r_prepare_data: Prepare data for regression routines

In eclust: Environment Based Clustering for Interpretable Predictive Models in High Dimensional Data

Description

This function will output the appropriate X and Y matrices in the right format for regression packages such as mgcv, caret and glmnet

Usage

r_prepare_data(data, response = "Y", exposure = "E", probe_names)

Arguments

data	the data frame which contains the response, exposure, and genes or cpgs or covariates. the columns should be labelled.
response	the column name of the response in the data argument
exposure	the column name of the exposure in the data argument
probe_names	the column names of the genes, or cpg sites or covariates

Value

a list of length 5:

X

the X matrix

Y

the response vector

E

the exposure vector

main_effect_names

the names of the main effects including the exposure

interaction_names

the names of the interaction effects

Examples

data("tcgaov")
tcgaov[1:5,1:6, with = FALSE]
Y <- log(tcgaov[["OS"]])
E <- tcgaov[["E"]]
genes <- as.matrix(tcgaov[,-c("OS","rn","subtype","E","status"),with = FALSE])
trainIndex <- drop(caret::createDataPartition(Y, p = 0.5, list = FALSE, times = 1))
testIndex <- setdiff(seq_len(length(Y)),trainIndex)

## Not run: 
cluster_res <- r_cluster_data(data = genes,
                              response = Y,
                              exposure = E,
                              train_index = trainIndex,
                              test_index = testIndex,
                              cluster_distance = "tom",
                              eclust_distance = "difftom",
                              measure_distance = "euclidean",
                              clustMethod = "hclust",
                              cutMethod = "dynamic",
                              method = "average",
                              nPC = 1,
                              minimum_cluster_size = 50)

pc_eclust_interaction <- r_prepare_data(data = cbind(cluster_res$clustersAddon$PC,
                                                     survival = Y[trainIndex],
                                                     subtype = E[trainIndex]),
                                        response = "survival", exposure = "subtype")
names(pc_eclust_interaction)
dim(pc_eclust_interaction$X)
pc_eclust_interaction$main_effect_names
pc_eclust_interaction$interaction_names

## End(Not run)

eclust documentation built on May 1, 2019, 8:46 p.m.