#' A repeated logistic regression function #' #' For bivariate analyses. #' @param y Effect meassure. #' @param v1 Main variable in model #' @keywords logistic regression #' @export #' @examples #' rep_glm() rep_glm<-function(y,v1,string,ci=FALSE,data,v2=NULL,v3=NULL){ ## 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. ## The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI. require(broom) d<-data x<-select(d,one_of(c(string))) m1<-length(coef(glm(y~v1,family = binomial()))) if (!is.factor(y)){stop("Some kind of error message would be nice, but y should be a factor!")} if (ci==TRUE){ df<-data.frame(matrix(ncol = 4)) names(df)<-c("pred","or_ci","pv","t") for(i in 1:ncol(x)){ m<-glm(y~v1+x[,i],family = binomial()) 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)$p.value[-c(1:m1)],3) pv<-ifelse(pv<0.001,"<0.001",pv) t <- ifelse(pv<=0.1|pv=="<0.001","include","drop") pv <- ifelse(pv<=0.05|pv=="<0.001",paste0("*",pv), ifelse(pv>0.05&pv<=0.1,paste0(".",pv),pv)) v<-x[,i] if (is.factor(v)){ pred<-paste(names(x)[i],levels(v)[-1],sep = "_") } else {pred<-names(x)[i]} df<-rbind(df,cbind(pred,or_ci,pv,t)) }} if (ci==FALSE){ df<-data.frame(matrix(ncol = 4)) names(df)<-c("pred","b","pv","t") for(i in 1:ncol(x)){ m<-glm(y~v1+x[,i],family = binomial()) b<-round(coef(m)[-c(1:m1)],3) pv<-round(tidy(m)$p.value[-c(1:m1)],3) pv<-ifelse(pv<0.001,"<0.001",pv) t <- ifelse(pv<=0.1|pv=="<0.001","include","drop") pv <- ifelse(pv<=0.05|pv=="<0.001",paste0("*",pv), ifelse(pv>0.05&pv<=0.1,paste0(".",pv),pv)) v<-x[,i] if (is.factor(v)){ pred<-paste(names(x)[i],levels(v)[-1],sep = "_") } else {pred<-names(x)[i]} df<-rbind(df,cbind(pred,b,pv,t)) }} result<-df return(df) }