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2
DESCRIPTION
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Package: daDoctoR Package: daDoctoR
Type: Package Type: Package
Title: FUNCTIONS FOR HEALTH RESEARCH Title: FUNCTIONS FOR HEALTH RESEARCH
Version: 0.1.0.9011 Version: 0.1.0.9012
Author@R: c(person("Andreas", "Gammelgaard Damsbo", email = "agdamsbo@pm.me", role = c("cre", "aut"))) Author@R: c(person("Andreas", "Gammelgaard Damsbo", email = "agdamsbo@pm.me", role = c("cre", "aut")))
Maintainer: Andreas Gammelgaard Damsbo <agdamsbo@pm.me> Maintainer: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>
Description: I am a Danish medical doctor involved in neuropsychiatric research. Description: I am a Danish medical doctor involved in neuropsychiatric research.

22
R/rep_epi_tests.R
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#' A repeated epi.tests function #' A repeated epi.tests function
#' #'
#' Repeats the epi.tests from the epiR package. #' Repeats the epi.tests from the epiR package. Either gs or test should be of length 1.
#' @description For bivariate analyses. The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI. #' @description For bivariate analyses. The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI.
#' @param gs the test or meassure used as "golden standard". Format as dichotomised factor. #' @param gold the test or meassure used as "golden standard". Format as dichotomised factor.
#' @param test possible predictive tests to evaluate. Format as dichotomised factor. #' @param test possible predictive tests to evaluate. Format as dichotomised factor.
#' @param data dataframe to draw variables from.
#' @keywords ppv npv sensitivity specificity #' @keywords ppv npv sensitivity specificity
#' @export #' @export
#' @examples #' @examples
#' rep_epi_tests() #' rep_epi_tests()
rep_epi_tests<-function(gs,test){ rep_epi_tests<-function(gold,test,data){
require(epiR) require(epiR)
d<-data
test<-d[,c(test)]
gs<-d[,c(gold)]
ls<-list() ls<-list()
if (length(gold)==1){
for (i in 1:ncol(test)){ for (i in 1:ncol(test)){
t<-table(test[,i],gs) t<-table(test[,i],gs)
rval <- epi.tests(t, conf.level = 0.95) rval <- epi.tests(t, conf.level = 0.95)
n<-names(test)[i] n<-names(test)[i]
ls[[i]]<-list(n,rval) ls[[i]]<-list(n,rval)
}}
else {
for (i in 1:ncol(gs)){
t<-table(test,gs[,i])
rval <- epi.gss(t, conf.level = 0.95)
n<-names(gs)[i]
ls[[i]]<-list(n,rval)
}
} }
return(ls) return(ls)
} }

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man/rep_epi_tests.Rd
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\alias{rep_epi_tests} \alias{rep_epi_tests}
\title{A repeated epi.tests function} \title{A repeated epi.tests function}
\usage{ \usage{
rep_epi_tests(gs, test) rep_epi_tests(gold, test, data)
} }
\arguments{ \arguments{
\item{gs}{the test or meassure used as "golden standard". Format as dichotomised factor.} \item{gold}{the test or meassure used as "golden standard". Format as dichotomised factor.}
\item{test}{possible predictive tests to evaluate. Format as dichotomised factor.} \item{test}{possible predictive tests to evaluate. Format as dichotomised factor.}
\item{data}{dataframe to draw variables from.}
} }
\description{ \description{
For bivariate analyses. The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI. For bivariate analyses. The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI.
} }
\details{ \details{
Repeats the epi.tests from the epiR package. Repeats the epi.tests from the epiR package. Either gs or test should be of length 1.
} }
\examples{ \examples{
rep_epi_tests() rep_epi_tests()