cvsim

A 3-way array in which first, second, and third
dimensions correspond to number of metabolites, Hazard ratio
infromation(Estimated HR, LowerCI and UpperCI),
and number of cross validation respectively.
This contains the estimated HR on test data and
dimension reduction method is PCA.

HRpca

A 3-way array in which first, second, and
third dimensions correspond to number of metabolites,
Hazard ratio infromation(Estimated HR, LowerCI and UpperCI),
and number of cross validation respectively. This contains the
estimated HR on test data and dimension reduction method is
PLS.

HRpls

The number of metabolites in the reduced matrix

Nmets

The number of cross validation done

A sequence of top k metabolites considered.
Default is Top=seq(5,100,by=5)

An approach to identifies metabolic biomarker signature for metabolic data by discovering predictive metabolite for predicting survival and classifying patients into risk groups.
Classifiers are constructed as a linear combination of predictive/important metabolites, prognostic factors and treatment effects if necessary.
Several methods were implemented to reduce the metabolomics matrix such as the principle component analysis of Wold Svante et al. (1987) <doi:10.1016/0169-7439(87)80084-9> ,
the LASSO method by Robert Tibshirani (1998) <doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>, the
elastic net approach by Hui Zou and Trevor Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>.
Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified.
Large scale cross validation can be performed in order to investigate the mostly selected predictive metabolites and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique.

cvsim: Constructor for the cvsim class

Description

Usage

Arguments

Value