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137 lines
3.5 KiB
R
137 lines
3.5 KiB
R
#' Logistic regression of predictors according to STROBE
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#'
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#' Printable table of logistic regression analysis according to STROBE.
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#' @param meas binary outcome meassure variable, column name in data.frame as a string. Can be numeric or factor. Result is calculated accordingly.
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#' @param adj variables to adjust for, as string.
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#' @param data dataframe of data.
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#' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1.
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#' @keywords logistic
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#' @export
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#' @examples
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#' strobe_pred()
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strobe_pred<-function(meas,adj,data,dec=2){
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## Ønskeliste:
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##
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## - Sum af alle, der indgår (Overall N)
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## - Ryd op i kode, der der er overflødig %-regning, alternativt, så fiks at NA'er ikke skal regnes med.
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##
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require(dplyr)
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d<-data
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m<-d[,c(meas)]
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ads<-d[,c(adj)]
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## Crude ORs
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dfcr<-data.frame(matrix(NA,ncol = 3))
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names(dfcr)<-c("pred","or_ci","pv")
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for(i in 1:ncol(ads)){
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dat<-data.frame(m=m,ads[,i])
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names(dat)<-c("m",names(ads)[i])
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mn<-glm(m~.,family = binomial(),data=dat)
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suppressMessages(ci<-exp(confint(mn)))
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l<-round(ci[-1,1],2)
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u<-round(ci[-1,2],2)
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or<-round(exp(coef(mn))[-1],2)
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or_ci<-paste0(or," (",l," to ",u,")")
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pv<-round(tidy(mn)$p.value[-1],3)
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x1<-ads[,i]
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if (is.factor(x1)){
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pred<-paste0(names(ads)[i],levels(x1)[-1])
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}
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else {
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pred<-names(ads)[i]
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}
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dfcr<-rbind(dfcr,cbind(pred,or_ci,pv))
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}
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## Mutually adjusted ORs
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dat<-data.frame(m=m,ads)
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ma <- glm(m ~ .,family = binomial(), data = dat)
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actable <- coef(summary(ma))
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pa <- actable[,4]
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pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
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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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apv<-pa[1:length(coef(ma))]
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aco<-round(exp(coef(ma)),dec)
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suppressMessages(aci<-round(exp(confint(ma)),dec))
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alo<-aci[,1]
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aup<-aci[,2]
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aor_ci<-paste0(aco," (",alo," to ",aup,")")
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dat2<-dat[,-1]
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# names(dat2)<-c(var,names(ads))
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nq<-c()
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for (i in 1:ncol(dat2)){
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if (is.factor(dat2[,i])){
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vec<-dat2[,i]
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ns<-names(dat2)[i]
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for (r in 1:length(levels(vec))){
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vr<-levels(vec)[r]
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dr<-vec[vec==vr&!is.na(vec)]
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n<-as.numeric(length(dr))
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nall<-as.numeric(nrow(dat[!is.na(dat2[,c(ns)]),]))
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nl<-paste0(ns,levels(vec)[r])
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pro<-round(n/nall*100,0)
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rt<-paste0(n," (",pro,"%)")
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nq<-rbind(nq,cbind(nl,rt))
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}
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}
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if (!is.factor(dat2[,i])){
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num<-dat2[,i]
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nl<-names(dat2)[i]
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rt<-as.numeric(nrow(dat[!is.na(dat2[,c(nl)]),]))
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nq<-rbind(nq,cbind(nl,rt))
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}
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}
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rnames<-c()
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for (i in 1:ncol(dat2)){
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if (is.factor(dat2[,i])){
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rnames<-c(rnames,names(dat2)[i],paste0(names(dat2)[i],levels(dat2[,i])))
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}
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if (!is.factor(dat2[,i])){
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rnames<-c(rnames,paste0(names(dat2)[i],".all"),names(dat2)[i])
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}
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}
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res<-cbind(aor_ci,apv)
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rest<-data.frame(names=row.names(res),res,stringsAsFactors = F)
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numb<-data.frame(names=nq[,c("nl")],N=nq[,c("rt")],stringsAsFactors = F)
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namt<-data.frame(names=rnames,stringsAsFactors = F)
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coll<-left_join(left_join(namt,numb,by="names"),rest,by="names")
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header<-data.frame(matrix("Adjusted",ncol = ncol(coll)),stringsAsFactors = F)
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names(header)<-names(coll)
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df<-data.frame(rbind(header,coll),stringsAsFactors = F)
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names(dfcr)[1]<-c("names")
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suppressWarnings(re<-left_join(df,dfcr,by="names"))
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ref<-data.frame(re[,1],re[,2],re[,5],re[,3])
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names(ref)<-c("Variable","N","Crude OR (95 % CI)","Mutually adjusted OR (95 % CI)")
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return(ref)
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}
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