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09719ea905
Merge branch 'master' of github.com:agdamsbo/daDoctoR # Conflicts: # DESCRIPTION # R/print_diff_bygroup.R # R/print_diff_byvar.R # R/print_log.R # R/print_olr.R # R/print_pred.R
363 lines
11 KiB
R
363 lines
11 KiB
R
#' Regression model of predictors according to STROBE, bi- and multivariable.
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#'
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#' Printable table of regression model according to STROBE for linear or binary outcome-variables.
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#' Includes both bivariate and multivariate in the same table.
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#' Output is a list, with the first item being the main "output" as a dataframe.
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#' Automatically uses logistic regression model for dichotomous outcome variable and linear regression model for continuous outcome variable. Linear regression will give estimated adjusted true mean in list.
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#' For logistic regression gives count of outcome variable pr variable level.
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#' @param meas binary outcome measure 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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#' @param n.by.adj flag to indicate whether to count number of patients in adjusted model or overall for outcome measure not NA.
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#' @param p.val flag to include p-values in table, set to FALSE as standard.
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#' @keywords logistic
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#' @export
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<<<<<<< HEAD
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print_pred<-function(meas,adj,data,dec=2,n.by.adj=FALSE,p.val=FALSE){
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=======
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strobe_pred<-function(meas,adj,data,dec=2,n.by.adj=FALSE,p.val=FALSE){
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>>>>>>> d8ffa3dc7b67e43a846bbd58e055a141a010b304
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## Wish list:
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## - SPEED, maybe flags to include/exclude time consuming tasks
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## - Include ANOVA in output list, flag to include
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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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if(is.factor(m)){
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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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n.mn<-c()
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nref<-c()
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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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n.mn<-c(n.mn,nrow(mn$model))
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suppressMessages(ci<-exp(confint(mn)))
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l<-round(ci[-1,1],dec)
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u<-round(ci[-1,2],dec)
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or<-round(exp(coef(mn))[-1],dec)
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or_ci<-paste0(or," (",l," to ",u,")")
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pv<-round(tidy(mn)$p.value[-1],dec+1)
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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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miss<-length(ma$na.action)
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actable <- coef(summary(ma))
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pa <- actable[,4]
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pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
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pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
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ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
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apv<-pa[1:length(coef(ma))]
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aco<-round(exp(coef(ma)),dec)
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suppressMessages(aci<-round(exp(confint(ma)),dec))
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alo<-aci[,1]
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aup<-aci[,2]
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aor_ci<-paste0(aco," (",alo," to ",aup,")")
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# names(dat2)<-c(var,names(ads))
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nq<-c()
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nall<-length(!is.na(dat[,1]))
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if (n.by.adj==TRUE){
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dat2<-ma$model
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# nalt<-nrow(dat2)
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for (i in 2: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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## Counting all included in analysis
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n <- length(vec[vec == vr & !is.na(vec)])
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rt <- paste0(n, " (", round(n/nall * 100, 0), "%)")
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## Counting all included in analysis with outcome
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lvl<-levels(dat2[,1])[2]
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no <- length(vec[vec == vr & dat2[,1]==lvl & !is.na(vec)])
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ro <- paste0(no, " (", round(no/n * 100, 0), "%)")
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## Combining
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nq <- rbind(nq, cbind(paste0(ns, levels(vec)[r]), rt,ro))
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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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n <- length(num[!is.na(num)])
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rt <- paste0(n, " (", round(n/nall * 100, 0), "%)")
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nq <- rbind(nq, cbind(names(dat2)[i], rt,ro="-"))
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}
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}
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}
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else {
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dat2<-dat[!is.na(dat[,1]),]
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for (i in 2: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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## Counting all included in analysis
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n <- length(vec[vec == vr & !is.na(vec)])
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rt <- paste0(n, " (", round(n/nall * 100, 0), "%)")
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## Counting all included in analysis with outcome
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lvl<-levels(dat2[,1])[2]
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no <- length(vec[vec == vr & dat2[,1]==lvl & !is.na(vec)])
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ro <- paste0(no, " (", round(no/n * 100, 0), "%)")
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## Combining
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nq <- rbind(nq, cbind(paste0(ns, levels(vec)[r]), rt,ro))
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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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n <- length(num[!is.na(num)])
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rt <- paste0(n, " (", round(n/nall * 100, 0), "%)")
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nq <- rbind(nq, cbind(names(dat2)[i], rt,ro="-"))
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}
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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[,1],N=nq[,2],N.out=nq[,3],stringsAsFactors = F)
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namt<-data.frame(names=tail(rnames,-3),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(paste0("Chance of ",meas," is ",levels(m)[2]),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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rona<-c()
