rankInverseNormalDataFrame: rank-based inverse normal transformation of the data In FRESA.CAD: Feature Selection Algorithms for Computer Aided Diagnosis

Description

This function takes a data frame and a reference control population to return a z-transformed data set conditioned to the reference population. Each sample data for each feature column in the data frame is conditionally z-transformed using a rank-based inverse normal transformation, based on the rank of the sample in the reference frame.

Usage

 ```1 2 3 4``` ``` rankInverseNormalDataFrame(variableList, data, referenceframe, strata=NA) ```

Arguments

 `variableList` A data frame with two columns. The first one must have the names of the candidate variables and the other one the description of such variables `data` A data frame where all variables are stored in different columns `referenceframe` A data frame similar to `data`, but with only the control population `strata` The name of the column in `data` that stores the variable that will be used to stratify the model

Value

A data frame where each observation has been conditionally z-transformed, given control data

Author(s)

Jose G. Tamez-Pena and Antonio Martinez-Torteya

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31``` ``` ## Not run: # Start the graphics device driver to save all plots in a pdf format pdf(file = "Example.pdf") # Get the stage C prostate cancer data from the rpart package library(rpart) data(stagec) # Split the stages into several columns dataCancer <- cbind(stagec[,c(1:3,5:6)], gleason4 = 1*(stagec[,7] == 4), gleason5 = 1*(stagec[,7] == 5), gleason6 = 1*(stagec[,7] == 6), gleason7 = 1*(stagec[,7] == 7), gleason8 = 1*(stagec[,7] == 8), gleason910 = 1*(stagec[,7] >= 9), eet = 1*(stagec[,4] == 2), diploid = 1*(stagec[,8] == "diploid"), tetraploid = 1*(stagec[,8] == "tetraploid"), notAneuploid = 1-1*(stagec[,8] == "aneuploid")) # Remove the incomplete cases dataCancer <- dataCancer[complete.cases(dataCancer),] # Load a pre-established data frame with the names and descriptions of all variables data(cancerVarNames) # Set the group of no progression noProgress <- subset(dataCancer,pgstat==0) # z-transform g2 values using the no-progression group as reference dataCancerZTransform <- rankInverseNormalDataFrame(variableList = cancerVarNames[2,], data = dataCancer, referenceframe = noProgress) # Shut down the graphics device driver dev.off() ## End(Not run) ```

FRESA.CAD documentation built on Jan. 13, 2021, 3:39 p.m.