cvle-class: The cvle Class.

In MetabolicSurv: A Biomarker Validation Approach for Classification and Predicting Survival Using Metabolomics Signature

Description

The cvle Class.

Slots

Coef.mat

A matrix of coefficients with rows equals to number of cross validations and columns equals to number of metabolites.

Runtime

A vector of runtime for each iteration measured in seconds.

lambda

A vector of estimated optimum lambda for each iterations.

n

A vector of the number of selected metabolites

Met.mat

A matrix with 0 and 1. Number of rows equals to number of iterations and number of columns equals to number of metabolites. 1 indicates that the particular metabolite was selected or had nonzero coefficient and otherwise it is zero.

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.

pld

A vector of partial likelihood deviance at each cross validations.

Mdata

The metabolite matrix that was used for the analysis which can either be the full the full data or a reduced supervised PCA version.

Author(s)

Olajumoke Evangelina Owokotomo, [e-mail]

Ziv Shkedy

See Also

EstimateHR, glmnet, Lasoelacox

Examples

## GENERATE SOME METABOLIC SURVIVAL DATA WITH PROGNOSTIC FACTORS
Data<-MSData(nPatients=100,nMet=150,Prop=0.5)

## USE THE FUNCTION
Eg = CVLasoelacox(Survival = Data$Survival,Censor = Data$Censor,
Mdata = t(Data$Mdata),Prognostic = Data$Prognostic, Quantile = 0.5,
Metlist = NULL,Standardize = TRUE, Reduce=FALSE, Select=15,
Alpha = 1,Fold = 4,Ncv = 10,nlambda = 100)

## GET THE CLASS OF THE OBJECT
class(Eg)     # An "cvle" Class

##  METHOD THAT CAN BE USED FOR THIS CLASS
show(Eg)
summary(Eg)
plot(Eg, type =3)

MetabolicSurv documentation built on June 11, 2021, 9:06 a.m.