mirror of
https://github.com/agdamsbo/daDoctoR.git
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90 lines
2.3 KiB
R
90 lines
2.3 KiB
R
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#' A repeated linear regression function
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#'
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#' For bivariate analyses.
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#' @param y Effect meassure.
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#' @param v1 Main variable in model
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#' @keywords linear regression
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#' @export
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#' @examples
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#' rep_lm()
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rep_lm<-function(y,v1,string,ci=FALSE,data,v2=NULL,v3=NULL){
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## x is data.frame of predictors, y is vector of an aoutcome as a factor
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## output is returned as coefficient, or if ci=TRUE as coefficient with 95 % CI.
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## 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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require(broom)
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d<-data
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x<-select(d,one_of(c(string)))
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m1<-length(coef(lm(y~v1)))
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if (is.factor(y)){stop("Some kind of error message would be nice, but y should not be a factor!")}
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if (ci==TRUE){
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df<-data.frame(matrix(ncol = 4))
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names(df)<-c("pred","co_ci","pv","t")
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for(i in 1:ncol(x)){
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m<-lm(y~v1+x[,i])
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l<-suppressMessages(round(confint(m)[-c(1:m1),1],2))
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u<-suppressMessages(round(confint(m)[-c(1:m1),2],2))
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co<-round(coef(m)[-c(1:m1)],2)
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co_ci<-paste0(co," (",l," to ",u,")")
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pv<-round(tidy(m)$p.value[-c(1:m1)],3)
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pv<-ifelse(pv<0.001,"<0.001",pv)
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t <- ifelse(pv<=0.1|pv=="<0.001","include","drop")
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pv <- ifelse(pv<=0.05|pv=="<0.001",paste0("*",pv),
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ifelse(pv>0.05&pv<=0.1,paste0(".",pv),pv))
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v<-x[,i]
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if (is.factor(v)){
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pred<-paste(names(x)[i],levels(v)[-1],sep = "_")
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}
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else {pred<-names(x)[i]}
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df<-rbind(df,cbind(pred,co_ci,pv,t))
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}}
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if (ci==FALSE){
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df<-data.frame(matrix(ncol = 4))
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names(df)<-c("pred","b","pv","t")
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for(i in 1:ncol(x)){
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m<-lm(y~v1+x[,i])
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b<-round(coef(m)[-c(1:m1)],3)
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pv<-round(tidy(m)$p.value[-c(1:m1)],3)
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pv<-ifelse(pv<0.001,"<0.001",pv)
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t <- ifelse(pv<=0.1|pv=="<0.001","include","drop")
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pv <- ifelse(pv<=0.05|pv=="<0.001",paste0("*",pv),
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ifelse(pv>0.05&pv<=0.1,paste0(".",pv),pv))
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v<-x[,i]
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if (is.factor(v)){
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pred<-paste(names(x)[i],levels(v)[-1],sep = "_")
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}
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else {pred<-names(x)[i]}
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df<-rbind(df,cbind(pred,b,pv,t))
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}}
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return(df)
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}
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