model_evaluation {MSML}

model_evaluation function

Description

This function will identify the best model in the validation and test dataset.

Usage

model_evaluation(dat, mv, tn, prev, pthreshold = 0.05, method = "R2ROC")

Arguments

dat

This is the dataframe for all the combinations of the model in a matrix format

mv

The total number of columns in data_train/data_valid

tn

The total number of best models to be identified

prev

The prevalence of disease in the data

pthreshold

The significance p value threshold when comparing models (default 0.05)

method

The methods to be used to evaluate models (e.g. R2ROC (default) or r2redux)

Value

This function will generate all possible model outcomes for validation and test dataset

Examples

dat <- predict_validation
mv=8
tn=15
prev=0.047
out=model_evaluation(dat,mv,tn,prev)
#This process will generate three output files.
#out$out_all, contains AUC, p values for AUC, R2, and p values for R2, 
#respectively for all models.
#out$out_start, contains AUC, p values for AUC, R2, and p values for R2,
#respectively for top tn models.
#out$out_selected, contains AUC, p values for AUC, R2, and p values for R2,
#respectively for best models.  This also includes selected features for models
#For details (see https://github.com/mommy003/MSML).