0.016 -0.002\n",
"113 0.0046 0.003 1.325 0.186 -0.002 0.011\n",
"114 0.0184 0.021 0.868 0.386 -0.023 0.060\n",
"115 -0.0002 0.004 -0.047 0.963 -0.007 0.007\n",
"160 0.0020 0.004 0.564 0.573 -0.005 0.009\n",
"161 0.0081 0.006 1.424 0.155 -0.003 0.019\n",
"163 0.0006 0.004 0.142 0.887 -0.008 0.009\n",
"207 0.0157 0.004 3.698 0.000 0.007 0.024\n",
"208 0.0044 0.006 0.792 0.429 -0.007 0.015\n",
"572 0.0089 0.004 1.975 0.049 3.82e-05 0.018\n",
"801 -0.0057 0.003 -1.772 0.077 -0.012 0.001\n",
"805 0.0053 0.004 1.280 0.201 -0.003 0.013\n",
"808 0.0055 0.005 1.141 0.255 -0.004 0.015\n",
"814 -0.0012 0.003 -0.477 0.633 -0.006 0.004\n",
"842 -0.0072 0.004 -1.804 0.072 -0.015 0.001\n",
"867 0.0034 0.005 0.666 0.506 -0.007 0.014\n",
"983 0.0062 0.002 3.919 0.000 0.003 0.009\n",
"998 -0.0097 0.005 -1.854 0.064 -0.020 0.001\n",
"1026 0.0025 0.001 1.717 0.087 -0.000 0.005\n",
"1027 -0.0033 0.003 -1.224 0.222 -0.009 0.002\n",
"1147 -0.0025 0.004 -0.635 0.526 -0.010 0.005\n",
"1173 0.0166 0.005 3.472 0.001 0.007 0.026\n",
"1175 -0.0029 0.003 -0.927 0.355 -0.009 0.003\n",
"1211 0.0137 0.004 3.271 0.001 0.005 0.022\n",
"1213 -0.0176 0.005 -3.238 0.001 -0.028 -0.007\n",
"1385 -0.0272 0.006 -4.807 0.000 -0.038 -0.016\n",
"1445 -0.0190 0.005 -3.692 0.000 -0.029 -0.009\n",
"1950 0.0015 0.001 1.262 0.208 -0.001 0.004\n",
"1956 0.0091 0.003 3.223 0.001 0.004 0.015\n",
"2060 -0.0084 0.005 -1.800 0.073 -0.018 0.001\n",
"2308 -0.0026 0.004 -0.730 0.466 -0.010 0.004\n",
"2309 0.0015 0.002 0.613 0.540 -0.003 0.006\n",
"2475 0.0009 0.003 0.326 0.744 -0.005 0.007\n",
"2549 0.0126 0.004 2.997 0.003 0.004 0.021\n",
"2885 0.0143 0.006 2.293 0.022 0.002 0.027\n",
"2931 0.0105 0.003 3.025 0.003 0.004 0.017\n",
"3164 0.0011 0.001 0.984 0.325 -0.001 0.003\n",
"3265 0.0007 0.004 0.162 0.872 -0.008 0.009\n",
"3320 0.0039 0.003 1.407 0.160 -0.002 0.009\n",
"3709 -0.0004 0.002 -0.168 0.867 -0.005 0.004\n",
"3710 0.0032 0.002 1.278 0.202 -0.002 0.008\n",
"3845 0.0043 0.003 1.285 0.200 -0.002 0.011\n",
"4193 0.0049 0.003 1.479 0.140 -0.002 0.011\n",
"4303 -0.0028 0.004 -0.781 0.435 -0.010 0.004\n",
"4893 -0.0048 0.003 -1.744 0.082 -0.010 0.001\n",
"5136 0.0006 0.002 0.346 0.729 -0.003 0.004\n",
"5170 0.0062 0.005 1.193 0.234 -0.004 0.016\n",
"5290 0.0012 0.004 0.276 0.782 -0.007 0.010\n",
"5295 -0.0067 0.003 -2.376 0.018 -0.012 -0.001\n",
"5335 0.0026 0.004 0.607 0.544 -0.006 0.011\n",
"5566 -0.0074 0.005 -1.360 0.175 -0.018 0.003\n",
"5567 -0.0004 0.002 -0.188 0.851 -0.004 0.003\n",
"5573 -0.0023 0.005 -0.451 0.652 -0.012 0.008\n",
"5575 0.0043 0.002 1.820 0.070 -0.000 0.009\n",
"5576 -0.0015 0.004 -0.396 0.692 -0.009 0.006\n",
"5577 0.0034 0.002 1.822 0.069 -0.000 0.007\n",
"5578 0.0009 0.002 0.418 0.676 -0.003 0.005\n",
"5580 -0.0008 0.003 -0.276 0.783 -0.007 0.005\n",
"5581 -0.0089 0.005 -1.857 0.064 -0.018 0.001\n",
"5582 0.0085 0.017 0.491 0.624 -0.026 0.043\n",
"5594 0.0003 0.006 0.055 0.956 -0.012 0.012\n",
"5595 0.0037 0.004 0.935 0.350 -0.004 0.011\n",
"5604 -0.0115 0.004 -3.119 0.002 -0.019 -0.004\n",
"5605 0.0087 0.005 1.745 0.082 -0.001 0.018\n",
"5728 -2.957e-05 0.002 -0.016 0.987 -0.004 0.004\n",
"5894 0.0119 0.006 2.114 0.035 0.001 0.023\n",
"6199 -0.0020 0.004 -0.486 0.627 -0.010 0.006\n",
"6456 -0.0035 0.001 -2.815 0.005 -0.006 -0.001\n",
"6464 0.0086 0.005 1.901 0.058 -0.000 0.018\n",
"6654 -0.0153 0.006 -2.782 0.006 -0.026 -0.004\n",
"6714 0.0003 0.003 0.118 0.906 -0.005 0.006\n",
"6868 -0.0008 0.005 -0.152 0.880 -0.011 0.009\n",
"7249 0.0062 0.006 1.129 0.259 -0.005 0.017\n",
"7311 0.0123 0.004 2.798 0.005 0.004 0.021\n",
"7529 0.0052 0.005 1.055 0.292 -0.004 0.015\n",
"8027 0.0059 0.005 1.259 0.209 -0.003 0.015\n",
"8038 -0.0037 0.003 -1.379 0.169 -0.009 0.002\n",
"9146 -0.0085 0.006 -1.370 0.172 -0.021 0.004\n",
"10000 -0.0011 0.003 -0.442 0.659 -0.006 0.004\n",
"10252 0.0025 0.002 1.088 0.277 -0.002 0.007\n",
"10253 0.0016 0.003 0.595 0.552 -0.004 0.007\n",
"10254 0.0036 0.004 0.861 0.390 -0.005 0.012\n",
"11140 0.0068 0.006 1.064 0.288 -0.006 0.019\n",
"23239 0.0050 0.003 1.664 0.097 -0.001 0.011\n",
"26018 0.0061 0.003 2.397 0.017 0.001 0.011\n",
"29924 -0.0105 0.005 -2.092 0.037 -0.020 -0.001\n",
"30011 -0.0037 0.003 -1.088 0.277 -0.010 0.003\n",
"55824 0.0007 0.003 0.277 0.782 -0.004 0.006\n",
"57761 -0.0018 0.002 -0.844 0.399 -0.006 0.002\n",
"58513 -0.0083 0.007 -1.252 0.211 -0.021 0.005\n",
"64223 -0.0173 0.006 -3.144 0.002 -0.028 -0.006\n",
"79109 0.0100 0.006 1.691 0.092 -0.002 0.022\n",
"84335 -0.0074 0.005 -1.453 0.147 -0.018 0.003\n",
"117145 0.0006 0.003 0.199 0.843 -0.006 0.007\n",
"196883 -0.0055 0.003 -1.924 0.055 -0.011 0.000\n",
"253260 0.0202 0.005 3.858 0.000 0.010 0.030\n",
"==============================================================================\n",
"Omnibus: 63.897 Durbin-Watson: 1.907\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 218.126\n",
"Skew: 0.560 Prob(JB): 4.31e-48\n",
"Kurtosis: 6.063 Cond. No. 1.34e+03\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 1.34e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = PRADEEGFR\n",
"y = PredPRAD.detach().numpy()\n",
"\n",
"# Note the difference in argument order\n",
"model = sm.OLS(y, X).fit()\n",
"predictions = model.predict(X) # make the predictions by the model\n",
"\n",
"# Print out the statistics\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(array([False, False, False, False, False, False, False, False, False,\n",
" False, False, False, True, False, False, False, False, False,\n",
" False, False, False, True, False, False, False, False, False,\n",
" False, False, False, True, True, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, True]), array([2.44677740e+01, 3.29985438e+01, 7.13092918e+01, 5.64211888e+01,\n",
" 1.54509011e+01, 1.11658624e+00, 1.87812262e+01, 3.89558813e+01,\n",
" 9.72150779e+01, 5.78711869e+01, 1.56777500e+01, 8.95682827e+01,\n",
" 2.51327007e-02, 4.33098320e+01, 4.95100431e+00, 7.78750423e+00,\n",
" 2.03159154e+01, 2.57117840e+01, 6.39829202e+01, 7.27759893e+00,\n",
" 5.10754128e+01, 1.05971240e-02, 6.51089314e+00, 8.75869838e+00,\n",
" 2.23897534e+01, 5.31341414e+01, 5.80062438e-02, 3.58073011e+01,\n",
" 1.18036918e-01, 1.32133918e-01, 2.21454179e-04, 2.56837981e-02,\n",
" 2.09656993e+01, 1.39029077e-01, 7.33409742e+00, 4.70189827e+01,\n",
" 5.45715861e+01, 7.51930189e+01, 2.93103927e-01, 2.25943396e+00,\n",
" 2.68121938e-01, 3.28754694e+01, 8.80486392e+01, 1.61682450e+01,\n",
" 8.75707363e+01, 2.03855259e+01, 2.01497555e+01, 1.41306446e+01,\n",
" 4.39405657e+01, 8.28053537e+00, 7.36785350e+01, 2.36031698e+01,\n",
" 7.90162057e+01, 1.81686393e+00, 5.49588676e+01, 1.76444324e+01,\n",
" 8.59214668e+01, 6.58684271e+01, 7.02399329e+00, 6.98945587e+01,\n",
" 6.99089901e+00, 6.83073512e+01, 7.90551006e+01, 6.46732160e+00,\n",
" 6.29964799e+01, 9.65470723e+01, 3.53987822e+01, 1.96703464e-01,\n",
" 8.26706688e+00, 9.97367957e+01, 3.54868575e+00, 6.33556532e+01,\n",
" 5.17631824e-01, 5.85916267e+00, 5.72527791e-01, 9.14875297e+01,\n",
" 8.88341003e+01, 2.62037891e+01, 5.44698589e-01, 2.94806683e+01,\n",
" 2.10871067e+01, 1.70404044e+01, 1.73259414e+01, 6.65603237e+01,\n",
" 2.79829894e+01, 5.57878182e+01, 3.93676302e+01, 2.90942330e+01,\n",
" 9.78555205e+00, 1.71681592e+00, 3.74347580e+00, 2.80121684e+01,\n",
" 7.89939136e+01, 4.03277635e+01, 2.13271697e+01, 1.81303993e-01,\n",
" 9.26249584e+00, 1.48573415e+01, 8.51061003e+01, 5.55844610e+00,\n",
" 1.34920701e-02]))\n"
]
}
],
"source": [
"print(bonferroni_correction(model.pvalues, alpha=0.05))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"listEGFR = KIRPE.columns.intersection(lsEGFR)\n",
"KIRPEEGFR = KIRPE[listEGFR]\n",
"KIRPMEGFR = KIRPM[listEGFR]\n",
"KIRPCEGFR = KIRPC[listEGFR] "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>y</td> <th> R-squared: </th> <td> 0.998</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.996</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 356.2</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 12 Jan 2019</td> <th> Prob (F-statistic):</th> <td>2.84e-62</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>18:16:45</td> <th> Log-Likelihood: </th> <td> 409.04</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 161</td> <th> AIC: </th> <td> -616.1</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 60</td> <th> BIC: </th> <td> -304.9</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 101</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>102</th> <td> -0.0203</td> <td> 0.011</td> <td> -1.884</td> <td> 0.064</td> <td> -0.042</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>107</th> <td> 0.0050</td> <td> 0.015</td> <td> 0.340</td> <td> 0.735</td> <td> -0.024</td> <td> 0.034</td>\n",
"</tr>\n",
"<tr>\n",
" <th>108</th> <td> -0.0149</td> <td> 0.006</td> <td> -2.486</td> <td> 0.016</td> <td> -0.027</td> <td> -0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>109</th> <td> 0.0090</td> <td> 0.008</td> <td> 1.139</td> <td> 0.259</td> <td> -0.007</td> <td> 0.025</td>\n",
"</tr>\n",
"<tr>\n",
" <th>111</th> <td> 0.0058</td> <td> 0.005</td> <td> 1.084</td> <td> 0.283</td> <td> -0.005</td> <td> 0.016</td>\n",
"</tr>\n",
"<tr>\n",
" <th>112</th> <td> 0.0015</td> <td> 0.013</td> <td> 0.118</td> <td> 0.906</td> <td> -0.024</td> <td> 0.027</td>\n",
"</tr>\n",
"<tr>\n",
" <th>113</th> <td> -0.0260</td> <td> 0.012</td> <td> -2.093</td> <td> 0.041</td> <td> -0.051</td> <td> -0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>114</th> <td> -0.0026</td> <td> 0.014</td> <td> -0.180</td> <td> 0.858</td> <td> -0.031</td> <td> 0.026</td>\n",
"</tr>\n",
"<tr>\n",
" <th>115</th> <td> 0.0049</td> <td> 0.012</td> <td> 0.412</td> <td> 0.682</td> <td> -0.019</td> <td> 0.029</td>\n",
"</tr>\n",
"<tr>\n",
" <th>160</th> <td> 0.0032</td> <td> 0.018</td> <td> 0.179</td> <td> 0.859</td> <td> -0.032</td> <td> 0.039</td>\n",
"</tr>\n",
"<tr>\n",
" <th>161</th> <td> -0.0461</td> <td> 0.019</td> <td> -2.433</td> <td> 0.018</td> <td> -0.084</td> <td> -0.008</td>\n",
"</tr>\n",
"<tr>\n",
" <th>163</th> <td> 0.0056</td> <td> 0.019</td> <td> 0.295</td> <td> 0.769</td> <td> -0.032</td> <td> 0.044</td>\n",
"</tr>\n",
"<tr>\n",
" <th>207</th> <td> -0.0264</td> <td> 0.016</td> <td> -1.696</td> <td> 0.095</td> <td> -0.058</td> <td> 0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>208</th> <td> 0.0413</td> <td> 0.016</td> <td> 2.571</td> <td> 0.013</td> <td> 0.009</td> <td> 0.073</td>\n",
"</tr>\n",
"<tr>\n",
" <th>572</th> <td> 0.0455</td> <td> 0.019</td> <td> 2.357</td> <td> 0.022</td> <td> 0.007</td> <td> 0.084</td>\n",
"</tr>\n",
"<tr>\n",
" <th>801</th> <td> -0.0083</td> <td> 0.013</td> <td> -0.614</td> <td> 0.542</td> <td> -0.035</td> <td> 0.019</td>\n",
"</tr>\n",
"<tr>\n",
" <th>805</th> <td> -0.0226</td> <td> 0.023</td> <td> -0.972</td> <td> 0.335</td> <td> -0.069</td> <td> 0.024</td>\n",
"</tr>\n",
"<tr>\n",
" <th>808</th> <td> -0.0373</td> <td> 0.018</td> <td> -2.121</td> <td> 0.038</td> <td> -0.072</td> <td> -0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>814</th> <td> 0.0115</td> <td> 0.020</td> <td> 0.561</td> <td> 0.577</td> <td> -0.029</td> <td> 0.052</td>\n",
"</tr>\n",
"<tr>\n",
" <th>842</th> <td> -0.0297</td> <td> 0.015</td> <td> -2.021</td> <td> 0.048</td> <td> -0.059</td> <td> -0.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>867</th> <td> -0.0059</td> <td> 0.024</td> <td> -0.248</td> <td> 0.805</td> <td> -0.053</td> <td> 0.042</td>\n",
"</tr>\n",
"<tr>\n",
" <th>983</th> <td> 0.0012</td> <td> 0.007</td> <td> 0.169</td> <td> 0.867</td> <td> -0.013</td> <td> 0.016</td>\n",
"</tr>\n",
"<tr>\n",
" <th>998</th> <td> 0.0193</td> <td> 0.024</td> <td> 0.822</td> <td> 0.414</td> <td> -0.028</td> <td> 0.066</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1026</th> <td> -0.0095</td> <td> 0.008</td> <td> -1.213</td> <td> 0.230</td> <td> -0.025</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1027</th> <td> -0.0049</td> <td> 0.012</td> <td> -0.417</td> <td> 0.678</td> <td> -0.028</td> <td> 0.019</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1147</th> <td> 0.0307</td> <td> 0.020</td> <td> 1.518</td> <td> 0.134</td> <td> -0.010</td> <td> 0.071</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1173</th> <td> 0.0353</td> <td> 0.019</td> <td> 1.827</td> <td> 0.073</td> <td> -0.003</td> <td> 0.074</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1175</th> <td> 0.0282</td> <td> 0.024</td> <td> 1.194</td> <td> 0.237</td> <td> -0.019</td> <td> 0.075</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1211</th> <td> 0.0049</td> <td> 0.015</td> <td> 0.337</td> <td> 0.738</td> <td> -0.024</td> <td> 0.034</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1213</th> <td> -0.0081</td> <td> 0.017</td> <td> -0.484</td> <td> 0.630</td> <td> -0.041</td> <td> 0.025</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1385</th> <td> -0.0046</td> <td> 0.020</td> <td> -0.234</td> <td> 0.816</td> <td> -0.044</td> <td> 0.035</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1445</th> <td> 0.0122</td> <td> 0.019</td> <td> 0.658</td> <td> 0.513</td> <td> -0.025</td> <td> 0.049</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1950</th> <td> 0.0033</td> <td> 0.008</td> <td> 0.419</td> <td> 0.677</td> <td> -0.012</td> <td> 0.019</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1956</th> <td> 0.0367</td> <td> 0.010</td> <td> 3.749</td> <td> 0.000</td> <td> 0.017</td> <td> 0.056</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2060</th> <td> -0.0484</td> <td> 0.024</td> <td> -2.035</td> <td> 0.046</td> <td> -0.096</td> <td> -0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2308</th> <td> -0.0066</td> <td> 0.013</td> <td> -0.519</td> <td> 0.606</td> <td> -0.032</td> <td> 0.019</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2309</th> <td> 0.0204</td> <td> 0.016</td> <td> 1.298</td> <td> 0.199</td> <td> -0.011</td> <td> 0.052</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2475</th> <td> 0.0076</td> <td> 0.019</td> <td> 0.407</td> <td> 0.685</td> <td> -0.030</td> <td> 0.045</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2549</th> <td> 0.0033</td> <td> 0.014</td> <td> 0.235</td> <td> 0.815</td> <td> -0.025</td> <td> 0.031</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2885</th> <td> 0.0503</td> <td> 0.023</td> <td> 2.180</td> <td> 0.033</td> <td> 0.004</td> <td> 0.097</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2931</th> <td> -0.0348</td> <td> 0.026</td> <td> -1.337</td> <td> 0.186</td> <td> -0.087</td> <td> 0.017</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3164</th> <td> 0.0059</td> <td> 0.003</td> <td> 1.970</td> <td> 0.054</td> <td>-9.21e-05</td> <td> 0.012</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3265</th> <td> -0.0328</td> <td> 0.018</td> <td> -1.859</td> <td> 0.068</td> <td> -0.068</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3320</th> <td> 0.0247</td> <td> 0.012</td> <td> 2.075</td> <td> 0.042</td> <td> 0.001</td> <td> 0.049</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3709</th> <td> -0.0169</td> <td> 0.011</td> <td> -1.583</td> <td> 0.119</td> <td> -0.038</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3710</th> <td> 0.0055</td> <td> 0.006</td> <td> 0.935</td> <td> 0.354</td> <td> -0.006</td> <td> 0.017</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3845</th> <td> 0.0395</td> <td> 0.017</td> <td> 2.365</td> <td> 0.021</td> <td> 0.006</td> <td> 0.073</td>\n",
"</tr>\n",
"<tr>\n",
" <th>4193</th> <td> 0.0065</td> <td> 0.012</td> <td> 0.553</td> <td> 0.582</td> <td> -0.017</td> <td> 0.030</td>\n",
"</tr>\n",
"<tr>\n",
" <th>4303</th> <td> -0.0064</td> <td> 0.014</td> <td> -0.460</td> <td> 0.647</td> <td> -0.034</td> <td> 0.021</td>\n",
"</tr>\n",
"<tr>\n",
