mirror of
https://github.com/agdamsbo/daDoctoR.git
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99 lines
2.4 KiB
R
99 lines
2.4 KiB
R
#' A repeated ordinal logistic regression function
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#'
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#' 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 meas Effect meassure. Input as c() of columnnames, use dput().
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#' @param vars variables in model. Input as c() of columnnames, use dput().
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#' @param string variables to test. Input as c() of columnnames, use dput().
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#' @param ci flag to get results as OR with 95% confidence interval.
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#' @param data data frame to pull variables from.
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#' @keywords olr
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#' @export
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rep_olr<-function(meas,vars,string,ci=FALSE,data){
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require(broom)
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require(MASS)
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d<-data
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x<-data.frame(d[,c(string)])
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v<-data.frame(d[,c(vars)])
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names(v)<-c(vars)
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y<-d[,c(meas)]
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dt<-cbind(y,v)
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m1<-length(coef(polr(y~.,data = dt,Hess=TRUE)))
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if (!is.factor(y)){stop("y should be a factor!")}
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if (ci==TRUE){
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df<-data.frame(matrix(ncol = 3))
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names(df)<-c("pred","or_ci","pv")
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for(i in 1:ncol(x)){
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dat<-cbind(dt,x[,i])
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m<-polr(y~.,data=dat,Hess=TRUE)
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ctable <- coef(summary(m))
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l<-suppressMessages(round(exp(confint(m))[-c(1:m1),1],2))
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u<-suppressMessages(round(exp(confint(m))[-c(1:m1),2],2))
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or<-round(exp(coef(m))[-c(1:m1)],2)
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or_ci<-paste0(or," (",l," to ",u,")")
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p <- (pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2)[1:length(coef(m))]
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pv<-round(p[-c(1:m1)],3)
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x1<-x[,i]
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if (is.factor(x1)){
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pred<-paste(names(x)[i],levels(x1)[-1],sep = "_")}
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else {pred<-names(x)[i]}
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df<-rbind(df,cbind(pred,or_ci,pv))
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}}
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if (ci==FALSE){
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df<-data.frame(matrix(ncol = 3))
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names(df)<-c("pred","b","pv")
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for(i in 1:ncol(x)){
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dat<-cbind(dt,x[,i])
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m<-polr(y~.,data=dat,Hess=TRUE)
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ctable <- coef(summary(m))
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b<-round(coef(m)[-c(1:m1)],2)
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p <- (pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2)[1:length(coef(m))]
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pv<-round(p[-c(1:m1)],3)
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x1<-x[,i]
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if (is.factor(x1)){
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pred<-paste(names(x)[i],levels(x1)[-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))
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}}
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pa<-as.numeric(df[,c("pv")])
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t <- ifelse(pa<=0.1,"include","drop")
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pa<-ifelse(pa<0.001,"<0.001",pa)
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pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
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ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
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r<-data.frame(df[,1:2],pa,t)[-1,]
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return(r)
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}
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