# $Id$
### Linear statistic with expectation and covariance
setClass(Class = "LinStatExpectCovar",
representation = representation(
linearstatistic = "numeric",
expcovinf = "ExpectCovarInfluence"
),
contains = "ExpectCovar"
)
### Memory for C_svd
setClass(Class = "svd_mem",
representation = representation(
method = "character",
jobu = "character",
jobv = "character",
u = "matrix",
v = "matrix",
s = "numeric",
p = "integer"
)
)
### with Moore-Penrose inverse of the covariance matrix
setClass(Class = "LinStatExpectCovarMPinv",
representation = representation(
MPinv = "matrix",
rank = "numeric",
svdmem = "svd_mem"
),
contains = "LinStatExpectCovar"
)
################ Memory Classes #####################
setClass(Class = "TreeFitMemory",
representation = representation(
expcovinf = "ExpectCovarInfluence",
expcovinfss = "ExpectCovarInfluence",
linexpcov2sample = "LinStatExpectCovar",
weights = "numeric",
varmemory = "list",
dontuse = "logical",
dontusetmp = "logical",
splitstatistics = "numeric"
),
validity = function(object) {
ni <- length(dontuse)
length(varmemory) == ni && length(dontusetmp) == ni
}
)
############## Tree Classes ######################
setClassUnion("df_OR_list", c("data.frame", "list"))
setClass(Class = "VariableControl",
representation = representation(
teststat = "factor",
pvalue = "logical",
tol = "numeric",
maxpts = "integer",
abseps = "numeric",
releps = "numeric"
),
prototype = list(
teststat = factor("max", levels = c("max", "quad")),
pvalue = as.logical(TRUE),
tol = as.double(1e-10),
maxpts = as.integer(25000),
abseps = as.double(1e-4),
releps = as.double(0.0)
)
)
setClass(Class = "SplitControl",
representation = representation(
minprob = "numeric",
minsplit = "numeric",
minbucket = "numeric",
tol = "numeric",
maxsurrogate = "integer"
),
prototype = list(minprob = as.double(0.01),
minsplit = as.double(20),
minbucket = as.double(7),
tol = as.double(1e-10),
maxsurrogate = as.integer(0)
),
validity = function(object) {
if (any(c(object@minsplit, object@minbucket,
object@tol, object@maxsurrogate) < 0)) {
warning("no negative values allowed in objects of class ",
sQuote("SplitControl"))
return(FALSE)
}
if (object@minprob < 0.01 || object@minprob > 0.99) {
warning(sQuote("minprob"), " must be in (0.01, 0.99)")
return(FALSE)
}
return(TRUE)
}
)
setClass(Class = "GlobalTestControl",
representation = representation(
testtype = "factor",
nresample = "integer",
randomsplits = "logical",
mtry = "integer",
mincriterion = "numeric"
),
prototype = list(
testtype = factor("Bonferroni",
levels = c("Bonferroni", "MonteCarlo", "Aggregated",
"Univariate", "Teststatistic")),
nresample = as.integer(9999),
randomsplits = as.logical(FALSE),
mtry = as.integer(0),
mincriterion = as.double(0.95)
),
validity = function(object) {
if (object@mincriterion < 0) {
warning(sQuote("mincriterion"), " must not be negative")
return(FALSE)
}
if (any(object@mtry < 0)) {
warning(sQuote("mtry"), " must be positive")
return(FALSE)
}
if (object@nresample < 100) {
warning(sQuote("nresample"), " must be larger than 100")
return(FALSE)
}
return(TRUE)
},
)
setClass(Class = "TreeGrowControl",
representation = representation(
stump = "logical",
varOnce = "logical",
maxdepth = "integer",
savesplitstats = "logical"
),
prototype = list(stump = as.logical(FALSE),
varOnce = as.logical(FALSE),
maxdepth = as.integer(0),
savesplitstats = as.logical(TRUE)),
validity = function(object) {
if (object@maxdepth < 0) {
warning(sQuote("maxdepth"), " must be positive")
return(FALSE)
}
return(TRUE)
}
)
setClass(Class = "TreeControl",
representation = representation(
varctrl = "VariableControl",
splitctrl = "SplitControl",