" <th>4893</th> <td> 0.0045</td> <td> 0.019</td> <td> 0.238</td> <td> 0.813</td> <td> -0.033</td> <td> 0.042</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5136</th> <td> -0.0038</td> <td> 0.003</td> <td> -1.194</td> <td> 0.237</td> <td> -0.010</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5170</th> <td> 0.0335</td> <td> 0.023</td> <td> 1.470</td> <td> 0.147</td> <td> -0.012</td> <td> 0.079</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5290</th> <td> -0.0036</td> <td> 0.021</td> <td> -0.171</td> <td> 0.865</td> <td> -0.046</td> <td> 0.039</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5295</th> <td> -0.0123</td> <td> 0.010</td> <td> -1.216</td> <td> 0.229</td> <td> -0.033</td> <td> 0.008</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5335</th> <td> 0.0016</td> <td> 0.013</td> <td> 0.119</td> <td> 0.906</td> <td> -0.025</td> <td> 0.028</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5566</th> <td> 0.0412</td> <td> 0.024</td> <td> 1.688</td> <td> 0.097</td> <td> -0.008</td> <td> 0.090</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5567</th> <td> 0.0210</td> <td> 0.013</td> <td> 1.658</td> <td> 0.102</td> <td> -0.004</td> <td> 0.046</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5573</th> <td> -0.0320</td> <td> 0.020</td> <td> -1.609</td> <td> 0.113</td> <td> -0.072</td> <td> 0.008</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5575</th> <td> -0.0102</td> <td> 0.009</td> <td> -1.115</td> <td> 0.269</td> <td> -0.029</td> <td> 0.008</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5576</th> <td> -0.0010</td> <td> 0.013</td> <td> -0.077</td> <td> 0.939</td> <td> -0.027</td> <td> 0.025</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5577</th> <td> 0.0011</td> <td> 0.005</td> <td> 0.212</td> <td> 0.833</td> <td> -0.009</td> <td> 0.012</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5578</th> <td> -0.0180</td> <td> 0.013</td> <td> -1.352</td> <td> 0.181</td> <td> -0.045</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5580</th> <td> -0.0243</td> <td> 0.013</td> <td> -1.828</td> <td> 0.073</td> <td> -0.051</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5581</th> <td> 0.0281</td> <td> 0.015</td> <td> 1.921</td> <td> 0.059</td> <td> -0.001</td> <td> 0.057</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5582</th> <td> -0.0033</td> <td> 0.008</td> <td> -0.436</td> <td> 0.665</td> <td> -0.018</td> <td> 0.012</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5594</th> <td> -0.0107</td> <td> 0.018</td> <td> -0.589</td> <td> 0.558</td> <td> -0.047</td> <td> 0.026</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5595</th> <td> -0.0510</td> <td> 0.021</td> <td> -2.426</td> <td> 0.018</td> <td> -0.093</td> <td> -0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5604</th> <td> -0.0220</td> <td> 0.018</td> <td> -1.243</td> <td> 0.219</td> <td> -0.057</td> <td> 0.013</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5605</th> <td> 0.0326</td> <td> 0.022</td> <td> 1.493</td> <td> 0.141</td> <td> -0.011</td> <td> 0.076</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5728</th> <td> -0.0120</td> <td> 0.016</td> <td> -0.770</td> <td> 0.444</td> <td> -0.043</td> <td> 0.019</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5894</th> <td> 0.0340</td> <td> 0.024</td> <td> 1.439</td> <td> 0.155</td> <td> -0.013</td> <td> 0.081</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6199</th> <td> -0.0215</td> <td> 0.019</td> <td> -1.106</td> <td> 0.273</td> <td> -0.060</td> <td> 0.017</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6456</th> <td> -0.0011</td> <td> 0.004</td> <td> -0.299</td> <td> 0.766</td> <td> -0.009</td> <td> 0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6464</th> <td> 0.0065</td> <td> 0.016</td> <td> 0.405</td> <td> 0.687</td> <td> -0.025</td> <td> 0.038</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6654</th> <td> -0.0203</td> <td> 0.022</td> <td> -0.938</td> <td> 0.352</td> <td> -0.063</td> <td> 0.023</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6714</th> <td> -0.0079</td> <td> 0.011</td> <td> -0.735</td> <td> 0.465</td> <td> -0.029</td> <td> 0.014</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6868</th> <td> -0.0096</td> <td> 0.015</td> <td> -0.627</td> <td> 0.533</td> <td> -0.040</td> <td> 0.021</td>\n",
"</tr>\n",
"<tr>\n",
" <th>7249</th> <td> 0.0322</td> <td> 0.020</td> <td> 1.614</td> <td> 0.112</td> <td> -0.008</td> <td> 0.072</td>\n",
"</tr>\n",
"<tr>\n",
" <th>7311</th> <td> 0.0065</td> <td> 0.021</td> <td> 0.310</td> <td> 0.758</td> <td> -0.036</td> <td> 0.049</td>\n",
"</tr>\n",
"<tr>\n",
" <th>7529</th> <td> 0.0649</td> <td> 0.022</td> <td> 2.910</td> <td> 0.005</td> <td> 0.020</td> <td> 0.109</td>\n",
"</tr>\n",
"<tr>\n",
" <th>8027</th> <td> -0.0083</td> <td> 0.019</td> <td> -0.436</td> <td> 0.665</td> <td> -0.046</td> <td> 0.030</td>\n",
"</tr>\n",
"<tr>\n",
" <th>8038</th> <td> -0.0063</td> <td> 0.006</td> <td> -1.116</td> <td> 0.269</td> <td> -0.018</td> <td> 0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>9146</th> <td> 0.0263</td> <td> 0.022</td> <td> 1.217</td> <td> 0.228</td> <td> -0.017</td> <td> 0.070</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10000</th> <td> 0.0119</td> <td> 0.010</td> <td> 1.229</td> <td> 0.224</td> <td> -0.007</td> <td> 0.031</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10252</th> <td> 0.0047</td> <td> 0.008</td> <td> 0.564</td> <td> 0.575</td> <td> -0.012</td> <td> 0.021</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10253</th> <td> -0.0065</td> <td> 0.009</td> <td> -0.744</td> <td> 0.460</td> <td> -0.024</td> <td> 0.011</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10254</th> <td> -0.0071</td> <td> 0.023</td> <td> -0.305</td> <td> 0.761</td> <td> -0.054</td> <td> 0.040</td>\n",
"</tr>\n",
"<tr>\n",
" <th>11140</th> <td> 0.0294</td> <td> 0.027</td> <td> 1.076</td> <td> 0.286</td> <td> -0.025</td> <td> 0.084</td>\n",
"</tr>\n",
"<tr>\n",
" <th>23239</th> <td> 0.0164</td> <td> 0.012</td> <td> 1.338</td> <td> 0.186</td> <td> -0.008</td> <td> 0.041</td>\n",
"</tr>\n",
"<tr>\n",
" <th>26018</th> <td> -0.0269</td> <td> 0.011</td> <td> -2.414</td> <td> 0.019</td> <td> -0.049</td> <td> -0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>29924</th> <td> -0.0294</td> <td> 0.020</td> <td> -1.479</td> <td> 0.144</td> <td> -0.069</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>30011</th> <td> -0.0023</td> <td> 0.008</td> <td> -0.285</td> <td> 0.777</td> <td> -0.019</td> <td> 0.014</td>\n",
"</tr>\n",
"<tr>\n",
" <th>55824</th> <td> -0.0012</td> <td> 0.013</td> <td> -0.090</td> <td> 0.929</td> <td> -0.028</td> <td> 0.026</td>\n",
"</tr>\n",
"<tr>\n",
" <th>57761</th> <td> 0.0011</td> <td> 0.005</td> <td> 0.213</td> <td> 0.832</td> <td> -0.009</td> <td> 0.011</td>\n",
"</tr>\n",
"<tr>\n",
" <th>58513</th> <td> -0.0452</td> <td> 0.015</td> <td> -3.034</td> <td> 0.004</td> <td> -0.075</td> <td> -0.015</td>\n",
"</tr>\n",
"<tr>\n",
" <th>64223</th> <td> -0.0160</td> <td> 0.022</td> <td> -0.733</td> <td> 0.466</td> <td> -0.060</td> <td> 0.028</td>\n",
"</tr>\n",
"<tr>\n",
" <th>79109</th> <td> 0.0001</td> <td> 0.017</td> <td> 0.006</td> <td> 0.995</td> <td> -0.035</td> <td> 0.035</td>\n",
"</tr>\n",
"<tr>\n",
" <th>84335</th> <td> -0.0121</td> <td> 0.025</td> <td> -0.491</td> <td> 0.625</td> <td> -0.061</td> <td> 0.037</td>\n",
"</tr>\n",
"<tr>\n",
" <th>117145</th> <td> -0.0002</td> <td> 0.017</td> <td> -0.010</td> <td> 0.992</td> <td> -0.034</td> <td> 0.034</td>\n",
"</tr>\n",
"<tr>\n",
" <th>196883</th> <td> -0.0140</td> <td> 0.013</td> <td> -1.094</td> <td> 0.278</td> <td> -0.040</td> <td> 0.012</td>\n",
"</tr>\n",
"<tr>\n",
" <th>253260</th> <td> -0.0265</td> <td> 0.018</td> <td> -1.485</td> <td> 0.143</td> <td> -0.062</td> <td> 0.009</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td> 0.349</td> <th> Durbin-Watson: </th> <td> 1.769</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.840</td> <th> Jarque-Bera (JB): </th> <td> 0.161</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td>-0.067</td> <th> Prob(JB): </th> <td> 0.922</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 3.077</td> <th> Cond. No. </th> <td> 941.</td>\n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.998\n",
"Model: OLS Adj. R-squared: 0.996\n",
"Method: Least Squares F-statistic: 356.2\n",
"Date: Sat, 12 Jan 2019 Prob (F-statistic): 2.84e-62\n",
"Time: 18:16:45 Log-Likelihood: 409.04\n",
"No. Observations: 161 AIC: -616.1\n",
"Df Residuals: 60 BIC: -304.9\n",
"Df Model: 101 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"102 -0.0203 0.011 -1.884 0.064 -0.042 0.001\n",
"107 0.0050 0.015 0.340 0.735 -0.024 0.034\n",
"108 -0.0149 0.006 -2.486 0.016 -0.027 -0.003\n",
"109 0.0090 0.008 1.139 0.259 -0.007 0.025\n",
"111 0.0058 0.005 1.084 0.283 -0.005 0.016\n",
"112 0.0015 0.013 0.118 0.906 -0.024 0.027\n",
"113 -0.0260 0.012 -2.093 0.041 -0.051 -0.001\n",
"114 -0.0026 0.014 -0.180 0.858 -0.031 0.026\n",
"115 0.0049 0.012 0.412 0.682 -0.019 0.029\n",
"160 0.0032 0.018 0.179 0.859 -0.032 0.039\n",
"161 -0.0461 0.019 -2.433 0.018 -0.084 -0.008\n",
"163 0.0056 0.019 0.295 0.769 -0.032 0.044\n",
"207 -0.0264 0.016 -1.696 0.095 -0.058 0.005\n",
"208 0.0413 0.016 2.571 0.013 0.009 0.073\n",
"572 0.0455 0.019 2.357 0.022 0.007 0.084\n",
"801 -0.0083 0.013 -0.614 0.542 -0.035 0.019\n",
"805 -0.0226 0.023 -0.972 0.335 -0.069 0.024\n",
"808 -0.0373 0.018 -2.121 0.038 -0.072 -0.002\n",
"814 0.0115 0.020 0.561 0.577 -0.029 0.052\n",
"842 -0.0297 0.015 -2.021 0.048 -0.059 -0.000\n",
"867 -0.0059 0.024 -0.248 0.805 -0.053 0.042\n",
"983 0.0012 0.007 0.169 0.867 -0.013 0.016\n",
"998 0.0193 0.024 0.822 0.414 -0.028 0.066\n",
"1026 -0.0095 0.008 -1.213 0.230 -0.025 0.006\n",
"1027 -0.0049 0.012 -0.417 0.678 -0.028 0.019\n",
"1147 0.0307 0.020 1.518 0.134 -0.010 0.071\n",
"1173 0.0353 0.019 1.827 0.073 -0.003 0.074\n",
"1175 0.0282 0.024 1.194 0.237 -0.019 0.075\n",
"1211 0.0049 0.015 0.337 0.738 -0.024 0.034\n",
"1213 -0.0081 0.017 -0.484 0.630 -0.041 0.025\n",
"1385 -0.0046 0.020 -0.234 0.816 -0.044 0.035\n",
"1445 0.0122 0.019 0.658 0.513 -0.025 0.049\n",
"1950 0.0033 0.008 0.419 0.677 -0.012 0.019\n",
"1956 0.0367 0.010 3.749 0.000 0.017 0.056\n",
"2060 -0.0484 0.024 -2.035 0.046 -0.096 -0.001\n",
"2308 -0.0066 0.013 -0.519 0.606 -0.032 0.019\n",
"2309 0.0204 0.016 1.298 0.199 -0.011 0.052\n",
"2475 0.0076 0.019 0.407 0.685 -0.030 0.045\n",
"2549 0.0033 0.014 0.235 0.815 -0.025 0.031\n",
"2885 0.0503 0.023 2.180 0.033 0.004 0.097\n",
"2931 -0.0348 0.026 -1.337 0.186 -0.087 0.017\n",
"3164 0.0059 0.003 1.970 0.054 -9.21e-05 0.012\n",
"3265 -0.0328 0.018 -1.859 0.068 -0.068 0.002\n",
"3320 0.0247 0.012 2.075 0.042 0.001 0.049\n",
"3709 -0.0169 0.011 -1.583 0.119 -0.038 0.004\n",
"3710 0.0055 0.006 0.935 0.354 -0.006 0.017\n",
"3845 0.0395 0.017 2.365 0.021 0.006 0.073\n",
"4193 0.0065 0.012 0.553 0.582 -0.017 0.030\n",
"4303 -0.0064 0.014 -0.460 0.647 -0.034 0.021\n",
"4893 0.0045 0.019 0.238 0.813 -0.033 0.042\n",
"5136 -0.0038 0.003 -1.194 0.237 -0.010 0.003\n",
"5170 0.0335 0.023 1.470 0.147 -0.012 0.079\n",
"5290 -0.0036 0.021 -0.171 0.865 -0.046 0.039\n",
"5295 -0.0123 0.010 -1.216 0.229 -0.033 0.008\n",
"5335 0.0016 0.013 0.119 0.906 -0.025 0.028\n",
"5566 0.0412 0.024 1.688 0.097 -0.008 0.090\n",
"5567 0.0210 0.013 1.658 0.102 -0.004 0.046\n",
"5573 -0.0320 0.020 -1.609 0.113 -0.072 0.008\n",
"5575 -0.0102 0.009 -1.115 0.269 -0.029 0.008\n",
"5576 -0.0010 0.013 -0.077 0.939 -0.027 0.025\n",
"5577 0.0011 0.005 0.212 0.833 -0.009 0.012\n",
"5578 -0.0180 0.013 -1.352 0.181 -0.045 0.009\n",
"5580 -0.0243 0.013 -1.828 0.073 -0.051 0.002\n",
"5581 0.0281 0.015 1.921 0.059 -0.001 0.057\n",
"5582 -0.0033 0.008 -0.436 0.665 -0.018 0.012\n",
"5594 -0.0107 0.018 -0.589 0.558 -0.047 0.026\n",
"5595 -0.0510 0.021 -2.426 0.018 -0.093 -0.009\n",
"5604 -0.0220 0.018 -1.243 0.219 -0.057 0.013\n",
"5605 0.0326 0.022 1.493 0.141 -0.011 0.076\n",
"5728 -0.0120 0.016 -0.770 0.444 -0.043 0.019\n",
"5894 0.0340 0.024 1.439 0.155 -0.013 0.081\n",
"6199 -0.0215 0.019 -1.106 0.273 -0.060 0.017\n",
"6456 -0.0011 0.004 -0.299 0.766 -0.009 0.007\n",
"6464 0.0065 0.016 0.405 0.687 -0.025 0.038\n",
"6654 -0.0203 0.022 -0.938 0.352 -0.063 0.023\n",
"6714 -0.0079 0.011 -0.735 0.465 -0.029 0.014\n",
"6868 -0.0096 0.015 -0.627 0.533 -0.040 0.021\n",
"7249 0.0322 0.020 1.614 0.112 -0.008 0.072\n",
"7311 0.0065 0.021 0.310 0.758 -0.036 0.049\n",
"7529 0.0649 0.022 2.910 0.005 0.020 0.109\n",
"8027 -0.0083 0.019 -0.436 0.665 -0.046 0.030\n",
"8038 -0.0063 0.006 -1.116 0.269 -0.018 0.005\n",
"9146 0.0263 0.022 1.217 0.228 -0.017 0.070\n",
"10000 0.0119 0.010 1.229 0.224 -0.007 0.031\n",
"10252 0.0047 0.008 0.564 0.575 -0.012 0.021\n",
"10253 -0.0065 0.009 -0.744 0.460 -0.024 0.011\n",
"10254 -0.0071 0.023 -0.305 0.761 -0.054 0.040\n",
"11140 0.0294 0.027 1.076 0.286 -0.025 0.084\n",
"23239 0.0164 0.012 1.338 0.186 -0.008 0.041\n",
"26018 -0.0269 0.011 -2.414 0.019 -0.049 -0.005\n",
"29924 -0.0294 0.020 -1.479 0.144 -0.069 0.010\n",
"30011 -0.0023 0.008 -0.285 0.777 -0.019 0.014\n",
"55824 -0.0012 0.013 -0.090 0.929 -0.028 0.026\n",
"57761 0.0011 0.005 0.213 0.832 -0.009 0.011\n",
"58513 -0.0452 0.015 -3.034 0.004 -0.075 -0.015\n",
"64223 -0.0160 0.022 -0.733 0.466 -0.060 0.028\n",
"79109 0.0001 0.017 0.006 0.995 -0.035 0.035\n",
"84335 -0.0121 0.025 -0.491 0.625 -0.061 0.037\n",
"117145 -0.0002 0.017 -0.010 0.992 -0.034 0.034\n",
"196883 -0.0140 0.013 -1.094 0.278 -0.040 0.012\n",
"253260 -0.0265 0.018 -1.485 0.143 -0.062 0.009\n",
"==============================================================================\n",
"Omnibus: 0.349 Durbin-Watson: 1.769\n",
"Prob(Omnibus): 0.840 Jarque-Bera (JB): 0.161\n",
"Skew: -0.067 Prob(JB): 0.922\n",
"Kurtosis: 3.077 Cond. No. 941.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = KIRPEEGFR\n",
"y = PredKIRP.detach().numpy()\n",
"\n",
"# Note the difference in argument order\n",
"model = sm.OLS(y, X).fit()\n",
"predictions = model.predict(X) # make the predictions by the model\n",
"\n",
"# Print out the statistics\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(array([False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, True, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False]), array([6.50924133e+00, 7.42508317e+01, 1.58961376e+00, 2.61829337e+01,\n",
" 2.85630855e+01, 9.15220271e+01, 4.09864261e+00, 8.66437911e+01,\n",
" 6.88992687e+01, 8.67118407e+01, 1.81588898e+00, 7.76483486e+01,\n",
" 9.60385004e+00, 1.27689793e+00, 2.19346945e+00, 5.46999451e+01,\n",
" 3.38503661e+01, 3.84427918e+00, 5.82378406e+01, 4.81885670e+00,\n",
" 8.13261753e+01, 8.75253594e+01, 4.18628277e+01, 2.32005412e+01,\n",
" 6.85096311e+01, 1.35581058e+01, 7.33793889e+00, 2.39558196e+01,\n",
" 7.44977002e+01, 6.36432682e+01, 8.23873035e+01, 5.18247252e+01,\n",
" 6.83479532e+01, 4.05302431e-02, 4.67644580e+00, 6.11696195e+01,\n",
" 2.01160277e+01, 6.92197855e+01, 8.23371412e+01, 3.35360038e+00,\n",
" 1.88156864e+01, 5.40424117e+00, 6.86220958e+00, 4.26728145e+00,\n",
" 1.19916210e+01, 3.57124009e+01, 2.14829823e+00, 5.88141982e+01,\n",
" 6.53761469e+01, 8.20869339e+01, 2.39499744e+01, 1.48225996e+01,\n",
" 8.73590669e+01, 2.31123957e+01, 9.14734677e+01, 9.75047102e+00,\n",
" 1.03462166e+01, 1.14103902e+01, 2.72031836e+01, 9.48489001e+01,\n",
" 8.41114074e+01, 1.83243560e+01, 7.32889060e+00, 6.00363356e+00,\n",
" 6.71240175e+01, 5.63404437e+01, 1.84848930e+00, 2.21054211e+01,\n",
" 1.42180451e+01, 4.48681168e+01, 1.57037134e+01, 2.76076095e+01,\n",
" 7.73776675e+01, 6.93695531e+01, 3.55455956e+01, 4.69821872e+01,\n",
" 5.38662274e+01, 1.12966032e+01, 7.65297883e+01, 5.11549906e-01,\n",
" 6.71327921e+01, 2.71551225e+01, 2.30738015e+01, 2.26089771e+01,\n",
" 5.80858590e+01, 4.64402309e+01, 7.68757282e+01, 2.89040346e+01,\n",
" 1.87755282e+01, 1.90200962e+00, 1.45689516e+01, 7.84330917e+01,\n",
" 9.38119545e+01, 8.40761596e+01, 3.60228247e-01, 4.70918026e+01,\n",
" 1.00494392e+02, 6.31281113e+01, 1.00236688e+02, 2.81050319e+01,\n",
" 1.44242354e+01]))\n"
]
}
],
"source": [
"print(bonferroni_correction(model.pvalues, alpha=0.05))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"listEGFR = BLCAE.columns.intersection(lsEGFR)\n",
"BLCAEEGFR = BLCAE[listEGFR]\n",
"BLCAMEGFR = BLCAM[listEGFR]\n",
