CVMajorityvotes function

Cross validation for majority votes

This function does cross validation for the Majority votes based classification.

CVMajorityvotes(
  Survival,
  Censor,
  Prognostic = NULL,
  Mdata,
  Reduce = TRUE,
  Select = 15,
  Fold = 3,
  Ncv = 100
)

Arguments

• Survival: A vector of survival time with length equals to number of subjects
• Censor: A vector of censoring indicator
• Prognostic: A dataframe containing possible prognostic(s) factor and/or treatment effect to be used in the model.
• Mdata: A large or small metabolic profile matrix. A matrix with metabolic profiles where the number of rows should be equal to the number of metabolites and number of columns should be equal to number of patients.
• Reduce: A boolean parameter indicating if the metabolic profile matrix should be reduced, default is TRUE and larger metabolic profile matrix is reduced by supervised pca approach and first pca is extracted from the reduced matrix to be used in the classifier.
• Select: Number of metabolites (default is 15) to be selected from supervised PCA. This is valid only if th argument Reduce=TRUE
• Fold: Number of times in which the dataset is divided. Default is 3 which implies dataset will be divided into three groups and 2/3 of the dataset will be the train datset and 1/3 will be to train the results.
• Ncv: The Number of cross validation loop. Default is 50 but it is recommended to have at least 100.

Returns

A object of class cvmv is returned with the following values - HRTrain: A matrix of survival information for the training dataset. It has three columns representing the estimated HR, the 95% lower confidence interval and the 95% upper confidence interval.

• HRTest: A matrix of survival information for the test dataset. It has three columns representing the estimated HR, the 95% lower confidence interval and the 95% upper confidence interval.

• Ncv: The number of cross validation used

• Mdata: The Metabolite data matrix that was used for the analysis either same as Mdata or a reduced version.

• Progfact: The names of prognostic factors used

Details

This function does cross validation for the Majority votes based classification which is a cross validated approach to Majorityvotes.

Examples

## FIRSTLY SIMULATING A METABOLIC SURVIVAL DATA
Data = MSData(nPatients = 100, nMet = 150, Prop = 0.5)

## USING THE FUNCTION
Result = CVMajorityvotes(Survival=Data$Survival,Censor=Data$Censor,
Prognostic=Data$Prognostic, Mdata=t(Data$Mdata), Reduce=FALSE,
Select=15, Fold=3, Ncv=10)

## GET THE CLASS OF THE OBJECT
class(Result)     # An "cvmv" Class

##  METHOD THAT CAN BE USED FOR THE RESULT
show(Result)
summary(Result)

See Also

Majorityvotes

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy