randomForestSRC (version 2.4.1)

nutrigenomic: Nutrigenomic Study

Description

Study the effects of five diet treatments on 21 liver lipids and 120 hepatic gene expression in wild-type and PPAR-alpha deficient mice. We use a multivariate mixed random forest analysis by regressing gene expression, diet and genotype (the x-variables) on lipid expressions (the multivariate y-responses).

References

Martin P.G. et al. (2007). Novel aspects of PPAR-alpha-mediated regulation of lipid and xenobiotic metabolism revealed through a nutrigenomic study. Hepatology, 45(3), 767--777.

Examples

## Not run: 
# ## ------------------------------------------------------------
# ## multivariate mixed forests
# ## lipids used as the multivariate y-responses
# ## ------------------------------------------------------------
# 
# ## load the data
# data(nutrigenomic, package = "randomForestSRC")
# 
# ## nice wrapper for making multivariate formula
# mvrfsrc.f <- function(ynames, dat) {
#   as.formula(paste("Multivar(", paste(ynames, collapse = ","),paste(") ~ ."), sep = ""))
# }
# 
# ## multivariate mixed forest call
# mv.obj <- rfsrc(mvrfsrc.f(colnames(nutrigenomic$lipids)),
#             data.frame(do.call(cbind, nutrigenomic)),
#             importance=TRUE, nsplit = 10)
# 
# 
# ## ------------------------------------------------------------
# ## nice wrappers to extract standardized performance values
# ## works for all forests - including multivariate forests
# ## ------------------------------------------------------------
# 
# ## pull the standardized error from a forest object
# get.error <- function(obj) {
#   100 * c(sapply(obj$yvar.names, function(nn) {
#     o.coerce <- randomForestSRC:::coerce.multivariate(obj, nn)
#     if (o.coerce$family == "class") {
#       tail(o.coerce$err.rate[, 1], 1)
#     }
#     else {
#       tail(o.coerce$err.rate, 1) / var(o.coerce$yvar, na.rm = TRUE)
#     }
#   }))
# }
# 
# ## pull the standardized VIMP from a forest object
# get.vimp <- function(obj) {
#   vimp <- 100 * do.call(cbind, lapply(obj$yvar.names, function(nn) {
#     o.coerce <- randomForestSRC:::coerce.multivariate(obj, nn)
#     if (o.coerce$family == "class") {
#       o.coerce$importance[, 1]
#     }
#     else {
#       o.coerce$importance / var(o.coerce$yvar, na.rm = TRUE)
#     }
#   }))
#   colnames(vimp) <- obj$yvar.names
#   vimp
# }
# 
# ## ------------------------------------------------------------
# ## plot the standarized performance and VIMP values
# ## ------------------------------------------------------------
# 
# ## acquire the error rate for each of the 21-coordinates 
# ## standardize to allow for comparison across coordinates
# serr <- get.error(mv.obj)
# 
# ## acquire standardized VIMP 
# svimp <- get.vimp(mv.obj)
# 
# par(mfrow = c(1,2))
# plot(serr, xlab = "Lipids", ylab = "Standardized Performance")
# matplot(svimp, xlab = "Genes/Diet/Genotype", ylab = "Standardized VIMP")
# 
# 
# ## End(Not run)