"BLCACEGFR = BLCAC[listEGFR] "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>y</td> <th> R-squared: </th> <td> 0.998</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.987</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 91.05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 12 Jan 2019</td> <th> Prob (F-statistic):</th> <td>4.86e-18</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>18:16:52</td> <th> Log-Likelihood: </th> <td> 291.96</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 123</td> <th> AIC: </th> <td> -381.9</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 22</td> <th> BIC: </th> <td> -97.88</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 101</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>102</th> <td> 0.0508</td> <td> 0.025</td> <td> 2.072</td> <td> 0.050</td> <td>-4.41e-05</td> <td> 0.102</td>\n",
"</tr>\n",
"<tr>\n",
" <th>107</th> <td> 0.0081</td> <td> 0.011</td> <td> 0.733</td> <td> 0.471</td> <td> -0.015</td> <td> 0.031</td>\n",
"</tr>\n",
"<tr>\n",
" <th>108</th> <td> 0.0587</td> <td> 0.039</td> <td> 1.503</td> <td> 0.147</td> <td> -0.022</td> <td> 0.140</td>\n",
"</tr>\n",
"<tr>\n",
" <th>109</th> <td> 0.0054</td> <td> 0.021</td> <td> 0.253</td> <td> 0.803</td> <td> -0.039</td> <td> 0.050</td>\n",
"</tr>\n",
"<tr>\n",
" <th>111</th> <td> 0.0114</td> <td> 0.018</td> <td> 0.620</td> <td> 0.541</td> <td> -0.027</td> <td> 0.050</td>\n",
"</tr>\n",
"<tr>\n",
" <th>112</th> <td> -0.0243</td> <td> 0.023</td> <td> -1.037</td> <td> 0.311</td> <td> -0.073</td> <td> 0.024</td>\n",
"</tr>\n",
"<tr>\n",
" <th>113</th> <td> 0.0279</td> <td> 0.022</td> <td> 1.292</td> <td> 0.210</td> <td> -0.017</td> <td> 0.073</td>\n",
"</tr>\n",
"<tr>\n",
" <th>114</th> <td> 0.4896</td> <td> 0.285</td> <td> 1.715</td> <td> 0.100</td> <td> -0.102</td> <td> 1.081</td>\n",
"</tr>\n",
"<tr>\n",
" <th>115</th> <td> -0.0062</td> <td> 0.029</td> <td> -0.215</td> <td> 0.831</td> <td> -0.066</td> <td> 0.054</td>\n",
"</tr>\n",
"<tr>\n",
" <th>160</th> <td> -0.0502</td> <td> 0.034</td> <td> -1.455</td> <td> 0.160</td> <td> -0.122</td> <td> 0.021</td>\n",
"</tr>\n",
"<tr>\n",
" <th>161</th> <td> 0.0540</td> <td> 0.036</td> <td> 1.521</td> <td> 0.143</td> <td> -0.020</td> <td> 0.128</td>\n",
"</tr>\n",
"<tr>\n",
" <th>163</th> <td> -0.0213</td> <td> 0.025</td> <td> -0.852</td> <td> 0.403</td> <td> -0.073</td> <td> 0.031</td>\n",
"</tr>\n",
"<tr>\n",
" <th>207</th> <td> 0.0241</td> <td> 0.034</td> <td> 0.714</td> <td> 0.483</td> <td> -0.046</td> <td> 0.094</td>\n",
"</tr>\n",
"<tr>\n",
" <th>208</th> <td> -0.0699</td> <td> 0.042</td> <td> -1.659</td> <td> 0.111</td> <td> -0.157</td> <td> 0.018</td>\n",
"</tr>\n",
"<tr>\n",
" <th>572</th> <td> 0.0345</td> <td> 0.029</td> <td> 1.182</td> <td> 0.250</td> <td> -0.026</td> <td> 0.095</td>\n",
"</tr>\n",
"<tr>\n",
" <th>801</th> <td> 0.0148</td> <td> 0.033</td> <td> 0.448</td> <td> 0.659</td> <td> -0.054</td> <td> 0.083</td>\n",
"</tr>\n",
"<tr>\n",
" <th>805</th> <td> 0.0813</td> <td> 0.027</td> <td> 3.000</td> <td> 0.007</td> <td> 0.025</td> <td> 0.138</td>\n",
"</tr>\n",
"<tr>\n",
" <th>808</th> <td> 0.0226</td> <td> 0.035</td> <td> 0.651</td> <td> 0.522</td> <td> -0.049</td> <td> 0.095</td>\n",
"</tr>\n",
"<tr>\n",
" <th>814</th> <td> 0.0117</td> <td> 0.025</td> <td> 0.469</td> <td> 0.644</td> <td> -0.040</td> <td> 0.063</td>\n",
"</tr>\n",
"<tr>\n",
" <th>842</th> <td> 0.0053</td> <td> 0.026</td> <td> 0.203</td> <td> 0.841</td> <td> -0.049</td> <td> 0.060</td>\n",
"</tr>\n",
"<tr>\n",
" <th>867</th> <td> -0.0095</td> <td> 0.034</td> <td> -0.280</td> <td> 0.782</td> <td> -0.080</td> <td> 0.061</td>\n",
"</tr>\n",
"<tr>\n",
" <th>983</th> <td> 0.0057</td> <td> 0.018</td> <td> 0.317</td> <td> 0.754</td> <td> -0.031</td> <td> 0.043</td>\n",
"</tr>\n",
"<tr>\n",
" <th>998</th> <td> -0.0248</td> <td> 0.042</td> <td> -0.598</td> <td> 0.556</td> <td> -0.111</td> <td> 0.061</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1026</th> <td> 0.0170</td> <td> 0.011</td> <td> 1.504</td> <td> 0.147</td> <td> -0.006</td> <td> 0.040</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1027</th> <td> -0.0315</td> <td> 0.023</td> <td> -1.370</td> <td> 0.185</td> <td> -0.079</td> <td> 0.016</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1147</th> <td> -0.0044</td> <td> 0.032</td> <td> -0.136</td> <td> 0.893</td> <td> -0.071</td> <td> 0.062</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1173</th> <td> 0.0110</td> <td> 0.034</td> <td> 0.319</td> <td> 0.752</td> <td> -0.061</td> <td> 0.083</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1175</th> <td> 0.0015</td> <td> 0.050</td> <td> 0.029</td> <td> 0.977</td> <td> -0.102</td> <td> 0.105</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1211</th> <td> -0.0113</td> <td> 0.024</td> <td> -0.467</td> <td> 0.645</td> <td> -0.062</td> <td> 0.039</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1213</th> <td> -0.0472</td> <td> 0.042</td> <td> -1.116</td> <td> 0.277</td> <td> -0.135</td> <td> 0.041</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1385</th> <td> -0.0493</td> <td> 0.043</td> <td> -1.155</td> <td> 0.261</td> <td> -0.138</td> <td> 0.039</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1445</th> <td> 0.0032</td> <td> 0.033</td> <td> 0.096</td> <td> 0.924</td> <td> -0.065</td> <td> 0.072</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1950</th> <td> 0.0118</td> <td> 0.023</td> <td> 0.507</td> <td> 0.617</td> <td> -0.037</td> <td> 0.060</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1956</th> <td> -0.0207</td> <td> 0.010</td> <td> -2.088</td> <td> 0.049</td> <td> -0.041</td> <td> -0.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2060</th> <td> -0.1096</td> <td> 0.052</td> <td> -2.090</td> <td> 0.048</td> <td> -0.218</td> <td> -0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2308</th> <td> 0.0061</td> <td> 0.021</td> <td> 0.298</td> <td> 0.769</td> <td> -0.037</td> <td> 0.049</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2309</th> <td> 0.0169</td> <td> 0.027</td> <td> 0.615</td> <td> 0.545</td> <td> -0.040</td> <td> 0.074</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2475</th> <td> -0.0019</td> <td> 0.036</td> <td> -0.054</td> <td> 0.957</td> <td> -0.076</td> <td> 0.072</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2549</th> <td> 0.0047</td> <td> 0.024</td> <td> 0.196</td> <td> 0.846</td> <td> -0.045</td> <td> 0.055</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2885</th> <td> -0.0271</td> <td> 0.072</td> <td> -0.378</td> <td> 0.709</td> <td> -0.176</td> <td> 0.121</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2931</th> <td> 0.0249</td> <td> 0.045</td> <td> 0.552</td> <td> 0.587</td> <td> -0.069</td> <td> 0.118</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3164</th> <td> -0.0043</td> <td> 0.009</td> <td> -0.454</td> <td> 0.654</td> <td> -0.024</td> <td> 0.015</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3265</th> <td> -0.0165</td> <td> 0.020</td> <td> -0.815</td> <td> 0.424</td> <td> -0.059</td> <td> 0.026</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3320</th> <td> -0.0002</td> <td> 0.028</td> <td> -0.007</td> <td> 0.994</td> <td> -0.058</td> <td> 0.057</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3709</th> <td> 0.0107</td> <td> 0.025</td> <td> 0.426</td> <td> 0.674</td> <td> -0.041</td> <td> 0.063</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3710</th> <td> -0.0068</td> <td> 0.025</td> <td> -0.278</td> <td> 0.784</td> <td> -0.058</td> <td> 0.044</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3845</th> <td> 0.0211</td> <td> 0.026</td> <td> 0.796</td> <td> 0.435</td> <td> -0.034</td> <td> 0.076</td>\n",
"</tr>\n",
"<tr>\n",
" <th>4193</th> <td> 0.0029</td> <td> 0.013</td> <td> 0.218</td> <td> 0.829</td> <td> -0.025</td> <td> 0.031</td>\n",
"</tr>\n",
"<tr>\n",
" <th>4303</th> <td> -0.0276</td> <td> 0.019</td> <td> -1.427</td> <td> 0.168</td> <td> -0.068</td> <td> 0.012</td>\n",
"</tr>\n",
"<tr>\n",
" <th>4893</th> <td> 0.0211</td> <td> 0.028</td> <td> 0.753</td> <td> 0.460</td> <td> -0.037</td> <td> 0.079</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5136</th> <td> -0.0105</td> <td> 0.022</td> <td> -0.466</td> <td> 0.646</td> <td> -0.057</td> <td> 0.036</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5170</th> <td> 0.0493</td> <td> 0.043</td> <td> 1.140</td> <td> 0.267</td> <td> -0.040</td> <td> 0.139</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5290</th> <td> 0.0312</td> <td> 0.051</td> <td> 0.610</td> <td> 0.548</td> <td> -0.075</td> <td> 0.137</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5295</th> <td> 0.0059</td> <td> 0.016</td> <td> 0.369</td> <td> 0.715</td> <td> -0.027</td> <td> 0.039</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5335</th> <td> 0.0182</td> <td> 0.025</td> <td> 0.722</td> <td> 0.478</td> <td> -0.034</td> <td> 0.071</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5566</th> <td> 0.0261</td> <td> 0.035</td> <td> 0.738</td> <td> 0.468</td> <td> -0.047</td> <td> 0.099</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5567</th> <td> -0.0521</td> <td> 0.022</td> <td> -2.371</td> <td> 0.027</td> <td> -0.098</td> <td> -0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5573</th> <td> 0.0343</td> <td> 0.047</td> <td> 0.724</td> <td> 0.477</td> <td> -0.064</td> <td> 0.133</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5575</th> <td> 0.0220</td> <td> 0.018</td> <td> 1.251</td> <td> 0.224</td> <td> -0.014</td> <td> 0.058</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5576</th> <td> 0.0253</td> <td> 0.032</td> <td> 0.784</td> <td> 0.442</td> <td> -0.042</td> <td> 0.092</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5577</th> <td> -0.0081</td> <td> 0.011</td> <td> -0.739</td> <td> 0.468</td> <td> -0.031</td> <td> 0.015</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5578</th> <td> -0.0108</td> <td> 0.021</td> <td> -0.518</td> <td> 0.609</td> <td> -0.054</td> <td> 0.033</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5580</th> <td> 0.0522</td> <td> 0.019</td> <td> 2.714</td> <td> 0.013</td> <td> 0.012</td> <td> 0.092</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5581</th> <td> -0.0035</td> <td> 0.027</td> <td> -0.132</td> <td> 0.896</td> <td> -0.059</td> <td> 0.052</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5582</th> <td> -0.0975</td> <td> 0.153</td> <td> -0.637</td> <td> 0.531</td> <td> -0.415</td> <td> 0.220</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5594</th> <td> -0.0243</td> <td> 0.034</td> <td> -0.714</td> <td> 0.483</td> <td> -0.095</td> <td> 0.046</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5595</th> <td> 0.0219</td> <td> 0.018</td> <td> 1.201</td> <td> 0.243</td> <td> -0.016</td> <td> 0.060</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5604</th> <td> -0.0548</td> <td> 0.045</td> <td> -1.231</td> <td> 0.231</td> <td> -0.147</td> <td> 0.038</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5605</th> <td> -0.0198</td> <td> 0.042</td> <td> -0.471</td> <td> 0.643</td> <td> -0.107</td> <td> 0.067</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5728</th> <td> 0.0233</td> <td> 0.025</td> <td> 0.939</td> <td> 0.358</td> <td> -0.028</td> <td> 0.075</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5894</th> <td> -0.0257</td> <td> 0.021</td> <td> -1.216</td> <td> 0.237</td> <td> -0.069</td> <td> 0.018</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6199</th> <td> 0.0482</td> <td> 0.036</td> <td> 1.332</td> <td> 0.196</td> <td> -0.027</td> <td> 0.123</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6456</th> <td> -0.0083</td> <td> 0.010</td> <td> -0.844</td> <td> 0.408</td> <td> -0.029</td> <td> 0.012</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6464</th> <td> -0.0142</td> <td> 0.015</td> <td> -0.923</td> <td> 0.366</td> <td> -0.046</td> <td> 0.018</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6654</th> <td> 0.0373</td> <td> 0.032</td> <td> 1.178</td> <td> 0.252</td> <td> -0.028</td> <td> 0.103</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6714</th> <td> -0.0454</td> <td> 0.029</td> <td> -1.579</td> <td> 0.129</td> <td> -0.105</td> <td> 0.014</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6868</th> <td> -0.0322</td> <td> 0.026</td> <td> -1.261</td> <td> 0.220</td> <td> -0.085</td> <td> 0.021</td>\n",
"</tr>\n",
"<tr>\n",
" <th>7249</th> <td> -0.0030</td> <td> 0.050</td> <td> -0.060</td> <td> 0.953</td> <td> -0.107</td> <td> 0.101</td>\n",
"</tr>\n",
"<tr>\n",
" <th>7311</th> <td> 0.0187</td> <td> 0.039</td> <td> 0.480</td> <td> 0.636</td> <td> -0.062</td> <td> 0.099</td>\n",
"</tr>\n",
"<tr>\n",
" <th>7529</th> <td> 0.0140</td> <td> 0.033</td> <td> 0.427</td> <td> 0.674</td> <td> -0.054</td> <td> 0.082</td>\n",
"</tr>\n",
"<tr>\n",
" <th>8027</th> <td> -0.0421</td> <td> 0.026</td> <td> -1.641</td> <td> 0.115</td> <td> -0.095</td> <td> 0.011</td>\n",
"</tr>\n",
"<tr>\n",
" <th>8038</th> <td> -0.0077</td> <td> 0.012</td> <td> -0.620</td> <td> 0.542</td> <td> -0.033</td> <td> 0.018</td>\n",
"</tr>\n",
"<tr>\n",
" <th>9146</th> <td> 0.0875</td> <td> 0.045</td> <td> 1.952</td> <td> 0.064</td> <td> -0.005</td> <td> 0.180</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10000</th> <td> -0.0031</td> <td> 0.017</td> <td> -0.184</td> <td> 0.856</td> <td> -0.038</td> <td> 0.031</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10252</th> <td> -0.0044</td> <td> 0.022</td> <td> -0.205</td> <td> 0.840</td> <td> -0.049</td> <td> 0.040</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10253</th> <td> -0.0123</td> <td> 0.014</td> <td> -0.861</td> <td> 0.398</td> <td> -0.042</td> <td> 0.017</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10254</th> <td> -0.0406</td> <td> 0.037</td> <td> -1.092</td> <td> 0.286</td> <td> -0.118</td> <td> 0.036</td>\n",
"</tr>\n",
"<tr>\n",
" <th>11140</th> <td> -0.0482</td> <td> 0.039</td> <td> -1.249</td> <td> 0.225</td> <td> -0.128</td> <td> 0.032</td>\n",
"</tr>\n",
"<tr>\n",
" <th>23239</th> <td> 0.0444</td> <td> 0.022</td> <td> 2.051</td> <td> 0.052</td> <td> -0.000</td> <td> 0.089</td>\n",
"</tr>\n",
"<tr>\n",
" <th>26018</th> <td> -0.0159</td> <td> 0.010</td> <td> -1.601</td> <td> 0.124</td> <td> -0.036</td> <td> 0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>29924</th> <td> -0.0028</td> <td> 0.038</td> <td> -0.073</td> <td> 0.942</td> <td> -0.083</td> <td> 0.077</td>\n",
"</tr>\n",
"<tr>\n",
" <th>30011</th> <td> 0.0035</td> <td> 0.011</td> <td> 0.316</td> <td> 0.755</td> <td> -0.020</td> <td> 0.027</td>\n",
"</tr>\n",
"<tr>\n",
" <th>55824</th> <td> 0.0007</td> <td> 0.022</td> <td> 0.033</td> <td> 0.974</td> <td> -0.045</td> <td> 0.047</td>\n",
"</tr>\n",
"<tr>\n",
" <th>57761</th> <td> -0.0003</td> <td> 0.013</td> <td> -0.020</td> <td> 0.984</td> <td> -0.028</td> <td> 0.027</td>\n",
"</tr>\n",
"<tr>\n",
" <th>58513</th> <td> 0.0410</td> <td> 0.043</td> <td> 0.946</td> <td> 0.354</td> <td> -0.049</td> <td> 0.131</td>\n",
"</tr>\n",
"<tr>\n",
" <th>64223</th> <td> -0.1063</td> <td> 0.041</td> <td> -2.564</td> <td> 0.018</td> <td> -0.192</td> <td> -0.020</td>\n",
"</tr>\n",
"<tr>\n",
" <th>79109</th> <td> 0.0476</td> <td> 0.033</td> <td> 1.462</td> <td> 0.158</td> <td> -0.020</td> <td> 0.115</td>\n",
"</tr>\n",
"<tr>\n",
" <th>84335</th> <td> 0.0365</td> <td> 0.033</td> <td> 1.117</td> <td> 0.276</td> <td> -0.031</td> <td> 0.104</td>\n",
"</tr>\n",
"<tr>\n",
" <th>117145</th> <td> 0.0150</td> <td> 0.018</td> <td> 0.821</td> <td> 0.421</td> <td> -0.023</td> <td> 0.053</td>\n",
"</tr>\n",
"<tr>\n",
" <th>196883</th> <td> -0.0293</td> <td> 0.029</td> <td> -1.017</td> <td> 0.320</td> <td> -0.089</td> <td> 0.030</td>\n",
"</tr>\n",
"<tr>\n",
" <th>253260</th> <td> -0.0124</td> <td> 0.029</td> <td> -0.433</td> <td> 0.669</td> <td> -0.072</td> <td> 0.047</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td> 7.830</td> <th> Durbin-Watson: </th> <td> 2.262</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.020</td> <th> Jarque-Bera (JB): </th> <td> 8.086</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.465</td> <th> Prob(JB): </th> <td> 0.0175</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 3.845</td> <th> Cond. No. </th> <td>3.01e+03</td>\n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.01e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.998\n",
