cvle-class function

class

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.

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)

See Also

EstimateHR, glmnet, Lasoelacox

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy