From af0c04e5ce8ec0030c3ce0342a4733a59cb7ccdf Mon Sep 17 00:00:00 2001
From: agdamsbo <agdamsbo@protonmail.com>
Date: Thu, 4 Oct 2018 10:10:35 +0200
Subject: [PATCH] universalising..

---
 R/rep_glm.R    | 23 +++++++++--------------
 man/rep_glm.Rd |  8 ++++++--
 2 files changed, 15 insertions(+), 16 deletions(-)

diff --git a/R/rep_glm.R b/R/rep_glm.R
index 4978c85..a06090e 100644
--- a/R/rep_glm.R
+++ b/R/rep_glm.R
@@ -1,15 +1,17 @@
 #' A repeated logistic regression function
 #'
 #' @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 y Effect meassure.
+#' @param meas Effect meassure. 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 ci flag to get results as OR with 95% confidence interval.
+#' @param data data frame to pull variables from.
 #' @keywords logistic regression
 #' @export
 #' @examples
 #' rep_glm()
 
-rep_glm<-function(y,vars,string,ci=FALSE,data){
+rep_glm<-function(meas,vars,string,ci=FALSE,data){
   ## x is data.frame of predictors, y is vector of an aoutcome as a factor
   ## output is returned as coefficient, or if or=TRUE as OR with 95 % CI.
   ##
@@ -17,8 +19,9 @@ rep_glm<-function(y,vars,string,ci=FALSE,data){
   require(dplyr)
 
   d<-data
-  x<-select(d,one_of(c(string)))
-  v<-select(d,one_of(c(vars)))
+  x<-data.frame(d[,c(string)])
+  v<-data.frame(d[,c(vars)])
+  y<-d[,c(meas)]
   dt<-cbind(y,v)
   m1<-length(coef(glm(y~.,family = binomial(),data = dt)))
 
@@ -30,30 +33,22 @@ rep_glm<-function(y,vars,string,ci=FALSE,data){
     names(df)<-c("pred","or_ci","pv")
 
     for(i in 1:ncol(x)){
-
       dat<-cbind(dt,x[,i])
-
       m<-glm(y~.,family = binomial(),data=dat)
 
       l<-suppressMessages(round(exp(confint(m))[-c(1:m1),1],2))
       u<-suppressMessages(round(exp(confint(m))[-c(1:m1),2],2))
       or<-round(exp(coef(m))[-c(1:m1)],2)
-
       or_ci<-paste0(or," (",l," to ",u,")")
-
       pv<-round(tidy(m)$p.value[-c(1:m1)],3)
-
       x1<-x[,i]
 
       if (is.factor(x1)){
-        pred<-paste(names(x)[i],levels(x1)[-1],sep = "_")
-      }
+        pred<-paste(names(x)[i],levels(x1)[-1],sep = "_")}
 
       else {pred<-names(x)[i]}
 
-      df<-rbind(df,cbind(pred,or_ci,pv))
-
-    }}
+      df<-rbind(df,cbind(pred,or_ci,pv))}}
 
   if (ci==FALSE){
 
diff --git a/man/rep_glm.Rd b/man/rep_glm.Rd
index 291c196..aacf28d 100644
--- a/man/rep_glm.Rd
+++ b/man/rep_glm.Rd
@@ -4,14 +4,18 @@
 \alias{rep_glm}
 \title{A repeated logistic regression function}
 \usage{
-rep_glm(y, vars, string, ci = FALSE, data)
+rep_glm(meas, vars, string, ci = FALSE, data)
 }
 \arguments{
-\item{y}{Effect meassure.}
+\item{meas}{Effect meassure. Input as c() of columnnames, use dput().}
 
 \item{vars}{variables in model. Input as c() of columnnames, use dput().}
 
 \item{string}{variables to test. Input as c() of columnnames, use dput().}
+
+\item{ci}{flag to get results as OR with 95% confidence interval.}
+
+\item{data}{data frame to pull variables from.}
 }
 \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.