"Model: OLS Adj. R-squared: 0.987\n",
"Method: Least Squares F-statistic: 91.05\n",
"Date: Sat, 12 Jan 2019 Prob (F-statistic): 4.86e-18\n",
"Time: 18:16:52 Log-Likelihood: 291.96\n",
"No. Observations: 123 AIC: -381.9\n",
"Df Residuals: 22 BIC: -97.88\n",
"Df Model: 101 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"102 0.0508 0.025 2.072 0.050 -4.41e-05 0.102\n",
"107 0.0081 0.011 0.733 0.471 -0.015 0.031\n",
"108 0.0587 0.039 1.503 0.147 -0.022 0.140\n",
"109 0.0054 0.021 0.253 0.803 -0.039 0.050\n",
"111 0.0114 0.018 0.620 0.541 -0.027 0.050\n",
"112 -0.0243 0.023 -1.037 0.311 -0.073 0.024\n",
"113 0.0279 0.022 1.292 0.210 -0.017 0.073\n",
"114 0.4896 0.285 1.715 0.100 -0.102 1.081\n",
"115 -0.0062 0.029 -0.215 0.831 -0.066 0.054\n",
"160 -0.0502 0.034 -1.455 0.160 -0.122 0.021\n",
"161 0.0540 0.036 1.521 0.143 -0.020 0.128\n",
"163 -0.0213 0.025 -0.852 0.403 -0.073 0.031\n",
"207 0.0241 0.034 0.714 0.483 -0.046 0.094\n",
"208 -0.0699 0.042 -1.659 0.111 -0.157 0.018\n",
"572 0.0345 0.029 1.182 0.250 -0.026 0.095\n",
"801 0.0148 0.033 0.448 0.659 -0.054 0.083\n",
"805 0.0813 0.027 3.000 0.007 0.025 0.138\n",
"808 0.0226 0.035 0.651 0.522 -0.049 0.095\n",
"814 0.0117 0.025 0.469 0.644 -0.040 0.063\n",
"842 0.0053 0.026 0.203 0.841 -0.049 0.060\n",
"867 -0.0095 0.034 -0.280 0.782 -0.080 0.061\n",
"983 0.0057 0.018 0.317 0.754 -0.031 0.043\n",
"998 -0.0248 0.042 -0.598 0.556 -0.111 0.061\n",
"1026 0.0170 0.011 1.504 0.147 -0.006 0.040\n",
"1027 -0.0315 0.023 -1.370 0.185 -0.079 0.016\n",
"1147 -0.0044 0.032 -0.136 0.893 -0.071 0.062\n",
"1173 0.0110 0.034 0.319 0.752 -0.061 0.083\n",
"1175 0.0015 0.050 0.029 0.977 -0.102 0.105\n",
"1211 -0.0113 0.024 -0.467 0.645 -0.062 0.039\n",
"1213 -0.0472 0.042 -1.116 0.277 -0.135 0.041\n",
"1385 -0.0493 0.043 -1.155 0.261 -0.138 0.039\n",
"1445 0.0032 0.033 0.096 0.924 -0.065 0.072\n",
"1950 0.0118 0.023 0.507 0.617 -0.037 0.060\n",
"1956 -0.0207 0.010 -2.088 0.049 -0.041 -0.000\n",
"2060 -0.1096 0.052 -2.090 0.048 -0.218 -0.001\n",
"2308 0.0061 0.021 0.298 0.769 -0.037 0.049\n",
"2309 0.0169 0.027 0.615 0.545 -0.040 0.074\n",
"2475 -0.0019 0.036 -0.054 0.957 -0.076 0.072\n",
"2549 0.0047 0.024 0.196 0.846 -0.045 0.055\n",
"2885 -0.0271 0.072 -0.378 0.709 -0.176 0.121\n",
"2931 0.0249 0.045 0.552 0.587 -0.069 0.118\n",
"3164 -0.0043 0.009 -0.454 0.654 -0.024 0.015\n",
"3265 -0.0165 0.020 -0.815 0.424 -0.059 0.026\n",
"3320 -0.0002 0.028 -0.007 0.994 -0.058 0.057\n",
"3709 0.0107 0.025 0.426 0.674 -0.041 0.063\n",
"3710 -0.0068 0.025 -0.278 0.784 -0.058 0.044\n",
"3845 0.0211 0.026 0.796 0.435 -0.034 0.076\n",
"4193 0.0029 0.013 0.218 0.829 -0.025 0.031\n",
"4303 -0.0276 0.019 -1.427 0.168 -0.068 0.012\n",
"4893 0.0211 0.028 0.753 0.460 -0.037 0.079\n",
"5136 -0.0105 0.022 -0.466 0.646 -0.057 0.036\n",
"5170 0.0493 0.043 1.140 0.267 -0.040 0.139\n",
"5290 0.0312 0.051 0.610 0.548 -0.075 0.137\n",
"5295 0.0059 0.016 0.369 0.715 -0.027 0.039\n",
"5335 0.0182 0.025 0.722 0.478 -0.034 0.071\n",
"5566 0.0261 0.035 0.738 0.468 -0.047 0.099\n",
"5567 -0.0521 0.022 -2.371 0.027 -0.098 -0.007\n",
"5573 0.0343 0.047 0.724 0.477 -0.064 0.133\n",
"5575 0.0220 0.018 1.251 0.224 -0.014 0.058\n",
"5576 0.0253 0.032 0.784 0.442 -0.042 0.092\n",
"5577 -0.0081 0.011 -0.739 0.468 -0.031 0.015\n",
"5578 -0.0108 0.021 -0.518 0.609 -0.054 0.033\n",
"5580 0.0522 0.019 2.714 0.013 0.012 0.092\n",
"5581 -0.0035 0.027 -0.132 0.896 -0.059 0.052\n",
"5582 -0.0975 0.153 -0.637 0.531 -0.415 0.220\n",
"5594 -0.0243 0.034 -0.714 0.483 -0.095 0.046\n",
"5595 0.0219 0.018 1.201 0.243 -0.016 0.060\n",
"5604 -0.0548 0.045 -1.231 0.231 -0.147 0.038\n",
"5605 -0.0198 0.042 -0.471 0.643 -0.107 0.067\n",
"5728 0.0233 0.025 0.939 0.358 -0.028 0.075\n",
"5894 -0.0257 0.021 -1.216 0.237 -0.069 0.018\n",
"6199 0.0482 0.036 1.332 0.196 -0.027 0.123\n",
"6456 -0.0083 0.010 -0.844 0.408 -0.029 0.012\n",
"6464 -0.0142 0.015 -0.923 0.366 -0.046 0.018\n",
"6654 0.0373 0.032 1.178 0.252 -0.028 0.103\n",
"6714 -0.0454 0.029 -1.579 0.129 -0.105 0.014\n",
"6868 -0.0322 0.026 -1.261 0.220 -0.085 0.021\n",
"7249 -0.0030 0.050 -0.060 0.953 -0.107 0.101\n",
"7311 0.0187 0.039 0.480 0.636 -0.062 0.099\n",
"7529 0.0140 0.033 0.427 0.674 -0.054 0.082\n",
"8027 -0.0421 0.026 -1.641 0.115 -0.095 0.011\n",
"8038 -0.0077 0.012 -0.620 0.542 -0.033 0.018\n",
"9146 0.0875 0.045 1.952 0.064 -0.005 0.180\n",
"10000 -0.0031 0.017 -0.184 0.856 -0.038 0.031\n",
"10252 -0.0044 0.022 -0.205 0.840 -0.049 0.040\n",
"10253 -0.0123 0.014 -0.861 0.398 -0.042 0.017\n",
"10254 -0.0406 0.037 -1.092 0.286 -0.118 0.036\n",
"11140 -0.0482 0.039 -1.249 0.225 -0.128 0.032\n",
"23239 0.0444 0.022 2.051 0.052 -0.000 0.089\n",
"26018 -0.0159 0.010 -1.601 0.124 -0.036 0.005\n",
"29924 -0.0028 0.038 -0.073 0.942 -0.083 0.077\n",
"30011 0.0035 0.011 0.316 0.755 -0.020 0.027\n",
"55824 0.0007 0.022 0.033 0.974 -0.045 0.047\n",
"57761 -0.0003 0.013 -0.020 0.984 -0.028 0.027\n",
"58513 0.0410 0.043 0.946 0.354 -0.049 0.131\n",
"64223 -0.1063 0.041 -2.564 0.018 -0.192 -0.020\n",
"79109 0.0476 0.033 1.462 0.158 -0.020 0.115\n",
"84335 0.0365 0.033 1.117 0.276 -0.031 0.104\n",
"117145 0.0150 0.018 0.821 0.421 -0.023 0.053\n",
"196883 -0.0293 0.029 -1.017 0.320 -0.089 0.030\n",
"253260 -0.0124 0.029 -0.433 0.669 -0.072 0.047\n",
"==============================================================================\n",
"Omnibus: 7.830 Durbin-Watson: 2.262\n",
"Prob(Omnibus): 0.020 Jarque-Bera (JB): 8.086\n",
"Skew: 0.465 Prob(JB): 0.0175\n",
"Kurtosis: 3.845 Cond. No. 3.01e+03\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 3.01e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = BLCAEEGFR\n",
"y = PredBLCA.detach().numpy()\n",
"\n",
"# Note the difference in argument order\n",
"model = sm.OLS(y, X).fit()\n",
"predictions = model.predict(X) # make the predictions by the model\n",
"\n",
"# Print out the statistics\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(array([False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False]), array([ 5.06840256, 47.60953626, 14.86290717, 81.05458141,\n",
" 54.68706095, 31.41998312, 21.17450173, 10.13547604,\n",
" 83.97572787, 16.15018927, 14.39431539, 40.7389554 ,\n",
" 48.7622678 , 11.25163908, 25.24933182, 66.53217609,\n",
" 0.66653408, 52.68913068, 65.01624174, 84.97032154,\n",
" 78.96788527, 76.19414517, 56.17863377, 14.82209503,\n",
" 18.64538126, 90.23406241, 76.00057555, 98.65567388,\n",
" 65.17566821, 27.92891398, 26.31132821, 93.33782611,\n",
" 62.32498483, 4.90432369, 4.88402139, 77.6546395 ,\n",
" 55.05906614, 96.68173076, 85.47561601, 71.6146374 ,\n",
" 59.23824967, 66.09782514, 42.82351029, 100.43457182,\n",
" 68.10069573, 79.14434754, 43.89125917, 83.76063748,\n",
" 16.92229075, 46.42142942, 65.22891846, 26.92473721,\n",
" 55.37319615, 72.25861436, 48.27420002, 47.27537543,\n",
" 2.72175867, 48.17531057, 22.64234997, 44.59470743,\n",
" 47.22090177, 61.55844715, 1.28104209, 90.54064326,\n",
" 53.63107252, 48.75299788, 24.49973515, 23.37963417,\n",
" 64.89529953, 36.14780333, 23.93290514, 19.82990307,\n",
" 41.20579122, 36.98475504, 25.406636 , 12.98970355,\n",
" 22.25623324, 96.23507665, 64.22040675, 68.02830777,\n",
" 11.62448639, 54.70568442, 6.43666445, 86.45827115,\n",
" 84.79664747, 40.22874239, 28.93374255, 22.700239 ,\n",
" 5.29036532, 12.49659856, 95.16948586, 76.25114624,\n",
" 98.35586247, 99.42202676, 35.79864472, 1.78787595,\n",
" 15.94299284, 27.89141517, 42.48126526, 32.32102167,\n",
" 67.60312619]))\n"
]
}
],
"source": [
"print(bonferroni_correction(model.pvalues, alpha=0.05))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"listEGFR = BRCAE.columns.intersection(lsEGFR)\n",
"BRCAEEGFR = BRCAE[listEGFR]\n",
"BRCAMEGFR = BRCAM[listEGFR]\n",
"BRCACEGFR = BRCAC[listEGFR] "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>y</td> <th> R-squared: </th> <td> 0.999</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.999</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 6893.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 12 Jan 2019</td> <th> Prob (F-statistic):</th> <td> 0.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>18:16:54</td> <th> Log-Likelihood: </th> <td> 2466.9</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 921</td> <th> AIC: </th> <td> -4732.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 820</td> <th> BIC: </th> <td> -4245.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 101</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>102</th> <td> 0.0011</td> <td> 0.002</td> <td> 0.672</td> <td> 0.502</td> <td> -0.002</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>107</th> <td> -0.0011</td> <td> 0.000</td> <td> -2.468</td> <td> 0.014</td> <td> -0.002</td> <td> -0.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>108</th> <td> -0.0013</td> <td> 0.001</td> <td> -2.000</td> <td> 0.046</td> <td> -0.003</td> <td>-2.48e-05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>109</th> <td> 0.0002</td> <td> 0.001</td> <td> 0.183</td> <td> 0.855</td> <td> -0.002</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>111</th> <td> -0.0003</td> <td> 0.001</td> <td> -0.611</td> <td> 0.541</td> <td> -0.001</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>112</th> <td> 0.0015</td> <td> 0.001</td> <td> 1.019</td> <td> 0.309</td> <td> -0.001</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>113</th> <td> -0.0020</td> <td> 0.001</td> <td> -1.351</td> <td> 0.177</td> <td> -0.005</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>114</th> <td> -0.0026</td> <td> 0.002</td> <td> -1.382</td> <td> 0.167</td> <td> -0.006</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>115</th> <td> 0.0008</td> <td> 0.001</td> <td> 0.651</td> <td> 0.515</td> <td> -0.002</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>160</th> <td> 0.0067</td> <td> 0.002</td> <td> 3.978</td> <td> 0.000</td> <td> 0.003</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>161</th> <td> -0.0014</td> <td> 0.002</td> <td> -0.675</td> <td> 0.500</td> <td> -0.006</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>163</th> <td> 5.495e-05</td> <td> 0.001</td> <td> 0.047</td> <td> 0.962</td> <td> -0.002</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>207</th> <td> 0.0028</td> <td> 0.002</td> <td> 1.587</td> <td> 0.113</td> <td> -0.001</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>208</th> <td> -0.0023</td> <td> 0.002</td> <td> -1.390</td> <td> 0.165</td> <td> -0.006</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>572</th> <td> -0.0023</td> <td> 0.002</td> <td> -1.392</td> <td> 0.164</td> <td> -0.005</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>801</th> <td> 0.0022</td> <td> 0.002</td> <td> 1.102</td> <td> 0.271</td> <td> -0.002</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>805</th> <td> 0.0063</td> <td> 0.002</td> <td> 3.883</td> <td> 0.000</td> <td> 0.003</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>808</th> <td> 0.0019</td> <td> 0.002</td> <td> 0.939</td> <td> 0.348</td> <td> -0.002</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>814</th> <td> 0.0010</td> <td> 0.002</td> <td> 0.553</td> <td> 0.580</td> <td> -0.003</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>842</th> <td> -0.0025</td> <td> 0.002</td> <td> -1.337</td> <td> 0.182</td> <td> -0.006</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>867</th> <td> -0.0038</td> <td> 0.002</td> <td> -1.849</td> <td> 0.065</td> <td> -0.008</td> <td> 0.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>983</th> <td> 1.694e-05</td> <td> 0.001</td> <td> 0.019</td> <td> 0.985</td> <td> -0.002</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>998</th> <td> 0.0023</td> <td> 0.002</td> <td> 0.999</td> <td> 0.318</td> <td> -0.002</td> <td> 0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1026</th> <td> -0.0022</td> <td> 0.001</td> <td> -2.660</td> <td> 0.008</td> <td> -0.004</td> <td> -0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1027</th> <td> 0.0012</td> <td> 0.001</td> <td> 1.529</td> <td> 0.127</td> <td> -0.000</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1147</th> <td> 0.0039</td> <td> 0.002</td> <td> 2.011</td> <td> 0.045</td> <td> 9.45e-05</td> <td> 0.008</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1173</th> <td> -0.0016</td> <td> 0.002</td> <td> -0.793</td> <td> 0.428</td> <td> -0.006</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1175</th> <td> 0.0050</td> <td> 0.002</td> <td> 2.424</td> <td> 0.016</td> <td> 0.001</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1211</th> <td> 0.0079</td> <td> 0.002</td> <td> 4.775</td> <td> 0.000</td> <td> 0.005</td> <td> 0.011</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1213</th> <td> 0.0011</td> <td> 0.001</td> <td> 0.815</td> <td> 0.415</td> <td> -0.002</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1385</th> <td> -0.0048</td> <td> 0.003</td> <td> -1.743</td> <td> 0.082</td> <td> -0.010</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1445</th> <td> -0.0036</td> <td> 0.002</td> <td> -1.679</td> <td> 0.094</td> <td> -0.008</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1950</th> <td> 0.0010</td> <td> 0.001</td> <td> 1.717</td> <td> 0.086</td> <td> -0.000</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1956</th> <td> 0.0041</td> <td> 0.001</td> <td> 5.366</td> <td> 0.000</td> <td> 0.003</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2060</th> <td> -0.0023</td> <td> 0.002</td> <td> -1.028</td> <td> 0.304</td> <td> -0.007</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2308</th> <td> -0.0014</td> <td> 0.002</td> <td> -0.885</td> <td> 0.377</td> <td> -0.004</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2309</th> <td> 0.0012</td> <td> 0.001</td> <td> 0.939</td> <td> 0.348</td> <td> -0.001</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2475</th> <td> -0.0078</td> <td> 0.002</td> <td> -3.325</td> <td> 0.001</td> <td> -0.012</td> <td> -0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2549</th> <td> -0.0019</td> <td> 0.001</td> <td> -1.341</td> <td> 0.180</td> <td> -0.005</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2885</th> <td> -0.0030</td> <td> 0.001</td> <td> -2.034</td> <td> 0.042</td> <td> -0.006</td> <td> -0.