MicrobiomeSurv (version 0.1.0)

CVMajorityvotes: Cross validation for majority votes

Description

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

Usage

CVMajorityvotes(
  Survival,
  Censor,
  Prognostic = NULL,
  Micro.mat,
  Reduce = TRUE,
  Select = 5,
  Fold = 3,
  Ncv = 100,
  Mean = TRUE,
  Quantile = 0.5
)

Value

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.

Micro.mat

The microbiome data matrix that was used for the analysis either same as Micro.mat or a reduced version.

Progfact

The names of prognostic factors used.

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.

Micro.mat

A large or small microbiome profile matrix. A matrix with microbiome profiles where the number of rows should be equal to the number of taxa and number of columns should be equal to number of patients.

Reduce

A boolean parameter indicating if the microbiome profile matrix should be reduced, default is TRUE and larger microbiome profile matrix is reduced by supervised pca approach.

Select

Number of taxa (default is 5) to be selected from supervised PCA. This is valid only if the 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 100.

Mean

The cut off value for the classifier, default is the mean cutoff.

Quantile

If users want to use quantile as cutoff point. They need to specify Mean = FALSE and a quantile that they wish to use. The default is the median cutoff.

Author

Thi Huyen Nguyen,

Olajumoke Evangelina Owokotomo,

Ziv Shkedy

See Also

Majorityvotes

Examples

# Prepare data
data(Week3_response)
Week3_response = data.frame(Week3_response)
surv_fam_shan_w3 = data.frame(cbind(as.numeric(Week3_response$T1Dweek),
as.numeric(Week3_response$T1D)))
colnames(surv_fam_shan_w3) = c("Survival", "Censor")
prog_fam_shan_w3 = data.frame(factor(Week3_response$Treatment_new))
colnames(prog_fam_shan_w3) = c("Treatment")
data(fam_shan_trim_w3)
names_fam_shan_trim_w3 =
c("Unknown", "Lachnospiraceae", "S24.7", "Lactobacillaceae", "Enterobacteriaceae", "Rikenellaceae")
fam_shan_trim_w3 = data.matrix(fam_shan_trim_w3[ ,2:82])
rownames(fam_shan_trim_w3) = names_fam_shan_trim_w3
# Using the function
CVMajority_fam_shan_w3 = CVMajorityvotes(Survival = surv_fam_shan_w3$Survival,
                                         Micro.mat = fam_shan_trim_w3,
                                         Censor = surv_fam_shan_w3$Censor,
                                         Reduce=TRUE,
                                         Select=5,
                                         Mean = TRUE,
                                         Prognostic = prog_fam_shan_w3,
                                         Fold=3,
                                         Ncv=10)

# Get the class of the object
class(CVMajority_fam_shan_w3)     # An "cvmv" Class

# Method that can be used for the result
show(CVMajority_fam_shan_w3)
summary(CVMajority_fam_shan_w3)
plot(CVMajority_fam_shan_w3)