mirror of
https://github.com/agdamsbo/daDoctoR.git
synced 2024-11-21 19:30:22 +01:00
New minor function for easy forestplotting
This commit is contained in:
parent
608afe02d8
commit
8e26088326
@ -1,7 +1,7 @@
|
|||||||
Package: daDoctoR
|
Package: daDoctoR
|
||||||
Type: Package
|
Type: Package
|
||||||
Title: FUNCTIONS FOR HEALTH RESEARCH
|
Title: FUNCTIONS FOR HEALTH RESEARCH
|
||||||
Version: 0.1.0.9015
|
Version: 0.1.0.9016
|
||||||
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.
|
||||||
|
@ -3,6 +3,7 @@
|
|||||||
export(age_calc)
|
export(age_calc)
|
||||||
export(col_fact)
|
export(col_fact)
|
||||||
export(col_num)
|
export(col_num)
|
||||||
|
export(comb_olr)
|
||||||
export(cpr_check)
|
export(cpr_check)
|
||||||
export(cpr_sex)
|
export(cpr_sex)
|
||||||
export(date_convert)
|
export(date_convert)
|
||||||
|
23
R/comb_olr.R
Normal file
23
R/comb_olr.R
Normal file
@ -0,0 +1,23 @@
|
|||||||
|
#' An ordinal logistic regression function for plotting
|
||||||
|
#'
|
||||||
|
#' 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.
|
||||||
|
#' @param meas primary outcome (factor with >2 levels).
|
||||||
|
#' @param vars variables in model. Input as c() of columnnames, use dput().
|
||||||
|
#' @param dta data frame to pull variables from.
|
||||||
|
#' @keywords olr
|
||||||
|
#' @export
|
||||||
|
#' @examples
|
||||||
|
#' comb_olr()
|
||||||
|
|
||||||
|
|
||||||
|
comb_olr<-function(meas,vars,data){
|
||||||
|
require(MASS)
|
||||||
|
|
||||||
|
ad<-vars
|
||||||
|
d<-data
|
||||||
|
d2<-d[,c(meas,ad)]
|
||||||
|
names(d2)[1]<-"meas"
|
||||||
|
x<-polr(meas~.,data = d2,Hess = TRUE)
|
||||||
|
mat<-rbind(mat,c(exp(coef(x)), exp(confint(x))))
|
||||||
|
return(data.frame(mat,stringsAsFactors = FALSE))
|
||||||
|
}
|
@ -2,8 +2,8 @@
|
|||||||
#'
|
#'
|
||||||
#' Repeats the epi.tests from the epiR package. Either gs or test should be of length 1.
|
#' 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 gold the test or meassure used as "golden standard". Format as dichotomised factor.
|
#' @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.
|
||||||
#' @param test possible predictive tests to evaluate. Format as dichotomised factor.
|
#' @param test possible predictive tests to evaluate. Format as list of variable names to include. All variables should be formated as dichotomised factor.
|
||||||
#' @param data dataframe to draw variables from.
|
#' @param data dataframe to draw variables from.
|
||||||
#' @keywords ppv npv sensitivity specificity
|
#' @keywords ppv npv sensitivity specificity
|
||||||
#' @export
|
#' @export
|
||||||
|
22
man/comb_olr.Rd
Normal file
22
man/comb_olr.Rd
Normal file
@ -0,0 +1,22 @@
|
|||||||
|
% Generated by roxygen2: do not edit by hand
|
||||||
|
% Please edit documentation in R/comb_olr.R
|
||||||
|
\name{comb_olr}
|
||||||
|
\alias{comb_olr}
|
||||||
|
\title{An ordinal logistic regression function for plotting}
|
||||||
|
\usage{
|
||||||
|
comb_olr(meas, vars, data)
|
||||||
|
}
|
||||||
|
\arguments{
|
||||||
|
\item{meas}{primary outcome (factor with >2 levels).}
|
||||||
|
|
||||||
|
\item{vars}{variables in model. Input as c() of columnnames, use dput().}
|
||||||
|
|
||||||
|
\item{dta}{data frame to pull variables from.}
|
||||||
|
}
|
||||||
|
\description{
|
||||||
|
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.
|
||||||
|
}
|
||||||
|
\examples{
|
||||||
|
comb_olr()
|
||||||
|
}
|
||||||
|
\keyword{olr}
|
@ -7,9 +7,9 @@
|
|||||||
rep_epi_tests(gold, test, data)
|
rep_epi_tests(gold, test, data)
|
||||||
}
|
}
|
||||||
\arguments{
|
\arguments{
|
||||||
\item{gold}{the test or meassure used as "golden standard". Format as dichotomised factor.}
|
\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.}
|
||||||
|
|
||||||
\item{test}{possible predictive tests to evaluate. Format as dichotomised factor.}
|
\item{test}{possible predictive tests to evaluate. Format as list of variable names to include. All variables should be formated as dichotomised factor.}
|
||||||
|
|
||||||
\item{data}{dataframe to draw variables from.}
|
\item{data}{dataframe to draw variables from.}
|
||||||
}
|
}
|
||||||
|
Loading…
Reference in New Issue
Block a user