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2931</th> <td> 0.0036</td> <td> 0.002</td> <td> 1.483</td> <td> 0.138</td> <td> -0.001</td> <td> 0.008</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3164</th> <td> 0.0007</td> <td> 0.001</td> <td> 1.135</td> <td> 0.257</td> <td> -0.000</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3265</th> <td> 0.0008</td> <td> 0.001</td> <td> 0.563</td> <td> 0.574</td> <td> -0.002</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3320</th> <td> 0.0034</td> <td> 0.001</td> <td> 2.468</td> <td> 0.014</td> <td> 0.001</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3709</th> <td> 0.0015</td> <td> 0.001</td> <td> 2.033</td> <td> 0.042</td> <td> 5.28e-05</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3710</th> <td> -0.0004</td> <td> 0.001</td> <td> -0.382</td> <td> 0.703</td> <td> -0.003</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3845</th> <td> 0.0007</td> <td> 0.001</td> <td> 0.462</td> <td> 0.644</td> <td> -0.002</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>4193</th> <td> -0.0005</td> <td> 0.001</td> <td> -0.443</td> <td> 0.658</td> <td> -0.003</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>4303</th> <td> 0.0018</td> <td> 0.001</td> <td> 1.436</td> <td> 0.151</td> <td> -0.001</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>4893</th> <td> -0.0007</td> <td> 0.001</td> <td> -0.608</td> <td> 0.544</td> <td> -0.003</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5136</th> <td> -0.0017</td> <td> 0.001</td> <td> -1.156</td> <td> 0.248</td> <td> -0.004</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5170</th> <td> -0.0022</td> <td> 0.002</td> <td> -0.930</td> <td> 0.352</td> <td> -0.007</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5290</th> <td> 0.0066</td> <td> 0.002</td> <td> 3.998</td> <td> 0.000</td> <td> 0.003</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5295</th> <td> -0.0001</td> <td> 0.001</td> <td> -0.152</td> <td> 0.880</td> <td> -0.002</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5335</th> <td> 0.0002</td> <td> 0.002</td> <td> 0.112</td> <td> 0.911</td> <td> -0.003</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5566</th> <td> 0.0012</td> <td> 0.002</td> <td> 0.609</td> <td> 0.543</td> <td> -0.003</td> <td> 0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5567</th> <td> -0.0010</td> <td> 0.001</td> <td> -1.916</td> <td> 0.056</td> <td> -0.002</td> <td> 2.43e-05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5573</th> <td> 0.0001</td> <td> 0.002</td> <td> 0.059</td> <td> 0.953</td> <td> -0.004</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5575</th> <td> -0.0016</td> <td> 0.001</td> <td> -1.479</td> <td> 0.139</td> <td> -0.004</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5576</th> <td> -0.0040</td> <td> 0.002</td> <td> -2.427</td> <td> 0.015</td> <td> -0.007</td> <td> -0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5577</th> <td> 0.0004</td> <td> 0.001</td> <td> 0.627</td> <td> 0.531</td> <td> -0.001</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5578</th> <td> 0.0007</td> <td> 0.001</td> <td> 0.548</td> <td> 0.584</td> <td> -0.002</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5580</th> <td> -0.0017</td> <td> 0.001</td> <td> -1.395</td> <td> 0.163</td> <td> -0.004</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5581</th> <td> -0.0006</td> <td> 0.001</td> <td> -0.528</td> <td> 0.598</td> <td> -0.003</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5582</th> <td> -0.0025</td> <td> 0.003</td> <td> -0.781</td> <td> 0.435</td> <td> -0.009</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5594</th> <td> 0.0020</td> <td> 0.002</td> <td> 1.066</td> <td> 0.287</td> <td> -0.002</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5595</th> <td> 0.0047</td> <td> 0.002</td> <td> 2.902</td> <td> 0.004</td> <td> 0.002</td> <td> 0.008</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5604</th> <td> 0.0006</td> <td> 0.002</td> <td> 0.311</td> <td> 0.756</td> <td> -0.003</td> <td> 0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5605</th> <td> 0.0032</td> <td> 0.002</td> <td> 1.607</td> <td> 0.108</td> <td> -0.001</td> <td> 0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5728</th> <td> 0.0010</td> <td> 0.001</td> <td> 0.796</td> <td> 0.426</td> <td> -0.002</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5894</th> <td> 0.0035</td> <td> 0.002</td> <td> 1.753</td> <td> 0.080</td> <td> -0.000</td> <td> 0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6199</th> <td> -0.0008</td> <td> 0.001</td> <td> -0.650</td> <td> 0.516</td> <td> -0.003</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6456</th> <td> -0.0005</td> <td> 0.001</td> <td> -0.475</td> <td> 0.635</td> <td> -0.003</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6464</th> <td> 0.0028</td> <td> 0.002</td> <td> 1.581</td> <td> 0.114</td> <td> -0.001</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6654</th> <td> 0.0027</td> <td> 0.002</td> <td> 1.176</td> <td> 0.240</td> <td> -0.002</td> <td> 0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6714</th> <td> -0.0009</td> <td> 0.001</td> <td> -0.742</td> <td> 0.458</td> <td> -0.003</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6868</th> <td> -0.0019</td> <td> 0.002</td> <td> -0.882</td> <td> 0.378</td> <td> -0.006</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>7249</th> <td> 0.0059</td> <td> 0.002</td> <td> 2.794</td> <td> 0.005</td> <td> 0.002</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>7311</th> <td> 0.0100</td> <td> 0.002</td> <td> 5.557</td> <td> 0.000</td> <td> 0.006</td> <td> 0.014</td>\n",
"</tr>\n",
"<tr>\n",
" <th>7529</th> <td> 0.0060</td> <td> 0.002</td> <td> 3.019</td> <td> 0.003</td> <td> 0.002</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>8027</th> <td> 0.0003</td> <td> 0.002</td> <td> 0.130</td> <td> 0.897</td> <td> -0.004</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>8038</th> <td> -0.0018</td> <td> 0.001</td> <td> -2.249</td> <td> 0.025</td> <td> -0.003</td> <td> -0.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>9146</th> <td> 0.0067</td> <td> 0.002</td> <td> 3.353</td> <td> 0.001</td> <td> 0.003</td> <td> 0.011</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10000</th> <td> 0.0001</td> <td> 0.001</td> <td> 0.161</td> <td> 0.872</td> <td> -0.001</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10252</th> <td> 0.0016</td> <td> 0.001</td> <td> 1.389</td> <td> 0.165</td> <td> -0.001</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10253</th> <td> -0.0026</td> <td> 0.001</td> <td> -2.333</td> <td> 0.020</td> <td> -0.005</td> <td> -0.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10254</th> <td> 0.0004</td> <td> 0.002</td> <td> 0.148</td> <td> 0.883</td> <td> -0.004</td> <td> 0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>11140</th> <td> 0.0034</td> <td> 0.002</td> <td> 1.598</td> <td> 0.111</td> <td> -0.001</td> <td> 0.008</td>\n",
"</tr>\n",
"<tr>\n",
" <th>23239</th> <td> -0.0026</td> <td> 0.001</td> <td> -2.319</td> <td> 0.021</td> <td> -0.005</td> <td> -0.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>26018</th> <td> 0.0004</td> <td> 0.001</td> <td> 0.409</td> <td> 0.683</td> <td> -0.002</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>29924</th> <td> 0.0002</td> <td> 0.002</td> <td> 0.084</td> <td> 0.933</td> <td> -0.004</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>30011</th> <td> 0.0027</td> <td> 0.001</td> <td> 1.923</td> <td> 0.055</td> <td>-5.67e-05</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>55824</th> <td> -0.0003</td> <td> 0.002</td> <td> -0.210</td> <td> 0.833</td> <td> -0.004</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>57761</th> <td> 0.0011</td> <td> 0.001</td> <td> 1.311</td> <td> 0.190</td> <td> -0.001</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>58513</th> <td> -0.0034</td> <td> 0.002</td> <td> -1.942</td> <td> 0.052</td> <td> -0.007</td> <td> 3.6e-05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>64223</th> <td> 0.0007</td> <td> 0.002</td> <td> 0.316</td> <td> 0.752</td> <td> -0.004</td> <td> 0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>79109</th> <td> 0.0022</td> <td> 0.002</td> <td> 1.107</td> <td> 0.268</td> <td> -0.002</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>84335</th> <td> -0.0057</td> <td> 0.002</td> <td> -2.607</td> <td> 0.009</td> <td> -0.010</td> <td> -0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>117145</th> <td> 0.0032</td> <td> 0.001</td> <td> 2.624</td> <td> 0.009</td> <td> 0.001</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>196883</th> <td> -0.0030</td> <td> 0.001</td> <td> -2.182</td> <td> 0.029</td> <td> -0.006</td> <td> -0.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>253260</th> <td> -0.0004</td> <td> 0.002</td> <td> -0.247</td> <td> 0.805</td> <td> -0.004</td> <td> 0.003</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>126.984</td> <th> Durbin-Watson: </th> <td> 1.943</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 391.225</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.676</td> <th> Prob(JB): </th> <td>1.11e-85</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 5.893</td> <th> Cond. No. </th> <td> 311.</td>\n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.999\n",
"Model: OLS Adj. R-squared: 0.999\n",
"Method: Least Squares F-statistic: 6893.\n",
"Date: Sat, 12 Jan 2019 Prob (F-statistic): 0.00\n",
"Time: 18:16:54 Log-Likelihood: 2466.9\n",
"No. Observations: 921 AIC: -4732.\n",
"Df Residuals: 820 BIC: -4245.\n",
"Df Model: 101 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"102 0.0011 0.002 0.672 0.502 -0.002 0.004\n",
"107 -0.0011 0.000 -2.468 0.014 -0.002 -0.000\n",
"108 -0.0013 0.001 -2.000 0.046 -0.003 -2.48e-05\n",
"109 0.0002 0.001 0.183 0.855 -0.002 0.003\n",
"111 -0.0003 0.001 -0.611 0.541 -0.001 0.001\n",
"112 0.0015 0.001 1.019 0.309 -0.001 0.004\n",
"113 -0.0020 0.001 -1.351 0.177 -0.005 0.001\n",
"114 -0.0026 0.002 -1.382 0.167 -0.006 0.001\n",
"115 0.0008 0.001 0.651 0.515 -0.002 0.003\n",
"160 0.0067 0.002 3.978 0.000 0.003 0.010\n",
"161 -0.0014 0.002 -0.675 0.500 -0.006 0.003\n",
"163 5.495e-05 0.001 0.047 0.962 -0.002 0.002\n",
"207 0.0028 0.002 1.587 0.113 -0.001 0.006\n",
"208 -0.0023 0.002 -1.390 0.165 -0.006 0.001\n",
"572 -0.0023 0.002 -1.392 0.164 -0.005 0.001\n",
"801 0.0022 0.002 1.102 0.271 -0.002 0.006\n",
"805 0.0063 0.002 3.883 0.000 0.003 0.010\n",
"808 0.0019 0.002 0.939 0.348 -0.002 0.006\n",
"814 0.0010 0.002 0.553 0.580 -0.003 0.004\n",
"842 -0.0025 0.002 -1.337 0.182 -0.006 0.001\n",
"867 -0.0038 0.002 -1.849 0.065 -0.008 0.000\n",
"983 1.694e-05 0.001 0.019 0.985 -0.002 0.002\n",
"998 0.0023 0.002 0.999 0.318 -0.002 0.007\n",
"1026 -0.0022 0.001 -2.660 0.008 -0.004 -0.001\n",
"1027 0.0012 0.001 1.529 0.127 -0.000 0.003\n",
"1147 0.0039 0.002 2.011 0.045 9.45e-05 0.008\n",
"1173 -0.0016 0.002 -0.793 0.428 -0.006 0.002\n",
"1175 0.0050 0.002 2.424 0.016 0.001 0.009\n",
"1211 0.0079 0.002 4.775 0.000 0.005 0.011\n",
"1213 0.0011 0.001 0.815 0.415 -0.002 0.004\n",
"1385 -0.0048 0.003 -1.743 0.082 -0.010 0.001\n",
"1445 -0.0036 0.002 -1.679 0.094 -0.008 0.001\n",
"1950 0.0010 0.001 1.717 0.086 -0.000 0.002\n",
"1956 0.0041 0.001 5.366 0.000 0.003 0.006\n",
"2060 -0.0023 0.002 -1.028 0.304 -0.007 0.002\n",
"2308 -0.0014 0.002 -0.885 0.377 -0.004 0.002\n",
"2309 0.0012 0.001 0.939 0.348 -0.001 0.004\n",
"2475 -0.0078 0.002 -3.325 0.001 -0.012 -0.003\n",
"2549 -0.0019 0.001 -1.341 0.180 -0.005 0.001\n",
"2885 -0.0030 0.001 -2.034 0.042 -0.006 -0.000\n",
"2931 0.0036 0.002 1.483 0.138 -0.001 0.008\n",
"3164 0.0007 0.001 1.135 0.257 -0.000 0.002\n",
"3265 0.0008 0.001 0.563 0.574 -0.002 0.004\n",
"3320 0.0034 0.001 2.468 0.014 0.001 0.006\n",
"3709 0.0015 0.001 2.033 0.042 5.28e-05 0.003\n",
"3710 -0.0004 0.001 -0.382 0.703 -0.003 0.002\n",
"3845 0.0007 0.001 0.462 0.644 -0.002 0.004\n",
"4193 -0.0005 0.001 -0.443 0.658 -0.003 0.002\n",
"4303 0.0018 0.001 1.436 0.151 -0.001 0.004\n",
"4893 -0.0007 0.001 -0.608 0.544 -0.003 0.001\n",
"5136 -0.0017 0.001 -1.156 0.248 -0.004 0.001\n",
"5170 -0.0022 0.002 -0.930 0.352 -0.007 0.002\n",
"5290 0.0066 0.002 3.998 0.000 0.003 0.010\n",
"5295 -0.0001 0.001 -0.152 0.880 -0.002 0.002\n",
"5335 0.0002 0.002 0.112 0.911 -0.003 0.004\n",
"5566 0.0012 0.002 0.609 0.543 -0.003 0.005\n",
"5567 -0.0010 0.001 -1.916 0.056 -0.002 2.43e-05\n",
"5573 0.0001 0.002 0.059 0.953 -0.004 0.004\n",
"5575 -0.0016 0.001 -1.479 0.139 -0.004 0.001\n",
"5576 -0.0040 0.002 -2.427 0.015 -0.007 -0.001\n",
"5577 0.0004 0.001 0.627 0.531 -0.001 0.002\n",
"5578 0.0007 0.001 0.548 0.584 -0.002 0.003\n",
"5580 -0.0017 0.001 -1.395 0.163 -0.004 0.001\n",
"5581 -0.0006 0.001 -0.528 0.598 -0.003 0.002\n",
"5582 -0.0025 0.003 -0.781 0.435 -0.009 0.004\n",
"5594 0.0020 0.002 1.066 0.287 -0.002 0.006\n",
"5595 0.0047 0.002 2.902 0.004 0.002 0.008\n",
"5604 0.0006 0.002 0.311 0.756 -0.003 0.005\n",
"5605 0.0032 0.002 1.607 0.108 -0.001 0.007\n",
"5728 0.0010 0.001 0.796 0.426 -0.002 0.004\n",
"5894 0.0035 0.002 1.753 0.080 -0.000 0.007\n",
"6199 -0.0008 0.001 -0.650 0.516 -0.003 0.002\n",
"6456 -0.0005 0.001 -0.475 0.635 -0.003 0.002\n",
"6464 0.0028 0.002 1.581 0.114 -0.001 0.006\n",
"6654 0.0027 0.002 1.176 0.240 -0.002 0.007\n",
"6714 -0.0009 0.001 -0.742 0.458 -0.003 0.002\n",
"6868 -0.0019 0.002 -0.882 0.378 -0.006 0.002\n",
"7249 0.0059 0.002 2.794 0.005 0.002 0.010\n",
"7311 0.0100 0.002 5.557 0.000 0.006 0.014\n",
"7529 0.0060 0.002 3.019 0.003 0.002 0.010\n",
"8027 0.0003 0.002 0.130 0.897 -0.004 0.004\n",
"8038 -0.0018 0.001 -2.249 0.025 -0.003 -0.000\n",
"9146 0.0067 0.002 3.353 0.001 0.003 0.011\n",
"10000 0.0001 0.001 0.161 0.872 -0.001 0.002\n",
"10252 0.0016 0.001 1.389 0.165 -0.001 0.004\n",
"10253 -0.0026 0.001 -2.333 0.020 -0.005 -0.000\n",
"10254 0.0004 0.002 0.148 0.883 -0.004 0.005\n",
"11140 0.0034 0.002 1.598 0.111 -0.001 0.008\n",
"23239 -0.0026 0.001 -2.319 0.021 -0.005 -0.000\n",
"26018 0.0004 0.001 0.409 0.683 -0.002 0.002\n",
"29924 0.0002 0.002 0.084 0.933 -0.004 0.004\n",
"30011 0.0027 0.001 1.923 0.055 -5.67e-05 0.006\n",
"55824 -0.0003 0.002 -0.210 0.833 -0.004 0.003\n",
"57761 0.0011 0.001 1.311 0.190 -0.001 0.003\n",
"58513 -0.0034 0.002 -1.942 0.052 -0.007 3.6e-05\n",
"64223 0.0007 0.002 0.316 0.752 -0.004 0.005\n",
"79109 0.0022 0.002 1.107 0.268 -0.002 0.006\n",
"84335 -0.0057 0.002 -2.607 0.009 -0.010 -0.001\n",
"117145 0.0032 0.001 2.624 0.009 0.001 0.006\n",
"196883 -0.0030 0.001 -2.182 0.029 -0.006 -0.000\n",
"253260 -0.0004 0.002 -0.247 0.805 -0.004 0.003\n",
"==============================================================================\n",
"Omnibus: 126.984 Durbin-Watson: 1.943\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 391.225\n",
"Skew: 0.676 Prob(JB): 1.11e-85\n",
"Kurtosis: 5.893 Cond. No. 311.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = BRCAEEGFR\n",
"y = PredBRCA.detach().numpy()\n",
"\n",
"# Note the difference in argument order\n",
"model = sm.OLS(y, X).fit()\n",
"predictions = model.predict(X) # make the predictions by the model\n",
"\n",
"# Print out the statistics\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(array([False, False, False, False, False, False, False, False, False,\n",
" True, False, False, False, False, False, False, True, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, True, False, False, False, False, True, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, True, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, True, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False]), array([5.06825579e+01, 1.39415496e+00, 4.62728727e+00, 8.63416692e+01,\n",
" 5.46703973e+01, 3.11693984e+01, 1.78878580e+01, 1.69076294e+01,\n",
" 5.20628937e+01, 7.63775258e-03, 5.05012625e+01, 9.71747798e+01,\n",
" 1.14047619e+01, 1.66547204e+01, 1.66002678e+01, 2.73452647e+01,\n",
" 1.12394342e-02, 3.51638004e+01, 5.86190456e+01, 1.83319447e+01,\n",
" 6.54695299e+00, 9.94635493e+01, 3.21067659e+01, 8.05601110e-01,\n",
" 1.27822910e+01, 4.50467121e+00, 4.32100404e+01, 1.57037777e+00,\n",
" 2.14790333e-04, 4.19184595e+01, 8.24890270e+00, 9.44769827e+00,\n",
" 8.71708891e+00, 1.05952842e-05, 3.07063472e+01, 3.80319057e+01,\n",
" 3.51583973e+01, 9.33166240e-02, 1.82235046e+01, 4.27457416e+00,\n",
" 1.39790238e+01, 2.59382965e+01, 5.79606884e+01, 1.39245484e+00,\n",
" 4.27958874e+00, 7.09755293e+01, 6.50415536e+01, 6.64791509e+01,\n",
" 1.52878177e+01, 5.49098096e+01, 2.50604528e+01, 3.55940643e+01,\n",
" 7.04010042e-03, 8.88385006e+01, 9.20001680e+01, 5.48126545e+01,\n",
" 5.62914348e+00, 9.62605127e+01, 1.40816890e+01, 1.55929716e+00,\n",
" 5.35888105e+01, 5.89525602e+01, 1.65058150e+01, 6.03911904e+01,\n",
" 4.39569903e+01, 2.89789530e+01, 3.84153378e-01, 7.63121342e+01,\n",
" 1.09480929e+01, 4.30656336e+01, 8.07928547e+00, 5.20798960e+01,\n",
" 6.41025395e+01, 1.15472161e+01, 2.42427222e+01, 4.62966468e+01,\n",
" 3.81851918e+01, 5.37540468e-01, 3.74789829e-06, 2.64334431e-01,\n",
" 9.05906863e+01, 2.50371075e+00, 8.43106091e-02, 8.80570964e+01,\n",
" 1.66905322e+01, 2.00775994e+00, 8.91590873e+01, 1.11621004e+01,\n",
" 2.08549764e+00, 6.89553451e+01, 9.42277767e+01, 5.53765760e+00,\n",
" 8.41771233e+01, 1.92219594e+01, 5.29466937e+00, 7.59562851e+01,\n",
" 2.71121774e+01, 9.38706483e-01, 8.93055121e-01, 2.97120494e+00,\n",
" 8.13250292e+01]))\n"
]
}
],
"source": [
"print(bonferroni_correction(model.pvalues, alpha=0.05))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"listEGFR = PAADE.columns.intersection(lsEGFR)\n",