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for (i in 1:length(ads)){
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if (is.factor(ads[,i])){
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rona<-c(rona,names(ads[i]),levels(ads[,i]))}
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if (!is.factor(ads[,i])){
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rona<-c(rona,names(ads[i]),"Per unit increase")
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}
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}
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if (p.val==TRUE){
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ref<-data.frame(c(NA,rona),re[,"N"],re[,"N.out"],re[,"or_ci"],re[,"pv"],re[,"aor_ci"],re[,"apv"])
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names(ref)<-c("Variable",paste0("N=",nall),paste0("N, ",meas," is ",levels(m)[2]),"Crude OR (95 % CI)","p-value","Mutually adjusted OR (95 % CI)","A p-value")
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}
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else{
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ref<-data.frame(c(NA,rona),re[,"N"],re[,"N.out"],re[,"or_ci"],re[,"aor_ci"])
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names(ref)<-c("Variable",paste0("N=",nall),paste0("N, ",meas," is ",levels(m)[2]),"Crude OR (95 % CI)","Mutually adjusted OR (95 % CI)")
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}
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ls<-list(tbl=ref,miss,nall,nrow(d))
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names(ls)<-c("Printable table","Deleted due to missingness in adjusted analysis","Number of outcome observations","Length of dataframe")
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}
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if(!is.factor(m)){
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dfcr<-data.frame(matrix(NA,ncol = 3))
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names(dfcr)<-c("pred","dif_ci","pv")
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n.mn<-c()
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nref<-c()
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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<-lm(m~.,data=dat)
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n.mn<-c(n.mn,nrow(mn$model))
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suppressMessages(ci<-confint(mn))
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l<-round(ci[-1,1],dec)
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u<-round(ci[-1,2],dec)
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dif<-round(coef(mn)[-1],dec)
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dif_ci<-paste0(dif," (",l," to ",u,")")
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pv<-round(tidy(mn)$p.value[-1],dec+1)
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pv<-ifelse(pv<0.001,"<0.001",round(pv,3))
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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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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,dif_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 <- lm(m ~ ., data = dat)
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miss<-length(ma$na.action)
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actable <- coef(summary(ma))
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pa <- actable[,4]
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pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
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pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
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ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
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apv<-pa[1:length(coef(ma))]
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aco<-round(coef(ma),dec)
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suppressMessages(aci<-round(confint(ma),dec))
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alo<-aci[,1]
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aup<-aci[,2]
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amean_ci<-paste0(aco," (",alo," to ",aup,")")
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mean_est<-amean_ci[[1]]
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nq<-c()
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nall<-length(!is.na(dat[,1]))
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if (n.by.adj==TRUE){
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dat2<-ma$model[,-1]
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# nalt<-nrow(dat2)
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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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n<-length(vec[vec==vr&!is.na(vec)])
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rt<-paste0(n," (",round(n/nall*100,0),"%)")
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nq<-rbind(nq,cbind(paste0(ns,levels(vec)[r]),rt))
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}}
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if (!is.factor(dat2[,i])){
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num<-dat2[,i]
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n<-as.numeric(length(num[!is.na(num)]))
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rt<-paste0(n," (",round(n/nall*100,0),"%)")
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nq<-rbind(nq,cbind(names(dat2)[i],rt))
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}}
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}
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else {
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dat2<-dat[!is.na(dat[,1]),][,-1]
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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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n <- length(vec[vec == vr & !is.na(vec)])
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rt <- paste0(n, " (", round(n/nall * 100, 0), "%)")
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nq <- rbind(nq, cbind(paste0(ns, levels(vec)[r]), 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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n <- length(num[!is.na(num)])
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rt <- paste0(n, " (", round(n/nall * 100, 0), "%)")
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nq <- rbind(nq, cbind(names(dat2)[i], rt))
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}
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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(amean_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[,1],N=nq[,2],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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rona<-c()
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for (i in 1:length(ads)){
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if (is.factor(ads[,i])){
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rona<-c(rona,names(ads[i]),levels(ads[,i]))}
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if (!is.factor(ads[,i])){
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rona<-c(rona,names(ads[i]),"Per unit increase")
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}
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}
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if (p.val==TRUE){
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ref<-data.frame(c(NA,rona),re[,2],re[,5],re[,6],re[,3],re[,4])
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names(ref)<-c("Variable",paste0("N=",nall),"Difference (95 % CI)","p-value","Mutually adjusted difference (95 % CI)","A p-value")
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}
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else{
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ref<-data.frame(c(NA,rona),re[,2],re[,5],re[,3])
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names(ref)<-c("Variable",paste0("N=",nall),"Difference (95 % CI)","Mutually adjusted difference (95 % CI)")
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}
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ls<-list(tbl=ref,miss,nall,nrow(d),mean_est)
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names(ls)<-c("Printable table","Deleted due to missingness in adjusted analysis","Number of outcome observations","Length of dataframe","Estimated true mean (95 % CI) in adjusted analysis")
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}
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return(ls)
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}
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