evaluateGlmnet: function to do prediction and evaluate based on the results...

In lizhongliu1996/PredictMisc: package that use three methods to predict methylation regions

Description

function to do prediction and evaluate based on the results of glmnet

Usage

evaluateGlmnet(fit, alpha, testTable, respCol_index, whichRep_int,
  whichCVfold_int)

Arguments

fit	fit result of glmnet
alpha	specific alpha value use for this glmnet fit object
testTable	test data frame that each column represents a CpG probe while each row represents a sample, each cell is a M value, first column is phenodata
respCol_index	col number of response variable
whichRep_int	number of repetation
whichCVfold_int	number of cross validation

Details

evaluate performance of results return by function glmnetWrapper

Value

A data frame contains parameters for evaluate prediction performance

• NumOfRep : Number of repetation

• NumOfCv : Number of cross validation

• alphaValue : alpha value of glmnet

• lambda.min : lambda min value of glmnet

• auc_results : auc value of prediction

• Sensitivity : Sensitivity value of prediction

• Specificity : Specificity value of prediction

• ... : more index

Examples

## Not run: 
   data(ExampleMvalue_test)
   data(glmnet_Fit_ls)
   alphaValue = seq(0, 1, by = 0.1)

   test <- evaluateGlmnet(
     fit = glmnet_Fit_ls[[1]],
     alpha = alphaValue[1],
     testTable = ExampleMvalue_test,
     respCol_index = 1,
     whichRep_int = 1,
     whichCVfold_int = 1
     )
 
## End(Not run)

lizhongliu1996/PredictMisc documentation built on Aug. 23, 2019, 5:55 a.m.