"PAADEEGFR = PAADE[listEGFR]\n",
"PAADMEGFR = PAADM[listEGFR]\n",
"PAADCEGFR = PAADC[listEGFR] "
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>y</td> <th> R-squared: </th> <td> 0.999</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.995</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 251.5</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 12 Jan 2019</td> <th> Prob (F-statistic):</th> <td>1.78e-29</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>18:17:00</td> <th> Log-Likelihood: </th> <td> 364.21</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 130</td> <th> AIC: </th> <td> -526.4</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 29</td> <th> BIC: </th> <td> -236.8</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 101</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>102</th> <td> -0.0263</td> <td> 0.023</td> <td> -1.162</td> <td> 0.255</td> <td> -0.072</td> <td> 0.020</td>\n",
"</tr>\n",
"<tr>\n",
" <th>107</th> <td> 0.0055</td> <td> 0.013</td> <td> 0.414</td> <td> 0.682</td> <td> -0.022</td> <td> 0.032</td>\n",
"</tr>\n",
"<tr>\n",
" <th>108</th> <td> -0.0179</td> <td> 0.025</td> <td> -0.717</td> <td> 0.479</td> <td> -0.069</td> <td> 0.033</td>\n",
"</tr>\n",
"<tr>\n",
" <th>109</th> <td> 0.0112</td> <td> 0.019</td> <td> 0.600</td> <td> 0.553</td> <td> -0.027</td> <td> 0.049</td>\n",
"</tr>\n",
"<tr>\n",
" <th>111</th> <td> 0.0037</td> <td> 0.013</td> <td> 0.282</td> <td> 0.780</td> <td> -0.023</td> <td> 0.031</td>\n",
"</tr>\n",
"<tr>\n",
" <th>112</th> <td> -0.0120</td> <td> 0.023</td> <td> -0.527</td> <td> 0.602</td> <td> -0.058</td> <td> 0.034</td>\n",
"</tr>\n",
"<tr>\n",
" <th>113</th> <td> 0.0143</td> <td> 0.014</td> <td> 1.031</td> <td> 0.311</td> <td> -0.014</td> <td> 0.043</td>\n",
"</tr>\n",
"<tr>\n",
" <th>114</th> <td> -0.0118</td> <td> 0.039</td> <td> -0.302</td> <td> 0.765</td> <td> -0.092</td> <td> 0.068</td>\n",
"</tr>\n",
"<tr>\n",
" <th>115</th> <td> 0.0008</td> <td> 0.036</td> <td> 0.022</td> <td> 0.983</td> <td> -0.072</td> <td> 0.074</td>\n",
"</tr>\n",
"<tr>\n",
" <th>160</th> <td> -0.0357</td> <td> 0.032</td> <td> -1.114</td> <td> 0.274</td> <td> -0.101</td> <td> 0.030</td>\n",
"</tr>\n",
"<tr>\n",
" <th>161</th> <td> -0.0005</td> <td> 0.027</td> <td> -0.017</td> <td> 0.987</td> <td> -0.056</td> <td> 0.056</td>\n",
"</tr>\n",
"<tr>\n",
" <th>163</th> <td> 0.0254</td> <td> 0.031</td> <td> 0.828</td> <td> 0.415</td> <td> -0.037</td> <td> 0.088</td>\n",
"</tr>\n",
"<tr>\n",
" <th>207</th> <td> 0.0099</td> <td> 0.031</td> <td> 0.323</td> <td> 0.749</td> <td> -0.053</td> <td> 0.073</td>\n",
"</tr>\n",
"<tr>\n",
" <th>208</th> <td> -0.0149</td> <td> 0.016</td> <td> -0.959</td> <td> 0.346</td> <td> -0.047</td> <td> 0.017</td>\n",
"</tr>\n",
"<tr>\n",
" <th>572</th> <td> 0.0064</td> <td> 0.024</td> <td> 0.267</td> <td> 0.791</td> <td> -0.042</td> <td> 0.055</td>\n",
"</tr>\n",
"<tr>\n",
" <th>801</th> <td> 0.0163</td> <td> 0.040</td> <td> 0.408</td> <td> 0.686</td> <td> -0.065</td> <td> 0.098</td>\n",
"</tr>\n",
"<tr>\n",
" <th>805</th> <td> 0.0394</td> <td> 0.032</td> <td> 1.232</td> <td> 0.228</td> <td> -0.026</td> <td> 0.105</td>\n",
"</tr>\n",
"<tr>\n",
" <th>808</th> <td> 0.0136</td> <td> 0.037</td> <td> 0.367</td> <td> 0.716</td> <td> -0.062</td> <td> 0.089</td>\n",
"</tr>\n",
"<tr>\n",
" <th>814</th> <td> -0.0174</td> <td> 0.015</td> <td> -1.129</td> <td> 0.268</td> <td> -0.049</td> <td> 0.014</td>\n",
"</tr>\n",
"<tr>\n",
" <th>842</th> <td> -0.0067</td> <td> 0.021</td> <td> -0.327</td> <td> 0.746</td> <td> -0.049</td> <td> 0.035</td>\n",
"</tr>\n",
"<tr>\n",
" <th>867</th> <td> -0.0127</td> <td> 0.029</td> <td> -0.442</td> <td> 0.661</td> <td> -0.072</td> <td> 0.046</td>\n",
"</tr>\n",
"<tr>\n",
" <th>983</th> <td> 0.0013</td> <td> 0.015</td> <td> 0.089</td> <td> 0.930</td> <td> -0.029</td> <td> 0.032</td>\n",
"</tr>\n",
"<tr>\n",
" <th>998</th> <td> -0.0207</td> <td> 0.043</td> <td> -0.482</td> <td> 0.633</td> <td> -0.109</td> <td> 0.067</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1026</th> <td> -0.0120</td> <td> 0.014</td> <td> -0.878</td> <td> 0.387</td> <td> -0.040</td> <td> 0.016</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1027</th> <td> 0.0094</td> <td> 0.025</td> <td> 0.382</td> <td> 0.705</td> <td> -0.041</td> <td> 0.060</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1147</th> <td> -0.0039</td> <td> 0.035</td> <td> -0.111</td> <td> 0.912</td> <td> -0.075</td> <td> 0.068</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1173</th> <td> 0.0050</td> <td> 0.041</td> <td> 0.121</td> <td> 0.905</td> <td> -0.079</td> <td> 0.089</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1175</th> <td> 0.0105</td> <td> 0.033</td> <td> 0.318</td> <td> 0.753</td> <td> -0.057</td> <td> 0.078</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1211</th> <td> -0.0214</td> <td> 0.015</td> <td> -1.443</td> <td> 0.160</td> <td> -0.052</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1213</th> <td> 0.0339</td> <td> 0.041</td> <td> 0.830</td> <td> 0.413</td> <td> -0.050</td> <td> 0.117</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1385</th> <td> -0.0315</td> <td> 0.034</td> <td> -0.915</td> <td> 0.367</td> <td> -0.102</td> <td> 0.039</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1445</th> <td> 0.0012</td> <td> 0.022</td> <td> 0.056</td> <td> 0.956</td> <td> -0.044</td> <td> 0.046</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1950</th> <td> 2.53e-05</td> <td> 0.011</td> <td> 0.002</td> <td> 0.998</td> <td> -0.023</td> <td> 0.023</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1956</th> <td> 0.0078</td> <td> 0.017</td> <td> 0.457</td> <td> 0.651</td> <td> -0.027</td> <td> 0.043</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2060</th> <td> 0.0024</td> <td> 0.044</td> <td> 0.054</td> <td> 0.958</td> <td> -0.087</td> <td> 0.092</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2308</th> <td> -0.0160</td> <td> 0.020</td> <td> -0.788</td> <td> 0.437</td> <td> -0.057</td> <td> 0.025</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2309</th> <td> 0.0524</td> <td> 0.019</td> <td> 2.699</td> <td> 0.011</td> <td> 0.013</td> <td> 0.092</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2475</th> <td> -0.0235</td> <td> 0.034</td> <td> -0.688</td> <td> 0.497</td> <td> -0.093</td> <td> 0.046</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2549</th> <td> -0.0339</td> <td> 0.028</td> <td> -1.213</td> <td> 0.235</td> <td> -0.091</td> <td> 0.023</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2885</th> <td> 0.0448</td> <td> 0.041</td> <td> 1.094</td> <td> 0.283</td> <td> -0.039</td> <td> 0.129</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2931</th> <td> 0.0273</td> <td> 0.035</td> <td> 0.787</td> <td> 0.438</td> <td> -0.044</td> <td> 0.098</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3164</th> <td> 0.0046</td> <td> 0.006</td> <td> 0.796</td> <td> 0.433</td> <td> -0.007</td> <td> 0.016</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3265</th> <td> -0.0047</td> <td> 0.023</td> <td> -0.207</td> <td> 0.838</td> <td> -0.051</td> <td> 0.042</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3320</th> <td> 0.0023</td> <td> 0.023</td> <td> 0.100</td> <td> 0.921</td> <td> -0.045</td> <td> 0.050</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3709</th> <td> 0.0019</td> <td> 0.016</td> <td> 0.120</td> <td> 0.905</td> <td> -0.030</td> <td> 0.034</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3710</th> <td> 0.0056</td> <td> 0.017</td> <td> 0.323</td> <td> 0.749</td> <td> -0.030</td> <td> 0.041</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3845</th> <td> 0.0280</td> <td> 0.030</td> <td> 0.933</td> <td> 0.359</td> <td> -0.033</td> <td> 0.090</td>\n",
"</tr>\n",
"<tr>\n",
" <th>4193</th> <td> 0.0034</td> <td> 0.016</td> <td> 0.206</td> <td> 0.838</td> <td> -0.030</td> <td> 0.037</td>\n",
"</tr>\n",
"<tr>\n",
" <th>4303</th> <td> -0.0149</td> <td> 0.020</td> <td> -0.760</td> <td> 0.454</td> <td> -0.055</td> <td> 0.025</td>\n",
"</tr>\n",
"<tr>\n",
" <th>4893</th> <td> -0.0206</td> <td> 0.037</td> <td> -0.560</td> <td> 0.579</td> <td> -0.096</td> <td> 0.055</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5136</th> <td> 0.0218</td> <td> 0.018</td> <td> 1.187</td> <td> 0.245</td> <td> -0.016</td> <td> 0.059</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5170</th> <td> 0.0066</td> <td> 0.041</td> <td> 0.160</td> <td> 0.874</td> <td> -0.078</td> <td> 0.091</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5290</th> <td> -0.0134</td> <td> 0.039</td> <td> -0.345</td> <td> 0.732</td> <td> -0.093</td> <td> 0.066</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5295</th> <td> 0.0044</td> <td> 0.022</td> <td> 0.196</td> <td> 0.846</td> <td> -0.041</td> <td> 0.050</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5335</th> <td> 0.0357</td> <td> 0.038</td> <td> 0.950</td> <td> 0.350</td> <td> -0.041</td> <td> 0.113</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5566</th> <td> 0.0261</td> <td> 0.033</td> <td> 0.804</td> <td> 0.428</td> <td> -0.040</td> <td> 0.093</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5567</th> <td> -0.0081</td> <td> 0.014</td> <td> -0.573</td> <td> 0.571</td> <td> -0.037</td> <td> 0.021</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5573</th> <td> -0.0622</td> <td> 0.036</td> <td> -1.748</td> <td> 0.091</td> <td> -0.135</td> <td> 0.011</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5575</th> <td> -0.0046</td> <td> 0.018</td> <td> -0.255</td> <td> 0.801</td> <td> -0.042</td> <td> 0.032</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5576</th> <td> 0.0045</td> <td> 0.022</td> <td> 0.205</td> <td> 0.839</td> <td> -0.040</td> <td> 0.049</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5577</th> <td> -0.0068</td> <td> 0.012</td> <td> -0.554</td> <td> 0.584</td> <td> -0.032</td> <td> 0.018</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5578</th> <td> -0.0009</td> <td> 0.018</td> <td> -0.047</td> <td> 0.963</td> <td> -0.039</td> <td> 0.037</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5580</th> <td> -0.0330</td> <td> 0.019</td> <td> -1.732</td> <td> 0.094</td> <td> -0.072</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5581</th> <td> -0.0278</td> <td> 0.027</td> <td> -1.015</td> <td> 0.319</td> <td> -0.084</td> <td> 0.028</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5582</th> <td> 0.0025</td> <td> 0.006</td> <td> 0.455</td> <td> 0.652</td> <td> -0.009</td> <td> 0.014</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5594</th> <td> -0.0124</td> <td> 0.032</td> <td> -0.386</td> <td> 0.702</td> <td> -0.078</td> <td> 0.053</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5595</th> <td> -0.0193</td> <td> 0.024</td> <td> -0.796</td> <td> 0.433</td> <td> -0.069</td> <td> 0.030</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5604</th> <td> -0.0006</td> <td> 0.035</td> <td> -0.018</td> <td> 0.986</td> <td> -0.071</td> <td> 0.070</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5605</th> <td> 0.0225</td> <td> 0.037</td> <td> 0.606</td> <td> 0.549</td> <td> -0.053</td> <td> 0.098</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5728</th> <td> -0.0036</td> <td> 0.034</td> <td> -0.106</td> <td> 0.917</td> <td> -0.072</td> <td> 0.065</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5894</th> <td> 0.0304</td> <td> 0.044</td> <td> 0.698</td> <td> 0.491</td> <td> -0.059</td> <td> 0.120</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6199</th> <td> -0.0566</td> <td> 0.030</td> <td> -1.915</td> <td> 0.065</td> <td> -0.117</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6456</th> <td> 0.0099</td> <td> 0.011</td> <td> 0.881</td> <td> 0.385</td> <td> -0.013</td> <td> 0.033</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6464</th> <td> -0.0511</td> <td> 0.024</td> <td> -2.113</td> <td> 0.043</td> <td> -0.100</td> <td> -0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6654</th> <td> -0.0389</td> <td> 0.034</td> <td> -1.147</td> <td> 0.261</td> <td> -0.108</td> <td> 0.030</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6714</th> <td> 0.0157</td> <td> 0.018</td> <td> 0.861</td> <td> 0.396</td> <td> -0.022</td> <td> 0.053</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6868</th> <td> 0.0148</td> <td> 0.031</td> <td> 0.477</td> <td> 0.637</td> <td> -0.049</td> <td> 0.078</td>\n",
"</tr>\n",
"<tr>\n",
" <th>7249</th> <td> 0.0824</td> <td> 0.044</td> <td> 1.858</td> <td> 0.073</td> <td> -0.008</td> <td> 0.173</td>\n",
"</tr>\n",
"<tr>\n",
" <th>7311</th> <td> 0.0085</td> <td> 0.031</td> <td> 0.270</td> <td> 0.789</td> <td> -0.056</td> <td> 0.073</td>\n",
"</tr>\n",
"<tr>\n",
" <th>7529</th> <td> -0.0009</td> <td> 0.039</td> <td> -0.023</td> <td> 0.982</td> <td> -0.082</td> <td> 0.080</td>\n",
"</tr>\n",
"<tr>\n",
" <th>8027</th> <td> -0.0035</td> <td> 0.027</td> <td> -0.133</td> <td> 0.895</td> <td> -0.058</td> <td> 0.051</td>\n",
"</tr>\n",
"<tr>\n",
" <th>8038</th> <td> 0.0074</td> <td> 0.009</td> <td> 0.807</td> <td> 0.426</td> <td> -0.011</td> <td> 0.026</td>\n",
"</tr>\n",
"<tr>\n",
" <th>9146</th> <td> 0.0232</td> <td> 0.033</td> <td> 0.706</td> <td> 0.486</td> <td> -0.044</td> <td> 0.090</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10000</th> <td> -0.0273</td> <td> 0.024</td> <td> -1.146</td> <td> 0.261</td> <td> -0.076</td> <td> 0.021</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10252</th> <td> 0.0124</td> <td> 0.015</td> <td> 0.815</td> <td> 0.422</td> <td> -0.019</td> <td> 0.044</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10253</th> <td> -0.0064</td> <td> 0.016</td> <td> -0.395</td> <td> 0.696</td> <td> -0.040</td> <td> 0.027</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10254</th> <td> 0.0475</td> <td> 0.034</td> <td> 1.379</td> <td> 0.178</td> <td> -0.023</td> <td> 0.118</td>\n",
"</tr>\n",
"<tr>\n",
" <th>11140</th> <td> 0.0289</td> <td> 0.038</td> <td> 0.753</td> <td> 0.458</td> <td> -0.050</td> <td> 0.108</td>\n",
"</tr>\n",
"<tr>\n",
" <th>23239</th> <td> 0.0138</td> <td> 0.026</td> <td> 0.539</td> <td> 0.594</td> <td> -0.038</td> <td> 0.066</td>\n",
"</tr>\n",
"<tr>\n",
" <th>26018</th> <td> 0.0022</td> <td> 0.015</td> <td> 0.148</td> <td> 0.884</td> <td> -0.029</td> <td> 0.033</td>\n",
"</tr>\n",
"<tr>\n",
" <th>29924</th> <td> 0.0047</td> <td> 0.033</td> <td> 0.143</td> <td> 0.887</td> <td> -0.062</td> <td> 0.072</td>\n",
"</tr>\n",
"<tr>\n",
" <th>30011</th> <td> 0.0248</td> <td> 0.022</td> <td> 1.143</td> <td> 0.262</td> <td> -0.020</td> <td> 0.069</td>\n",
"</tr>\n",
"<tr>\n",
" <th>55824</th> <td> 0.0005</td> <td> 0.020</td> <td> 0.025</td> <td> 0.980</td> <td> -0.040</td> <td> 0.042</td>\n",
"</tr>\n",
"<tr>\n",
" <th>57761</th> <td> -0.0018</td> <td> 0.013</td> <td> -0.137</td> <td> 0.892</td> <td> -0.029</td> <td> 0.025</td>\n",
"</tr>\n",
"<tr>\n",
" <th>58513</th> <td> -0.0800</td> <td> 0.037</td> <td> -2.155</td> <td> 0.040</td> <td> -0.156</td> <td> -0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>64223</th> <td> -0.0533</td> <td> 0.040</td> <td> -1.339</td> <td> 0.191</td> <td> -0.135</td> <td> 0.028</td>\n",
"</tr>\n",
"<tr>\n",
" <th>79109</th> <td> 0.0152</td> <td> 0.033</td> <td> 0.457</td> <td> 0.651</td> <td> -0.053</td> <td> 0.083</td>\n",
"</tr>\n",
"<tr>\n",
" <th>84335</th> <td> -0.0249</td> <td> 0.037</td> <td> -0.670</td> <td> 0.508</td> <td> -0.101</td> <td> 0.051</td>\n",
"</tr>\n",
"<tr>\n",
" <th>117145</th> <td> 0.0240</td> <td> 0.025</td> <td> 0.961</td> <td> 0.344</td> <td> -0.027</td> <td> 0.075</td>\n",
"</tr>\n",
"<tr>\n",
" <th>196883</th> <td> -0.0494</td> <td> 0.015</td> <td> -3.296</td> <td> 0.003</td> <td> -0.080</td> <td> -0.019</td>\n",
"</tr>\n",
"<tr>\n",
