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2
DESCRIPTION
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@ -1,7 +1,7 @@
Package: daDoctoR Package: daDoctoR
Type: Package Type: Package
Title: FUNCTIONS FOR HEALTH RESEARCH Title: FUNCTIONS FOR HEALTH RESEARCH
Version: 0.1.0.9002 Version: 0.1.0.9003
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.

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R/rep_reg_cie.R
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#' A repeated regression function for change-in-estimate analysis #' A repeated regression function for change-in-estimate analysis
#' #'
#' For bivariate analyses. From "Modeling and variable selection in epidemiologic analysis." - S. Greenland, 1989. #' For bivariate analyses, binary logistic or linear regression. From "Modeling and variable selection in epidemiologic analysis." - S. Greenland, 1989.
#' @param meas Effect meassure. Input as c() of columnnames, use dput(). #' @param meas Effect meassure. Input as c() of columnnames, use dput().
#' @param vars variables in model. Input as c() of columnnames, use dput(). #' @param vars variables in model. Input as c() of columnnames, use dput().
#' @param string variables to test. Input as c() of columnnames, use dput(). #' @param string variables to test. Input as c() of columnnames, use dput().
@ -12,7 +12,7 @@
#' @examples #' @examples
#' rep_reg_cie() #' rep_reg_cie()
rep_reg_cie<-function(meas,vars,string,data,logistic=FALSE,cut=0.1){ rep_reg_cie<-function(meas,vars,string,data,cut=0.1){
require(broom) require(broom)
@ -25,9 +25,9 @@ rep_reg_cie<-function(meas,vars,string,data,logistic=FALSE,cut=0.1){
c<-as.numeric(cut) c<-as.numeric(cut)
if(logistic==FALSE){ if(!is.factor(y)){
if (is.factor(y)){stop("Logistic is flagged as FALSE, but the provided meassure is formatted as a factor!")} meth<-"linear regression"
e<-as.numeric(round(coef(lm(y~.,data = dt)),3))[1] e<-as.numeric(round(coef(lm(y~.,data = dt)),3))[1]
df<-data.frame(pred="base",b=e) df<-data.frame(pred="base",b=e)
@ -42,14 +42,13 @@ if (is.factor(y)){stop("Logistic is flagged as FALSE, but the provided meassure
df<-rbind(df,cbind(pred,b)) } df<-rbind(df,cbind(pred,b)) }
di<-as.vector(abs(e-as.numeric(df[-1,2]))/e) di<-as.vector(round(abs(e-as.numeric(df[-1,2]))/e,3))
dif<-c(NA,di) dif<-c(NA,di)
t<-c(NA,ifelse(di>=c,"include","drop")) t<-c(NA,ifelse(di>=c,"include","drop"))
r<-cbind(df,dif,t) } r<-cbind(df,dif,t) }
if(logistic==TRUE){ if(is.factor(y)){
meth="logistic regression"
if (!is.factor(y)){stop("Logistic is flagged as TRUE, but the provided meassure is NOT formatted as a factor!")}
e<-as.numeric(round(exp(coef(glm(y~.,family=binomial(),data=dt))),3))[1] e<-as.numeric(round(exp(coef(glm(y~.,family=binomial(),data=dt))),3))[1]
@ -65,10 +64,10 @@ if (!is.factor(y)){stop("Logistic is flagged as TRUE, but the provided meassure
df<-rbind(df,cbind(pred,b)) } df<-rbind(df,cbind(pred,b)) }
di<-as.vector(abs(e-as.numeric(df[-1,2]))/e) di<-as.vector(round(abs(e-as.numeric(df[-1,2]))/e,3))
dif<-c(NA,di) dif<-c(NA,di)
t<-c(NA,ifelse(di>=c,"include","drop")) t<-c(NA,ifelse(di>=c,"include","drop"))
r<-cbind(df,dif,t) r<-cbind(df,dif,t)
} }
return(r) return(list("method"=meth,"analyses"=r))
} }

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man/rep_reg_cie.Rd
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@ -4,7 +4,7 @@
\alias{rep_reg_cie} \alias{rep_reg_cie}
\title{A repeated regression function for change-in-estimate analysis} \title{A repeated regression function for change-in-estimate analysis}
\usage{ \usage{
rep_reg_cie(meas, vars, string, data, logistic = FALSE, cut = 0.1) rep_reg_cie(meas, vars, string, data, cut = 0.1)
} }
\arguments{ \arguments{
\item{meas}{Effect meassure. Input as c() of columnnames, use dput().} \item{meas}{Effect meassure. Input as c() of columnnames, use dput().}
@ -15,12 +15,12 @@ rep_reg_cie(meas, vars, string, data, logistic = FALSE, cut = 0.1)
\item{data}{data frame to pull variables from.} \item{data}{data frame to pull variables from.}
\item{logistic}{flag to set logistic (TRUE) or linear (FALSE,standard) analysis.}
\item{cut}{cut value for gating if including or dropping the tested variable. As suggested bu S. Greenland (1989).} \item{cut}{cut value for gating if including or dropping the tested variable. As suggested bu S. Greenland (1989).}
\item{logistic}{flag to set logistic (TRUE) or linear (FALSE,standard) analysis.}
} }
\description{ \description{
For bivariate analyses. From "Modeling and variable selection in epidemiologic analysis." - S. Greenland, 1989. For bivariate analyses, binary logistic or linear regression. From "Modeling and variable selection in epidemiologic analysis." - S. Greenland, 1989.
} }
\examples{ \examples{
rep_reg_cie() rep_reg_cie()