--- a
+++ b/data-raw/script_timeOmics.simdata.R
@@ -0,0 +1,166 @@
+library(tidyverse)
+library(lmms)
+
+# RAW DATA
+c1 <- c(0, 0.5,1,1.1,1.2,1.8,2.5,5,9)
+c3 <-  c(-2,4, 8, 6,4.5,4,3.9, 3, 1)
+c2 <- -c1
+c4 <- -c3
+
+list(c1,c2,c3,c4)
+
+
+c1.0 <-  c1
+c1.1 <-  c1*1.5
+c1.2 <- (c1-0.3)*0.3
+c1.3 <- (c1 +0.5)*0.8
+c1.4 <- (c1-1)*1.1
+
+c2.0 <-  c2
+c2.1 <-  c2*1.5
+c2.2 <- (c2-0.3)*0.3
+c2.3 <- (c2 +0.5)*0.8
+c2.4 <- (c2-1)*1.1
+
+c3.0 <-  c3
+c3.1 <-  c3*1.5
+c3.2 <- (c3-0.3)*0.3
+c3.3 <- (c3 +0.5)*0.8
+c3.4 <- (c3-1)*1.1
+
+c4.0 <-  c4
+c4.1 <-  c4*1.5
+c4.2 <- (c4-0.3)*0.3
+c4.3 <- (c4 +0.5)*0.8
+c4.4 <- (c4-1)*1.4
+
+# noise 
+c0 <- c(0,0.1,0.05,0,0,0.1,0,0.05,0.1) +1
+sd(c0)/mean(c0)
+
+data <- list(c1.0,c1.1,c1.2,c1.3,c1.4,c2.0,c2.1,c2.2,c2.3,c2.4,c3.0,c3.1,c3.2,c3.3,c3.4,c4.0,c4.1,c4.2,c4.3,c4.4, c0)
+names(data) <- c("c1.0", "c1.1", "c1.2", "c1.3", "c1.4",
+                 "c2.0", "c2.1", "c2.2", "c2.3", "c2.4",
+                 "c3.0", "c3.1", "c3.2", "c3.3", "c3.4",
+                 "c4.0", "c4.1", "c4.2", "c4.3", "c4.4",
+                 "c0")
+raw.data <- as.data.frame(data)setwd("~")
+load("~/Downloads/lmms.out2.rda")
+data.gather <- raw.data %>% rownames_to_column("time") %>%
+    mutate(time = as.numeric(time)) %>%
+    gather(sample, value, -time)
+
+# SIM DATA
+sd <- 0.3
+N_Ind <- 5
+set.seed(123)
+
+tmp <- data.gather
+for(ind in 1:N_Ind){
+    vect <- vector(length = nrow(tmp), mode = "numeric")
+    for(x in 1:length(vect)){
+        vect[x] <- rnorm(1, mean = tmp$value[x], sd = sd)
+    }
+    name.c <- names(tmp)
+    tmp <- data.frame(tmp, vect)
+    colnames(tmp) <- c(name.c, LETTERS[ind])
+}
+
+sim.data <- tmp %>% dplyr::select(-c(value)) %>%
+    gather(ind, value, -c(sample, time))%>%
+    mutate(ind = c(paste0(ind, "_", time))) %>% dplyr::select(-time) %>%
+    spread(ind, value) %>% column_to_rownames("sample") %>% t
+
+# modelled data
+time <- rownames(sim.data) %>% str_split("_") %>% map_chr(~.x[2]) %>% as.numeric()
+sampleID <- rownames(sim.data)
+lmms.out <- lmmSpline(data = sim.data, time = time, sampleID = sampleID, keepModels = TRUE)
+
+# build new s4
+#setClass('lmms',slots=c(basis="character", knots="numeric",errorMolecules="character"))
+setClass("lmmspline",slots= c(predSpline="data.frame", modelsUsed="numeric",models="list",derivative='logical', basis="character", knots="numeric",errorMolecules="character"))
+
+lmms.out2 <- new("lmmspline", 
+                 predSpline = lmms.out@predSpline,
+                 modelsUsed = lmms.out@modelsUsed,
+                 models = lmms.out@models,
+                 derivative = lmms.out@derivative, 
+                 basis = lmms.out@basis, 
+                 knots = lmms.out@knots, 