" <th>253260</th> <td> 0.0409</td> <td> 0.046</td> <td> 0.893</td> <td> 0.379</td> <td> -0.053</td> <td> 0.135</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td> 0.520</td> <th> Durbin-Watson: </th> <td> 2.062</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.771</td> <th> Jarque-Bera (JB): </th> <td> 0.425</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.140</td> <th> Prob(JB): </th> <td> 0.808</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 2.980</td> <th> Cond. No. </th> <td>1.84e+03</td>\n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.84e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.999\n",
"Model: OLS Adj. R-squared: 0.995\n",
"Method: Least Squares F-statistic: 251.5\n",
"Date: Sat, 12 Jan 2019 Prob (F-statistic): 1.78e-29\n",
"Time: 18:17:00 Log-Likelihood: 364.21\n",
"No. Observations: 130 AIC: -526.4\n",
"Df Residuals: 29 BIC: -236.8\n",
"Df Model: 101 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"102 -0.0263 0.023 -1.162 0.255 -0.072 0.020\n",
"107 0.0055 0.013 0.414 0.682 -0.022 0.032\n",
"108 -0.0179 0.025 -0.717 0.479 -0.069 0.033\n",
"109 0.0112 0.019 0.600 0.553 -0.027 0.049\n",
"111 0.0037 0.013 0.282 0.780 -0.023 0.031\n",
"112 -0.0120 0.023 -0.527 0.602 -0.058 0.034\n",
"113 0.0143 0.014 1.031 0.311 -0.014 0.043\n",
"114 -0.0118 0.039 -0.302 0.765 -0.092 0.068\n",
"115 0.0008 0.036 0.022 0.983 -0.072 0.074\n",
"160 -0.0357 0.032 -1.114 0.274 -0.101 0.030\n",
"161 -0.0005 0.027 -0.017 0.987 -0.056 0.056\n",
"163 0.0254 0.031 0.828 0.415 -0.037 0.088\n",
"207 0.0099 0.031 0.323 0.749 -0.053 0.073\n",
"208 -0.0149 0.016 -0.959 0.346 -0.047 0.017\n",
"572 0.0064 0.024 0.267 0.791 -0.042 0.055\n",
"801 0.0163 0.040 0.408 0.686 -0.065 0.098\n",
"805 0.0394 0.032 1.232 0.228 -0.026 0.105\n",
"808 0.0136 0.037 0.367 0.716 -0.062 0.089\n",
"814 -0.0174 0.015 -1.129 0.268 -0.049 0.014\n",
"842 -0.0067 0.021 -0.327 0.746 -0.049 0.035\n",
"867 -0.0127 0.029 -0.442 0.661 -0.072 0.046\n",
"983 0.0013 0.015 0.089 0.930 -0.029 0.032\n",
"998 -0.0207 0.043 -0.482 0.633 -0.109 0.067\n",
"1026 -0.0120 0.014 -0.878 0.387 -0.040 0.016\n",
"1027 0.0094 0.025 0.382 0.705 -0.041 0.060\n",
"1147 -0.0039 0.035 -0.111 0.912 -0.075 0.068\n",
"1173 0.0050 0.041 0.121 0.905 -0.079 0.089\n",
"1175 0.0105 0.033 0.318 0.753 -0.057 0.078\n",
"1211 -0.0214 0.015 -1.443 0.160 -0.052 0.009\n",
"1213 0.0339 0.041 0.830 0.413 -0.050 0.117\n",
"1385 -0.0315 0.034 -0.915 0.367 -0.102 0.039\n",
"1445 0.0012 0.022 0.056 0.956 -0.044 0.046\n",
"1950 2.53e-05 0.011 0.002 0.998 -0.023 0.023\n",
"1956 0.0078 0.017 0.457 0.651 -0.027 0.043\n",
"2060 0.0024 0.044 0.054 0.958 -0.087 0.092\n",
"2308 -0.0160 0.020 -0.788 0.437 -0.057 0.025\n",
"2309 0.0524 0.019 2.699 0.011 0.013 0.092\n",
"2475 -0.0235 0.034 -0.688 0.497 -0.093 0.046\n",
"2549 -0.0339 0.028 -1.213 0.235 -0.091 0.023\n",
"2885 0.0448 0.041 1.094 0.283 -0.039 0.129\n",
"2931 0.0273 0.035 0.787 0.438 -0.044 0.098\n",
"3164 0.0046 0.006 0.796 0.433 -0.007 0.016\n",
"3265 -0.0047 0.023 -0.207 0.838 -0.051 0.042\n",
"3320 0.0023 0.023 0.100 0.921 -0.045 0.050\n",
"3709 0.0019 0.016 0.120 0.905 -0.030 0.034\n",
"3710 0.0056 0.017 0.323 0.749 -0.030 0.041\n",
"3845 0.0280 0.030 0.933 0.359 -0.033 0.090\n",
"4193 0.0034 0.016 0.206 0.838 -0.030 0.037\n",
"4303 -0.0149 0.020 -0.760 0.454 -0.055 0.025\n",
"4893 -0.0206 0.037 -0.560 0.579 -0.096 0.055\n",
"5136 0.0218 0.018 1.187 0.245 -0.016 0.059\n",
"5170 0.0066 0.041 0.160 0.874 -0.078 0.091\n",
"5290 -0.0134 0.039 -0.345 0.732 -0.093 0.066\n",
"5295 0.0044 0.022 0.196 0.846 -0.041 0.050\n",
"5335 0.0357 0.038 0.950 0.350 -0.041 0.113\n",
"5566 0.0261 0.033 0.804 0.428 -0.040 0.093\n",
"5567 -0.0081 0.014 -0.573 0.571 -0.037 0.021\n",
"5573 -0.0622 0.036 -1.748 0.091 -0.135 0.011\n",
"5575 -0.0046 0.018 -0.255 0.801 -0.042 0.032\n",
"5576 0.0045 0.022 0.205 0.839 -0.040 0.049\n",
"5577 -0.0068 0.012 -0.554 0.584 -0.032 0.018\n",
"5578 -0.0009 0.018 -0.047 0.963 -0.039 0.037\n",
"5580 -0.0330 0.019 -1.732 0.094 -0.072 0.006\n",
"5581 -0.0278 0.027 -1.015 0.319 -0.084 0.028\n",
"5582 0.0025 0.006 0.455 0.652 -0.009 0.014\n",
"5594 -0.0124 0.032 -0.386 0.702 -0.078 0.053\n",
"5595 -0.0193 0.024 -0.796 0.433 -0.069 0.030\n",
"5604 -0.0006 0.035 -0.018 0.986 -0.071 0.070\n",
"5605 0.0225 0.037 0.606 0.549 -0.053 0.098\n",
"5728 -0.0036 0.034 -0.106 0.917 -0.072 0.065\n",
"5894 0.0304 0.044 0.698 0.491 -0.059 0.120\n",
"6199 -0.0566 0.030 -1.915 0.065 -0.117 0.004\n",
"6456 0.0099 0.011 0.881 0.385 -0.013 0.033\n",
"6464 -0.0511 0.024 -2.113 0.043 -0.100 -0.002\n",
"6654 -0.0389 0.034 -1.147 0.261 -0.108 0.030\n",
"6714 0.0157 0.018 0.861 0.396 -0.022 0.053\n",
"6868 0.0148 0.031 0.477 0.637 -0.049 0.078\n",
"7249 0.0824 0.044 1.858 0.073 -0.008 0.173\n",
"7311 0.0085 0.031 0.270 0.789 -0.056 0.073\n",
"7529 -0.0009 0.039 -0.023 0.982 -0.082 0.080\n",
"8027 -0.0035 0.027 -0.133 0.895 -0.058 0.051\n",
"8038 0.0074 0.009 0.807 0.426 -0.011 0.026\n",
"9146 0.0232 0.033 0.706 0.486 -0.044 0.090\n",
"10000 -0.0273 0.024 -1.146 0.261 -0.076 0.021\n",
"10252 0.0124 0.015 0.815 0.422 -0.019 0.044\n",
"10253 -0.0064 0.016 -0.395 0.696 -0.040 0.027\n",
"10254 0.0475 0.034 1.379 0.178 -0.023 0.118\n",
"11140 0.0289 0.038 0.753 0.458 -0.050 0.108\n",
"23239 0.0138 0.026 0.539 0.594 -0.038 0.066\n",
"26018 0.0022 0.015 0.148 0.884 -0.029 0.033\n",
"29924 0.0047 0.033 0.143 0.887 -0.062 0.072\n",
"30011 0.0248 0.022 1.143 0.262 -0.020 0.069\n",
"55824 0.0005 0.020 0.025 0.980 -0.040 0.042\n",
"57761 -0.0018 0.013 -0.137 0.892 -0.029 0.025\n",
"58513 -0.0800 0.037 -2.155 0.040 -0.156 -0.004\n",
"64223 -0.0533 0.040 -1.339 0.191 -0.135 0.028\n",
"79109 0.0152 0.033 0.457 0.651 -0.053 0.083\n",
"84335 -0.0249 0.037 -0.670 0.508 -0.101 0.051\n",
"117145 0.0240 0.025 0.961 0.344 -0.027 0.075\n",
"196883 -0.0494 0.015 -3.296 0.003 -0.080 -0.019\n",
"253260 0.0409 0.046 0.893 0.379 -0.053 0.135\n",
"==============================================================================\n",
"Omnibus: 0.520 Durbin-Watson: 2.062\n",
"Prob(Omnibus): 0.771 Jarque-Bera (JB): 0.425\n",
"Skew: 0.140 Prob(JB): 0.808\n",
"Kurtosis: 2.980 Cond. No. 1.84e+03\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 1.84e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = PAADEEGFR\n",
"y = PredPAAD.detach().numpy()\n",
"\n",
"# Note the difference in argument order\n",
"model = sm.OLS(y, X).fit()\n",
"predictions = model.predict(X) # make the predictions by the model\n",
"\n",
"# Print out the statistics\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(array([False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False]), array([ 25.71654309, 68.88518558, 48.36811448, 55.88769998,\n",
" 78.77410576, 60.81762683, 31.41328896, 77.27234893,\n",
" 99.26457938, 27.69865112, 99.63968843, 41.88315265,\n",
" 75.66797196, 34.90617989, 79.8933991 , 69.30354072,\n",
" 23.00476997, 72.33422978, 27.07452713, 75.35095038,\n",
" 66.80529175, 93.88769455, 63.96140325, 39.08209479,\n",
" 71.23367866, 92.15867969, 91.37186718, 76.01882484,\n",
" 16.14111894, 41.75472905, 37.11689276, 96.5460688 ,\n",
" 100.82224759, 65.73660253, 96.71260657, 44.13402207,\n",
" 1.15992322, 50.19810301, 23.73852166, 28.57323444,\n",
" 44.22337651, 43.70695825, 84.62151046, 92.99084428,\n",
" 91.44853418, 75.6610565 , 36.23271223, 84.62829129,\n",
" 45.80441445, 58.5278806 , 24.72551896, 88.26889264,\n",
" 73.97259467, 85.44107636, 35.35726029, 43.22913637,\n",
" 57.65320828, 9.1996007 , 80.86879718, 84.77120041,\n",
" 58.95560148, 97.25785942, 9.48626631, 32.18986414,\n",
" 65.87683843, 70.92028156, 43.70345735, 99.56588916,\n",
" 55.4642251 , 92.57057856, 49.57156958, 6.60461621,\n",
" 38.91499157, 4.37977584, 26.3190754 , 40.00277853,\n",
" 64.33396747, 7.40933999, 79.70251399, 99.14239031,\n",
" 90.41462196, 43.03320452, 49.04616013, 26.37560835,\n",
" 42.58791361, 70.27675668, 18.01385415, 46.21681982,\n",
" 59.96442188, 89.24362686, 89.57925236, 26.51049056,\n",
" 99.00283406, 90.1284706 , 4.00007563, 19.29804105,\n",
" 65.75781376, 51.32626421, 34.78151666, 0.26216152,\n",
" 38.29707195]))\n"
]
}
],
"source": [
"print(bonferroni_correction(model.pvalues, alpha=0.05))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"listEGFR = LUADE.columns.intersection(lsEGFR)\n",
"LUADEEGFR = LUADE[listEGFR]\n",
"LUADMEGFR = LUADM[listEGFR]\n",
"LUADCEGFR = LUADC[listEGFR]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>y</td> <th> R-squared: </th> <td> 0.998</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.998</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 1895.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 12 Jan 2019</td> <th> Prob (F-statistic):</th> <td> 0.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>18:17:07</td> <th> Log-Likelihood: </th> <td> 1160.5</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 475</td> <th> AIC: </th> <td> -2119.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 374</td> <th> BIC: </th> <td> -1699.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 101</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>102</th> <td> 0.0028</td> <td> 0.003</td> <td> 0.825</td> <td> 0.410</td> <td> -0.004</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>107</th> <td> -0.0021</td> <td> 0.002</td> <td> -1.161</td> <td> 0.247</td> <td> -0.006</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>108</th> <td> 0.0013</td> <td> 0.003</td> <td> 0.412</td> <td> 0.681</td> <td> -0.005</td> <td> 0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>109</th> <td> 0.0013</td> <td> 0.003</td> <td> 0.524</td> <td> 0.600</td> <td> -0.004</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>111</th> <td> -0.0006</td> <td> 0.002</td> <td> -0.411</td> <td> 0.681</td> <td> -0.004</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>112</th> <td> 0.0020</td> <td> 0.003</td> <td> 0.771</td> <td> 0.441</td> <td> -0.003</td> <td> 0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>113</th> <td> -0.0034</td> <td> 0.002</td> <td> -1.652</td> <td> 0.099</td> <td> -0.007</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>114</th> <td> -0.0046</td> <td> 0.004</td> <td> -1.172</td> <td> 0.242</td> <td> -0.012</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>115</th> <td> 0.0020</td> <td> 0.003</td> <td> 0.798</td> <td> 0.426</td> <td> -0.003</td> <td> 0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>160</th> <td> 0.0059</td> <td> 0.005</td> <td> 1.221</td> <td> 0.223</td> <td> -0.004</td> <td> 0.015</td>\n",
"</tr>\n",
"<tr>\n",
" <th>161</th> <td> -0.0005</td> <td> 0.004</td> <td> -0.128</td> <td> 0.898</td> <td> -0.007</td> <td> 0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>163</th> <td> 0.0038</td> <td> 0.003</td> <td> 1.226</td> <td> 0.221</td> <td> -0.002</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>207</th> <td> -0.0066</td> <td> 0.004</td> <td> -1.705</td> <td> 0.089</td> <td> -0.014</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>208</th> <td> 0.0001</td> <td> 0.004</td> <td> 0.033</td> <td> 0.974</td> <td> -0.008</td> <td> 0.008</td>\n",
"</tr>\n",
"<tr>\n",
" <th>572</th> <td> -0.0035</td> <td> 0.003</td> <td> -1.009</td> <td> 0.314</td> <td> -0.010</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>801</th> <td> -0.0123</td> <td> 0.004</td> <td> -3.097</td> <td> 0.002</td> <td> -0.020</td> <td> -0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>805</th> <td> 0.0078</td> <td> 0.004</td> <td> 2.175</td> <td> 0.030</td> <td> 0.001</td> <td> 0.015</td>\n",
"</tr>\n",
"<tr>\n",
" <th>808</th> <td> 0.0009</td> <td> 0.005</td> <td> 0.193</td> <td> 0.847</td> <td> -0.008</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>814</th> <td> -0.0018</td> <td> 0.004</td> <td> -0.500</td> <td> 0.617</td> <td> -0.009</td> <td> 0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>842</th> <td> 0.0039</td> <td> 0.004</td> <td> 1.104</td> <td> 0.270</td> <td> -0.003</td> <td> 0.011</td>\n",
"</tr>\n",
"<tr>\n",
" <th>867</th> <td> 0.0006</td> <td> 0.004</td> <td> 0.139</td> <td> 0.890</td> <td> -0.008</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>983</th> <td> 0.0038</td> <td> 0.002</td> <td> 1.848</td> <td> 0.065</td> <td> -0.000</td> <td> 0.008</td>\n",
"</tr>\n",
"<tr>\n",
" <th>998</th> <td> 0.0167</td> <td> 0.005</td> <td> 3.572</td> <td> 0.000</td> <td> 0.008</td> <td> 0.026</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1026</th> <td> -0.0011</td> <td> 0.002</td> <td> -0.589</td> <td> 0.556</td> <td> -0.005</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1027</th> <td> 0.0030</td> <td> 0.003</td> <td> 1.039</td> <td> 0.300</td> <td> -0.003</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1147</th> <td> 0.0067</td> <td> 0.005</td> <td> 1.436</td> <td> 0.152</td> <td> -0.002</td> <td> 0.016</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1173</th> <td> 0.0047</td> <td> 0.004</td> <td> 1.070</td> <td> 0.285</td> <td> -0.004</td> <td> 0.013</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1175</th> <td> -0.0014</td> <td> 0.005</td> <td> -0.270</td> <td> 0.787</td> <td> -0.012</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1211</th> <td> 0.0088</td> <td> 0.003</td> <td> 2.973</td> <td> 0.003</td> <td> 0.003</td> <td> 0.015</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1213</th> <td> -0.0055</td> <td> 0.004</td> <td> -1.259</td> <td> 0.209</td> <td> -0.014</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1385</th> <td> -0.0124</td> <td> 0.005</td> <td> -2.316</td> <td> 0.021</td> <td> -0.023</td> <td> -0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1445</th> <td> -0.0057</td> <td> 0.004</td> <td> -1.319</td> <td> 0.188</td> <td> -0.014</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1950</th> <td> 0.0016</td> <td> 0.001</td> <td> 1.287</td> <td> 0.199</td> <td> -0.001</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>1956</th> <td> 0.0073</td> <td> 0.001</td> <td> 5.165</td> <td> 0.000</td> <td> 0.005</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2060</th> <td> -0.0115</td> <td> 0.005</td> <td> -2.353</td> <td> 0.019</td> <td> -0.021</td> <td> -0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2308</th> <td> 0.0014</td> <td> 0.003</td> <td> 0.494</td> <td> 0.621</td> <td> -0.004</td> <td> 0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2309</th> <td> 0.0060</td> <td> 0.003</td> <td> 1.801</td> <td> 0.072</td> <td> -0.001</td> <td> 0.013</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2475</th> <td> -0.0080</td> <td> 0.005</td> <td> -1.582</td> <td> 0.114</td> <td> -0.018</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2549</th> <td> -0.0035</td> <td> 0.003</td> <td> -1.021</td> <td> 0.308</td> <td> -0.010</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2885</th> <td> 0.0081</td> <td> 0.006</td> <td> 1.414</td> <td> 0.158</td> <td> -0.003</td> <td> 0.019</td>\n",
"</tr>\n",
"<tr>\n",
