multivariate_BinEnsemble {FRESA.CAD} | R Documentation |
Multivariate Filters
Description
Returns the top set of features that are associated with the outcome based on Multivariate logistic models: LASSO and BSWiMS
Usage
multivariate_BinEnsemble(data,Outcome,limit=-1,adjustMethod="BH",...)
Arguments
data |
The data frame |
Outcome |
The outcome feature |
adjustMethod |
The method used by the p.adjust method |
limit |
The samples-wise fraction of features to return. |
... |
Parameters to be passed to the correlated_Remove function |
Value
Named vector with the adjusted p-values of the associted features
Author(s)
Jose G. Tamez-Pena
Examples
## Not run:
library("FRESA.CAD")
### Univariate Filter Examples ####
# Get the stage C prostate cancer data from the rpart package
data(stagec,package = "rpart")
# Prepare the data. Create a model matrix without the event time and interactions
stagec$pgtime <- NULL
stagec$eet <- as.factor(stagec$eet)
options(na.action = 'na.pass')
stagec_mat <- cbind(pgstat = stagec$pgstat,
as.data.frame(model.matrix(pgstat ~ .*.,stagec))[-1])
fnames <- colnames(stagec_mat)
fnames <- str_replace_all(fnames,":","__")
colnames(stagec_mat) <- fnames
# Impute the missing data
dataCancerImputed <- nearestNeighborImpute(stagec_mat)
dataCancerImputed[,1:ncol(dataCancerImputed)] <- sapply(dataCancerImputed,as.numeric)
# Get the top Features associated to pgstat
q_values <- multivariate_BinEnsemble(data=dataCancerImputed,
Outcome="pgstat")
## End(Not run)
[Package FRESA.CAD version 3.4.8 Index]