+                 errorMolecules = lmms.out@errorMolecules )
+
+save(lmms.out2, file = "~/Downloads/lmms.out2.rda")
+
+modelled.data <-  as.data.frame(t(lmms.out2@predSpline))
+
+detach("package:lmms", unload=TRUE)
+
+timeOmics.simdata <- list(rawdata = raw.data, sim = sim.data,
+                          modelled = modelled.data[,-c0], 
+                          lmms.output = lmms.out2,
+                          time = time)
+
+library(lmms)
+# Y same as data but increase noise
+sd <- 0.5
+N_Ind <- 4
+set.seed(123)
+
+tmp <- data.gather %>% filter(time %in% c(1,2,3,5,7,9))
+for(ind in 1:N_Ind){
+    vect <- vector(length = nrow(tmp), mode = "numeric")
+    for(x in 1:length(vect)){
+        vect[x] <- rnorm(1, mean = tmp$value[x], sd = sd)
+    }
+    name.c <- names(tmp)
+    tmp <- data.frame(tmp, vect)
+    colnames(tmp) <- c(name.c, LETTERS[ind])
+}
+
+Y <- tmp %>% dplyr::select(-c(value)) %>%
+    gather(ind, value, -c(sample, time))%>%
+    mutate(ind = c(paste0(ind, "_", time))) %>% dplyr::select(-time) %>%
+    spread(ind, value) %>% column_to_rownames("sample") %>% t
+
+time.Y <- rownames(Y) %>% str_split("_") %>% map_chr(~.x[2]) %>% as.numeric()
+sampleID.Y <- rownames(Y)
+lmms.Y <- lmmSpline(data = Y, time = time.Y, sampleID = sampleID.Y, keepModels = TRUE, 
+                          timePredict = 1:9)
+modelled.Y <- lmms.Y@predSpline %>% t %>% as.data.frame()
+colnames(modelled.Y) <- paste0("Y_", seq_along(colnames(modelled.Y)))
+
+timeOmics.simdata[["Y"]] <- modelled.Y
+
+# Z
+# Y same as data but increase noise
+sd <- 1
+N_Ind <- 4
+set.seed(123)
+
+tmp <- data.gather %>% filter(time %in% c(1,3,4,5,8,9))
+for(ind in 1:N_Ind){
+    vect <- vector(length = nrow(tmp), mode = "numeric")
+    for(x in 1:length(vect)){
+        vect[x] <- rnorm(1, mean = tmp$value[x], sd = sd)
+    }
+    name.c <- names(tmp)
+    tmp <- data.frame(tmp, vect)
+    colnames(tmp) <- c(name.c, LETTERS[ind])
+}
+
+Z <- tmp %>% dplyr::select(-c(value)) %>%
+    gather(ind, value, -c(sample, time))%>%
+    mutate(ind = c(paste0(ind, "_", time))) %>% dplyr::select(-time) %>%
+    spread(ind, value) %>% column_to_rownames("sample") %>% t
+
+time.Z <- rownames(Z) %>% str_split("_") %>% map_chr(~.x[2]) %>% as.numeric()
+sampleID.Z <- rownames(Z)
+lmms.Z <- lmms::lmmSpline(data = Z, time = time.Z, sampleID = sampleID.Z, keepModels = TRUE, 
+                          timePredict = 1:9)
+modelled.Z <- lmms.Z@predSpline %>% t %>% as.data.frame()
+colnames(modelled.Z) <- paste0("Z_", seq_along(colnames(modelled.Z)))
+
+timeOmics.simdata[["Z"]] <- modelled.Z
+
+usethis::use_data(timeOmics.simdata, overwrite = TRUE)
+#save(timeOmics.simdata, file = "./data/timeOmics.simdata.rda", compress = "gzip", ascii = FALSE)