" <th>2931</th> <td> 0.0025</td> <td> 0.005</td> <td> 0.479</td> <td> 0.632</td> <td> -0.008</td> <td> 0.013</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3164</th> <td> 0.0004</td> <td> 0.001</td> <td> 0.398</td> <td> 0.691</td> <td> -0.002</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3265</th> <td> 0.0018</td> <td> 0.003</td> <td> 0.574</td> <td> 0.566</td> <td> -0.004</td> <td> 0.008</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3320</th> <td> 0.0070</td> <td> 0.003</td> <td> 2.097</td> <td> 0.037</td> <td> 0.000</td> <td> 0.014</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3709</th> <td> -0.0031</td> <td> 0.002</td> <td> -1.262</td> <td> 0.208</td> <td> -0.008</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3710</th> <td> 0.0024</td> <td> 0.002</td> <td> 1.164</td> <td> 0.245</td> <td> -0.002</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>3845</th> <td> -0.0020</td> <td> 0.002</td> <td> -0.814</td> <td> 0.416</td> <td> -0.007</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>4193</th> <td> 0.0026</td> <td> 0.002</td> <td> 1.360</td> <td> 0.175</td> <td> -0.001</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>4303</th> <td> -0.0058</td> <td> 0.003</td> <td> -2.072</td> <td> 0.039</td> <td> -0.011</td> <td> -0.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>4893</th> <td> -0.0029</td> <td> 0.003</td> <td> -0.855</td> <td> 0.393</td> <td> -0.010</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5136</th> <td> -0.0007</td> <td> 0.002</td> <td> -0.346</td> <td> 0.729</td> <td> -0.005</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5170</th> <td> -0.0020</td> <td> 0.005</td> <td> -0.365</td> <td> 0.715</td> <td> -0.013</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5290</th> <td> -0.0005</td> <td> 0.005</td> <td> -0.108</td> <td> 0.914</td> <td> -0.010</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5295</th> <td> 0.0006</td> <td> 0.003</td> <td> 0.179</td> <td> 0.858</td> <td> -0.006</td> <td> 0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5335</th> <td> 0.0037</td> <td> 0.003</td> <td> 1.104</td> <td> 0.270</td> <td> -0.003</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5566</th> <td> -0.0012</td> <td> 0.006</td> <td> -0.208</td> <td> 0.835</td> <td> -0.012</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5567</th> <td> -0.0006</td> <td> 0.002</td> <td> -0.311</td> <td> 0.756</td> <td> -0.004</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5573</th> <td> 0.0005</td> <td> 0.004</td> <td> 0.108</td> <td> 0.914</td> <td> -0.008</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5575</th> <td> 0.0013</td> <td> 0.002</td> <td> 0.627</td> <td> 0.531</td> <td> -0.003</td> <td> 0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5576</th> <td> -0.0134</td> <td> 0.003</td> <td> -3.897</td> <td> 0.000</td> <td> -0.020</td> <td> -0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5577</th> <td> 0.0011</td> <td> 0.001</td> <td> 0.742</td> <td> 0.459</td> <td> -0.002</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5578</th> <td> -0.0005</td> <td> 0.002</td> <td> -0.249</td> <td> 0.804</td> <td> -0.004</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5580</th> <td> 0.0077</td> <td> 0.003</td> <td> 2.264</td> <td> 0.024</td> <td> 0.001</td> <td> 0.014</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5581</th> <td> 0.0029</td> <td> 0.003</td> <td> 0.860</td> <td> 0.391</td> <td> -0.004</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5582</th> <td> 0.0045</td> <td> 0.004</td> <td> 1.147</td> <td> 0.252</td> <td> -0.003</td> <td> 0.012</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5594</th> <td> 0.0058</td> <td> 0.003</td> <td> 1.729</td> <td> 0.085</td> <td> -0.001</td> <td> 0.012</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5595</th> <td> -0.0001</td> <td> 0.003</td> <td> -0.038</td> <td> 0.970</td> <td> -0.006</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5604</th> <td> 0.0019</td> <td> 0.003</td> <td> 0.543</td> <td> 0.588</td> <td> -0.005</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5605</th> <td> 0.0131</td> <td> 0.004</td> <td> 2.988</td> <td> 0.003</td> <td> 0.004</td> <td> 0.022</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5728</th> <td> -0.0003</td> <td> 0.004</td> <td> -0.063</td> <td> 0.950</td> <td> -0.008</td> <td> 0.008</td>\n",
"</tr>\n",
"<tr>\n",
" <th>5894</th> <td> -0.0067</td> <td> 0.005</td> <td> -1.381</td> <td> 0.168</td> <td> -0.016</td> <td> 0.003</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6199</th> <td> 0.0016</td> <td> 0.004</td> <td> 0.425</td> <td> 0.671</td> <td> -0.006</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6456</th> <td> -0.0059</td> <td> 0.002</td> <td> -2.729</td> <td> 0.007</td> <td> -0.010</td> <td> -0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6464</th> <td> 0.0003</td> <td> 0.003</td> <td> 0.114</td> <td> 0.909</td> <td> -0.005</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6654</th> <td> 0.0022</td> <td> 0.005</td> <td> 0.410</td> <td> 0.682</td> <td> -0.008</td> <td> 0.013</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6714</th> <td> -0.0010</td> <td> 0.003</td> <td> -0.366</td> <td> 0.715</td> <td> -0.006</td> <td> 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>6868</th> <td> 0.0093</td> <td> 0.003</td> <td> 2.710</td> <td> 0.007</td> <td> 0.003</td> <td> 0.016</td>\n",
"</tr>\n",
"<tr>\n",
" <th>7249</th> <td> 0.0050</td> <td> 0.005</td> <td> 1.030</td> <td> 0.304</td> <td> -0.005</td> <td> 0.014</td>\n",
"</tr>\n",
"<tr>\n",
" <th>7311</th> <td> 0.0098</td> <td> 0.004</td> <td> 2.696</td> <td> 0.007</td> <td> 0.003</td> <td> 0.017</td>\n",
"</tr>\n",
"<tr>\n",
" <th>7529</th> <td> -0.0035</td> <td> 0.005</td> <td> -0.746</td> <td> 0.456</td> <td> -0.013</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>8027</th> <td> 0.0026</td> <td> 0.004</td> <td> 0.731</td> <td> 0.465</td> <td> -0.004</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>8038</th> <td> -0.0044</td> <td> 0.001</td> <td> -2.945</td> <td> 0.003</td> <td> -0.007</td> <td> -0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>9146</th> <td> 0.0017</td> <td> 0.005</td> <td> 0.339</td> <td> 0.734</td> <td> -0.008</td> <td> 0.011</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10000</th> <td> 0.0011</td> <td> 0.002</td> <td> 0.525</td> <td> 0.600</td> <td> -0.003</td> <td> 0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10252</th> <td> 0.0010</td> <td> 0.002</td> <td> 0.430</td> <td> 0.667</td> <td> -0.004</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10253</th> <td> -0.0067</td> <td> 0.002</td> <td> -2.954</td> <td> 0.003</td> <td> -0.011</td> <td> -0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>10254</th> <td> -0.0002</td> <td> 0.005</td> <td> -0.035</td> <td> 0.972</td> <td> -0.009</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>11140</th> <td> 0.0005</td> <td> 0.005</td> <td> 0.101</td> <td> 0.920</td> <td> -0.010</td> <td> 0.011</td>\n",
"</tr>\n",
"<tr>\n",
" <th>23239</th> <td> 0.0006</td> <td> 0.002</td> <td> 0.252</td> <td> 0.801</td> <td> -0.004</td> <td> 0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>26018</th> <td> 0.0047</td> <td> 0.002</td> <td> 2.039</td> <td> 0.042</td> <td> 0.000</td> <td> 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>29924</th> <td> 0.0014</td> <td> 0.004</td> <td> 0.323</td> <td> 0.747</td> <td> -0.007</td> <td> 0.010</td>\n",
"</tr>\n",
"<tr>\n",
" <th>30011</th> <td> -0.0027</td> <td> 0.002</td> <td> -1.093</td> <td> 0.275</td> <td> -0.007</td> <td> 0.002</td>\n",
"</tr>\n",
"<tr>\n",
" <th>55824</th> <td> 0.0011</td> <td> 0.002</td> <td> 0.521</td> <td> 0.602</td> <td> -0.003</td> <td> 0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>57761</th> <td> -0.0023</td> <td> 0.002</td> <td> -1.336</td> <td> 0.182</td> <td> -0.006</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>58513</th> <td> -0.0008</td> <td> 0.005</td> <td> -0.177</td> <td> 0.860</td> <td> -0.010</td> <td> 0.008</td>\n",
"</tr>\n",
"<tr>\n",
" <th>64223</th> <td> 0.0029</td> <td> 0.005</td> <td> 0.623</td> <td> 0.534</td> <td> -0.006</td> <td> 0.012</td>\n",
"</tr>\n",
"<tr>\n",
" <th>79109</th> <td> -0.0021</td> <td> 0.004</td> <td> -0.504</td> <td> 0.615</td> <td> -0.010</td> <td> 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>84335</th> <td> -0.0087</td> <td> 0.005</td> <td> -1.845</td> <td> 0.066</td> <td> -0.018</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>117145</th> <td> 0.0064</td> <td> 0.003</td> <td> 2.461</td> <td> 0.014</td> <td> 0.001</td> <td> 0.012</td>\n",
"</tr>\n",
"<tr>\n",
" <th>196883</th> <td> -0.0042</td> <td> 0.003</td> <td> -1.559</td> <td> 0.120</td> <td> -0.010</td> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>253260</th> <td> 0.0034</td> <td> 0.003</td> <td> 1.329</td> <td> 0.185</td> <td> -0.002</td> <td> 0.009</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>43.558</td> <th> Durbin-Watson: </th> <td> 2.088</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 91.380</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.522</td> <th> Prob(JB): </th> <td>1.44e-20</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 4.878</td> <th> Cond. No. </th> <td> 366.</td>\n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.998\n",
"Model: OLS Adj. R-squared: 0.998\n",
"Method: Least Squares F-statistic: 1895.\n",
"Date: Sat, 12 Jan 2019 Prob (F-statistic): 0.00\n",
"Time: 18:17:07 Log-Likelihood: 1160.5\n",
"No. Observations: 475 AIC: -2119.\n",
"Df Residuals: 374 BIC: -1699.\n",
"Df Model: 101 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"102 0.0028 0.003 0.825 0.410 -0.004 0.010\n",
"107 -0.0021 0.002 -1.161 0.247 -0.006 0.001\n",
"108 0.0013 0.003 0.412 0.681 -0.005 0.007\n",
"109 0.0013 0.003 0.524 0.600 -0.004 0.006\n",
"111 -0.0006 0.002 -0.411 0.681 -0.004 0.002\n",
"112 0.0020 0.003 0.771 0.441 -0.003 0.007\n",
"113 -0.0034 0.002 -1.652 0.099 -0.007 0.001\n",
"114 -0.0046 0.004 -1.172 0.242 -0.012 0.003\n",
"115 0.0020 0.003 0.798 0.426 -0.003 0.007\n",
"160 0.0059 0.005 1.221 0.223 -0.004 0.015\n",
"161 -0.0005 0.004 -0.128 0.898 -0.007 0.007\n",
"163 0.0038 0.003 1.226 0.221 -0.002 0.010\n",
"207 -0.0066 0.004 -1.705 0.089 -0.014 0.001\n",
"208 0.0001 0.004 0.033 0.974 -0.008 0.008\n",
"572 -0.0035 0.003 -1.009 0.314 -0.010 0.003\n",
"801 -0.0123 0.004 -3.097 0.002 -0.020 -0.004\n",
"805 0.0078 0.004 2.175 0.030 0.001 0.015\n",
"808 0.0009 0.005 0.193 0.847 -0.008 0.010\n",
"814 -0.0018 0.004 -0.500 0.617 -0.009 0.005\n",
"842 0.0039 0.004 1.104 0.270 -0.003 0.011\n",
"867 0.0006 0.004 0.139 0.890 -0.008 0.009\n",
"983 0.0038 0.002 1.848 0.065 -0.000 0.008\n",
"998 0.0167 0.005 3.572 0.000 0.008 0.026\n",
"1026 -0.0011 0.002 -0.589 0.556 -0.005 0.003\n",
"1027 0.0030 0.003 1.039 0.300 -0.003 0.009\n",
"1147 0.0067 0.005 1.436 0.152 -0.002 0.016\n",
"1173 0.0047 0.004 1.070 0.285 -0.004 0.013\n",
"1175 -0.0014 0.005 -0.270 0.787 -0.012 0.009\n",
"1211 0.0088 0.003 2.973 0.003 0.003 0.015\n",
"1213 -0.0055 0.004 -1.259 0.209 -0.014 0.003\n",
"1385 -0.0124 0.005 -2.316 0.021 -0.023 -0.002\n",
"1445 -0.0057 0.004 -1.319 0.188 -0.014 0.003\n",
"1950 0.0016 0.001 1.287 0.199 -0.001 0.004\n",
"1956 0.0073 0.001 5.165 0.000 0.005 0.010\n",
"2060 -0.0115 0.005 -2.353 0.019 -0.021 -0.002\n",
"2308 0.0014 0.003 0.494 0.621 -0.004 0.007\n",
"2309 0.0060 0.003 1.801 0.072 -0.001 0.013\n",
"2475 -0.0080 0.005 -1.582 0.114 -0.018 0.002\n",
"2549 -0.0035 0.003 -1.021 0.308 -0.010 0.003\n",
"2885 0.0081 0.006 1.414 0.158 -0.003 0.019\n",
"2931 0.0025 0.005 0.479 0.632 -0.008 0.013\n",
"3164 0.0004 0.001 0.398 0.691 -0.002 0.002\n",
"3265 0.0018 0.003 0.574 0.566 -0.004 0.008\n",
"3320 0.0070 0.003 2.097 0.037 0.000 0.014\n",
"3709 -0.0031 0.002 -1.262 0.208 -0.008 0.002\n",
"3710 0.0024 0.002 1.164 0.245 -0.002 0.006\n",
"3845 -0.0020 0.002 -0.814 0.416 -0.007 0.003\n",
"4193 0.0026 0.002 1.360 0.175 -0.001 0.006\n",
"4303 -0.0058 0.003 -2.072 0.039 -0.011 -0.000\n",
"4893 -0.0029 0.003 -0.855 0.393 -0.010 0.004\n",
"5136 -0.0007 0.002 -0.346 0.729 -0.005 0.003\n",
"5170 -0.0020 0.005 -0.365 0.715 -0.013 0.009\n",
"5290 -0.0005 0.005 -0.108 0.914 -0.010 0.009\n",
"5295 0.0006 0.003 0.179 0.858 -0.006 0.007\n",
"5335 0.0037 0.003 1.104 0.270 -0.003 0.010\n",
"5566 -0.0012 0.006 -0.208 0.835 -0.012 0.010\n",
"5567 -0.0006 0.002 -0.311 0.756 -0.004 0.003\n",
"5573 0.0005 0.004 0.108 0.914 -0.008 0.009\n",
"5575 0.0013 0.002 0.627 0.531 -0.003 0.005\n",
"5576 -0.0134 0.003 -3.897 0.000 -0.020 -0.007\n",
"5577 0.0011 0.001 0.742 0.459 -0.002 0.004\n",
"5578 -0.0005 0.002 -0.249 0.804 -0.004 0.003\n",
"5580 0.0077 0.003 2.264 0.024 0.001 0.014\n",
"5581 0.0029 0.003 0.860 0.391 -0.004 0.009\n",
"5582 0.0045 0.004 1.147 0.252 -0.003 0.012\n",
"5594 0.0058 0.003 1.729 0.085 -0.001 0.012\n",
"5595 -0.0001 0.003 -0.038 0.970 -0.006 0.006\n",
"5604 0.0019 0.003 0.543 0.588 -0.005 0.009\n",
"5605 0.0131 0.004 2.988 0.003 0.004 0.022\n",
"5728 -0.0003 0.004 -0.063 0.950 -0.008 0.008\n",
"5894 -0.0067 0.005 -1.381 0.168 -0.016 0.003\n",
"6199 0.0016 0.004 0.425 0.671 -0.006 0.009\n",
"6456 -0.0059 0.002 -2.729 0.007 -0.010 -0.002\n",
"6464 0.0003 0.003 0.114 0.909 -0.005 0.006\n",
"6654 0.0022 0.005 0.410 0.682 -0.008 0.013\n",
"6714 -0.0010 0.003 -0.366 0.715 -0.006 0.004\n",
"6868 0.0093 0.003 2.710 0.007 0.003 0.016\n",
"7249 0.0050 0.005 1.030 0.304 -0.005 0.014\n",
"7311 0.0098 0.004 2.696 0.007 0.003 0.017\n",
"7529 -0.0035 0.005 -0.746 0.456 -0.013 0.006\n",
"8027 0.0026 0.004 0.731 0.465 -0.004 0.010\n",
"8038 -0.0044 0.001 -2.945 0.003 -0.007 -0.001\n",
"9146 0.0017 0.005 0.339 0.734 -0.008 0.011\n",
"10000 0.0011 0.002 0.525 0.600 -0.003 0.005\n",
"10252 0.0010 0.002 0.430 0.667 -0.004 0.006\n",
"10253 -0.0067 0.002 -2.954 0.003 -0.011 -0.002\n",
"10254 -0.0002 0.005 -0.035 0.972 -0.009 0.009\n",
"11140 0.0005 0.005 0.101 0.920 -0.010 0.011\n",
"23239 0.0006 0.002 0.252 0.801 -0.004 0.005\n",
"26018 0.0047 0.002 2.039 0.042 0.000 0.009\n",
"29924 0.0014 0.004 0.323 0.747 -0.007 0.010\n",
"30011 -0.0027 0.002 -1.093 0.275 -0.007 0.002\n",
"55824 0.0011 0.002 0.521 0.602 -0.003 0.005\n",
"57761 -0.0023 0.002 -1.336 0.182 -0.006 0.001\n",
"58513 -0.0008 0.005 -0.177 0.860 -0.010 0.008\n",
"64223 0.0029 0.005 0.623 0.534 -0.006 0.012\n",
"79109 -0.0021 0.004 -0.504 0.615 -0.010 0.006\n",
"84335 -0.0087 0.005 -1.845 0.066 -0.018 0.001\n",
"117145 0.0064 0.003 2.461 0.014 0.001 0.012\n",
"196883 -0.0042 0.003 -1.559 0.120 -0.010 0.001\n",
"253260 0.0034 0.003 1.329 0.185 -0.002 0.009\n",
"==============================================================================\n",
"Omnibus: 43.558 Durbin-Watson: 2.088\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 91.380\n",
"Skew: 0.522 Prob(JB): 1.44e-20\n",
"Kurtosis: 4.878 Cond. No. 366.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = LUADEEGFR\n",
"y = PredLUAD.detach().numpy()\n",
"\n",
"# Note the difference in argument order\n",
"model = sm.OLS(y, X).fit()\n",
"predictions = model.predict(X) # make the predictions by the model\n",
"\n",
"# Print out the statistics\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(array([False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, True, False, False, False, False,\n",
" False, False, False, False, False, False, True, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, True, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False]), array([4.14015235e+01, 2.48985067e+01, 6.87655871e+01, 6.06407957e+01,\n",
" 6.88273009e+01, 4.45728983e+01, 1.00290905e+01, 2.44236844e+01,\n",
" 4.29890891e+01, 2.25237889e+01, 9.06980328e+01, 2.23338545e+01,\n",
" 8.98385247e+00, 9.83573120e+01, 3.16968699e+01, 2.12121420e-01,\n",
" 3.05570011e+00, 8.55424721e+01, 6.23448783e+01, 2.72870372e+01,\n",
" 8.98633410e+01, 6.60488342e+00, 4.04124456e-02, 5.61667950e+01,\n",
" 3.02544778e+01, 1.53460327e+01, 2.88183240e+01, 7.95045115e+01,\n",
" 3.17136654e-01, 2.10836580e+01, 2.13318505e+00, 1.89824840e+01,\n",
" 2.01032828e+01, 3.95510978e-05, 1.93330041e+00, 6.27577203e+01,\n",
" 7.31548083e+00, 1.15576665e+01, 3.10839496e+01, 1.59815035e+01,\n",
" 6.38765252e+01, 6.98100511e+01, 5.71721313e+01, 3.70384856e+00,\n",
" 2.09784933e+01, 2.47574544e+01, 4.20584879e+01, 1.76263456e+01,\n",
" 3.93798091e+00, 3.97200959e+01, 7.36691578e+01, 7.22146703e+01,\n",
" 9.23540721e+01, 8.66810315e+01, 2.73081316e+01, 8.43743365e+01,\n",
" 7.63168572e+01, 9.22877149e+01, 5.36144076e+01, 1.16774259e-02,\n",
" 4.63226333e+01, 8.11664996e+01, 2.43831571e+00, 3.94516497e+01,\n",
" 2.54481798e+01, 8.54272406e+00, 9.79257383e+01, 5.93442425e+01,\n",
" 3.02023351e-01, 9.59097960e+01, 1.69827761e+01, 6.77638632e+01,\n",
" 6.71173946e-01, 9.18373294e+01, 6.88643524e+01, 7.21909359e+01,\n",
" 7.11695781e-01, 3.06722593e+01, 7.40430399e-01, 4.60684526e+01,\n",
" 4.69928395e+01, 3.46394030e-01, 7.41824356e+01, 6.06084806e+01,\n",
" 6.73821895e+01, 3.37003178e-01, 9.81702523e+01, 9.29017562e+01,\n",
" 8.09271567e+01, 4.26041583e+00, 7.54604810e+01, 2.77902496e+01,\n",
" 6.08401844e+01, 1.84323634e+01, 8.68103068e+01, 5.38850645e+01,\n",
" 6.21034660e+01, 6.64966356e+00, 1.44584009e+00, 1.21155444e+01,\n",
" 1.86469613e+01]))\n"
]
}
],
"source": [
"print(bonferroni_correction(model.pvalues, alpha=0.05))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Datasets
Models
