eval_CI: Compute R-squared or AUC with confidence interval.

In rivas-lab/snpnet: snpnet: Efficient Lasso Solver for Large SNP Data

Description

Given a data frame containing a response variable and predictor(s), compute R-squared or AUC with confidence interval and the p-value. We currently supports gaussian and binomial models. If multiple predictors are specified, we return the minimum p-value

Usage

eval_CI(data, response, predictors, family, level = 0.95)

Arguments

data	A data frame containing the response and predictor(s)
response	The name of the response variable
predictors	The predictor variable(s)
level	The confidence level of the interval

Value

a data frame containing r-squared or AUC (eval), confidence interval (l_eval, u_eval), p-value along with the information of the specified response and predictors

Examples

## Not run: 
eval_CI(data_df, 'INI50', c('covar_score'), 'gaussian')
eval_CI(data_df, 'HC326', c('covar_score'), 'binomial')

## End(Not run)

rivas-lab/snpnet documentation built on Dec. 14, 2021, 3:22 a.m.