gtctrl = "GlobalTestControl",
tgctrl = "TreeGrowControl"
),
prototype = list(varctrl = new("VariableControl"),
splitctrl = new("SplitControl"),
gtctrl = new("GlobalTestControl"),
tgctrl = new("TreeGrowControl")
),
validity = function(object) {
(validObject(object@varctrl) &&
validObject(object@splitctrl)) &&
(validObject(object@gtctrl) &&
validObject(object@tgctrl))
}
)
setClass(Class = "ForestControl",
representation = representation(
ntree = "integer",
replace = "logical",
fraction = "numeric",
trace = "logical",
dropcriterion = "logical",
compress = "function",
expand = "function"),
contains = "TreeControl",
validity = function(object) {
if (object@ntree < 1) {
warning(sQuote("ntree"), " must be equal or greater 1")
return(FALSE)
}
if (object@fraction < 0.01 || object@fraction > 0.99) {
warning(sQuote("fraction"), " must be in (0.01, 0.99)")
return(FALSE)
}
return(TRUE)
}
)
setClass(Class = "VariableFrame",
representation = representation(
variables = "df_OR_list",
transformations = "list",
is_nominal = "logical",
is_ordinal = "logical",
is_censored = "logical",
ordering = "list",
levels = "list",
scores = "list",
has_missings = "logical",
whichNA = "list",
nobs = "integer",
ninputs = "integer")
)
setClass(Class = "ResponseFrame",
representation = representation(
test_trafo = "matrix",
predict_trafo = "matrix"
), contains = "VariableFrame"
)
setClass(Class = "LearningSample",
representation = representation(
responses = "ResponseFrame",
inputs = "VariableFrame",
weights = "numeric",
nobs = "integer",
ninputs = "integer"
)
)
setClass(Class = "LearningSampleFormula",
representation = representation(
menv = "ModelEnv"
), contains = "LearningSample"
)
### the tree structure itself is a list,
### and we need to make sure that the tree slot excepts
### the S3 classes.
setClass(Class = "SplittingNode", contains = "list")
setClass(Class = "TerminalNode", contains = "list")
setClass(Class = "TerminalModelNode", contains = "list")
setClass(Class = "orderedSplit", contains = "list")
setClass(Class = "nominalSplit", contains = "list")
### and we don't want to see warnings that class `Surv'
### (S3 method in `survival') is unknown
setClass(Class = "Surv", contains = "list")
### A class for partitions induced by recursive binary splits
setClass(Class = "BinaryTreePartition",
representation = representation(
tree = "list", # the basic tree structure as (named or
# unnamed) list
where = "integer", # the nodeID of the observations in the
# learning sample
weights = "numeric" # the weights in the root node
),
)
### A class for binary trees
setClass(Class = "BinaryTree",
representation = representation(
data = "ModelEnv",
responses = "VariableFrame", # a list of response `variables'
# for computing predictions
cond_distr_response = "function", # predict distribtion
predict_response = "function", # predict responses
prediction_weights = "function", # prediction weights
get_where = "function", # node numbers
update = "function" # update weights
),
contains = "BinaryTreePartition"
)
### A class for random forest
setClass(Class = "RandomForest",
representation = representation(
ensemble = "list",
where = "list",
weights = "list",
initweights = "numeric",
data = "ModelEnv",
responses = "VariableFrame", # a list of response `variables'
# for computing predictions
cond_distr_response = "function", # predict distribtion
predict_response = "function", # predict responses
prediction_weights = "function", # prediction weights
get_where = "function", # node numbers
update = "function", # update weights
expand = "function" # function to invert compress operation
)
)
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