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New minor function for easy forestplotting
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Package: daDoctoR
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Type: Package
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Title: FUNCTIONS FOR HEALTH RESEARCH
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Version: 0.1.0.9015
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Version: 0.1.0.9016
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Author@R: c(person("Andreas", "Gammelgaard Damsbo", email = "agdamsbo@pm.me", role = c("cre", "aut")))
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Maintainer: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>
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Description: I am a Danish medical doctor involved in neuropsychiatric research.
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export(age_calc)
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export(col_fact)
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export(col_num)
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export(comb_olr)
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export(cpr_check)
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export(cpr_sex)
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export(date_convert)
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R/comb_olr.R
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R/comb_olr.R
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#' An ordinal logistic regression function for plotting
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#'
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#' Should be combined with "rep_olr()". The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI.
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#' @param meas primary outcome (factor with >2 levels).
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#' @param vars variables in model. Input as c() of columnnames, use dput().
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#' @param dta data frame to pull variables from.
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#' @keywords olr
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#' @export
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#' @examples
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#' comb_olr()
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comb_olr<-function(meas,vars,data){
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require(MASS)
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ad<-vars
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d<-data
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d2<-d[,c(meas,ad)]
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names(d2)[1]<-"meas"
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x<-polr(meas~.,data = d2,Hess = TRUE)
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mat<-rbind(mat,c(exp(coef(x)), exp(confint(x))))
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return(data.frame(mat,stringsAsFactors = FALSE))
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}
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#'
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#' Repeats the epi.tests from the epiR package. Either gs or test should be of length 1.
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#' @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.
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#' @param gold the test or meassure used as "golden standard". Format as dichotomised factor.
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#' @param test possible predictive tests to evaluate. Format as dichotomised factor.
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#' @param gold the test or meassure used as "golden standard". Format as list of variable names to include. All variables should be formated as dichotomised factor.
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#' @param test possible predictive tests to evaluate. Format as list of variable names to include. All variables should be formated as dichotomised factor.
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#' @param data dataframe to draw variables from.
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#' @keywords ppv npv sensitivity specificity
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#' @export
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man/comb_olr.Rd
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man/comb_olr.Rd
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% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/comb_olr.R
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\name{comb_olr}
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\alias{comb_olr}
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\title{An ordinal logistic regression function for plotting}
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\usage{
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comb_olr(meas, vars, data)
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}
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\arguments{
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\item{meas}{primary outcome (factor with >2 levels).}
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\item{vars}{variables in model. Input as c() of columnnames, use dput().}
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\item{dta}{data frame to pull variables from.}
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}
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\description{
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Should be combined with "rep_olr()". The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI.
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}
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\examples{
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comb_olr()
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}
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\keyword{olr}
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rep_epi_tests(gold, test, data)
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}
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\arguments{
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\item{gold}{the test or meassure used as "golden standard". Format as dichotomised factor.}
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\item{gold}{the test or meassure used as "golden standard". Format as list of variable names to include. All variables should be formated as dichotomised factor.}
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\item{test}{possible predictive tests to evaluate. Format as dichotomised factor.}
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\item{test}{possible predictive tests to evaluate. Format as list of variable names to include. All variables should be formated as dichotomised factor.}
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\item{data}{dataframe to draw variables from.}
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}
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