R/print_pred.R
In agdamsbo/daDoctoR: Functions For Health Research
Defines functions
print_pred
Documented in
print_pred
#' Regression model of predictors according to STROBE, bi- and multivariable. Printable result.
#'
#' Printable table of regression model according to STROBE for linear or binary outcome-variables.
#' Includes both bivariate and multivariate in the same table.
#' Output is a list, with the first item being the main "output" as a dataframe.
#' 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.
#' For logistic regression gives count of outcome variable pr variable level.
#' @param meas binary outcome measure variable, column name in data.frame as a string. Can be numeric or factor. Result is calculated accordingly.
#' @param adj variables to adjust for, as string.
#' @param data dataframe of data.
#' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1.
#' @param n.by.adj flag to indicate whether to count number of patients in adjusted model or overall for outcome measure not NA.
#' @param p.val flag to include p-values in table, set to FALSE as standard.
#' @keywords logistic
#' @export
print_pred<-function(meas,adj,data,dec=2,n.by.adj=FALSE,p.val=FALSE){
## Wish list:
## - SPEED, maybe flags to include/exclude time consuming tasks ??
## - Include ANOVA in output list, flag to include
require(dplyr)
require(broom)
d<-data
m<-d[,c(meas)]
ads<-d[,c(adj)]
if(is.factor(m)){
## Crude ORs
dfcr<-data.frame(matrix(NA,ncol = 3))
names(dfcr)<-c("pred","or_ci","pv")
n.mn<-c()
nref<-c()
for(i in 1:ncol(ads)){
dat<-data.frame(m=m,ads[,i])
names(dat)<-c("m",names(ads)[i])
mn<-glm(m~.,family = binomial(),data=dat)
n.mn<-c(n.mn,nrow(mn$model))
suppressMessages(ci<-exp(confint(mn)))
l<-round(ci[-1,1],dec)
u<-round(ci[-1,2],dec)
or<-round(exp(coef(mn))[-1],dec)
or_ci<-paste0(or," (",l," to ",u,")")
pv<-round(tidy(mn)$p.value[-1],dec+1)
x1<-ads[,i]
if (is.factor(x1)){
pred<-paste0(names(ads)[i],levels(x1)[-1])
}
else {
pred<-names(ads)[i]
}
dfcr<-rbind(dfcr,cbind(pred,or_ci,pv))
}
## Mutually adjusted ORs
dat<-data.frame(m=m,ads)
ma <- glm(m ~ .,family = binomial(), data = dat)
miss<-length(ma$na.action)
actable <- coef(summary(ma))
pa <- actable[,4]
pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
apv<-pa[1:length(coef(ma))]
aco<-round(exp(coef(ma)),dec)
suppressMessages(aci<-round(exp(confint(ma)),dec))
alo<-aci[,1]
aup<-aci[,2]
aor_ci<-paste0(aco," (",alo," to ",aup,")")
# names(dat2)<-c(var,names(ads))
nq<-c()
nall<-length(!is.na(dat[,1]))
nalt<-c()
if (n.by.adj==TRUE){
dat2<-ma$model
nalt<-nrow(dat2)
for (i in 2:ncol(dat2)) {
if (is.factor(dat2[, i])) {
vec <- dat2[, i]
ns <- names(dat2)[i]
for (r in 1:length(levels(vec))) {
vr <- levels(vec)[r]
## Counting all included in analysis
n <- length(vec[vec == vr & !is.na(vec)])
rt <- paste0(n, " (", round(n/nalt * 100, 0), "%)")
## Counting all included in analysis with outcome
lvl<-levels(dat2[,1])[2]
no <- length(vec[vec == vr & dat2[,1]==lvl & !is.na(vec)])
ro <- paste0(no, " (", round(no/n * 100, 0), "%)")
## Combining
nq <- rbind(nq, cbind(paste0(ns, levels(vec)[r]), rt,ro))
}
}
if (!is.factor(dat2[, i])) {
num <- dat2[, i]
n <- length(num[!is.na(num)])
rt <- paste0(n, " (", round(n/nalt * 100, 0), "%)")
nq <- rbind(nq, cbind(names(dat2)[i], rt,ro="-"))
}
}
}
else {
dat2<-dat[!is.na(dat[,1]),]
nalt<-nall
for (i in 2:ncol(dat2)) {
if (is.factor(dat2[, i])) {
vec <- dat2[, i]
ns <- names(dat2)[i]
for (r in 1:length(levels(vec))) {
vr <- levels(vec)[r]
## Counting all included in analysis
n <- length(vec[vec == vr & !is.na(vec)])
rt <- paste0(n, " (", round(n/nalt * 100, 0), "%)")
## Counting all included in analysis with outcome
lvl<-levels(dat2[,1])[2]
no <- length(vec[vec == vr & dat2[,1]==lvl & !is.na(vec)])
ro <- paste0(no, " (", round(no/n * 100, 0), "%)")
## Combining
nq <- rbind(nq, cbind(paste0(ns, levels(vec)[r]), rt,ro))
}
}
if (!is.factor(dat2[, i])) {
num <- dat2[, i]
n <- length(num[!is.na(num)])
rt <- paste0(n, " (", round(n/nalt * 100, 0), "%)")
nq <- rbind(nq, cbind(names(dat2)[i], rt,ro="-"))
}
}
}
rnames<-c()
for (i in 1:ncol(dat2)){
if (is.factor(dat2[,i])){
rnames<-c(rnames,names(dat2)[i],paste0(names(dat2)[i],levels(dat2[,i])))
}
if (!is.factor(dat2[,i])){
rnames<-c(rnames,paste0(names(dat2)[i],".all"),names(dat2)[i])
}
}
res<-cbind(aor_ci,apv)
rest<-data.frame(names=row.names(res),res,stringsAsFactors = F)
numb<-data.frame(names=nq[,1],N=nq[,2],N.out=nq[,3],stringsAsFactors = F)
namt<-data.frame(names=tail(rnames,-3),stringsAsFactors = F)
coll<-left_join(left_join(namt,numb,by="names"),rest,by="names")
header<-data.frame(matrix(paste0("Chance of ",meas," is ",levels(m)[2]),ncol = ncol(coll)),stringsAsFactors = F)
names(header)<-names(coll)
df<-data.frame(rbind(header,coll),stringsAsFactors = F)
names(dfcr)[1]<-c("names")
suppressWarnings(re<-left_join(df,dfcr,by="names"))
rona<-c()
for (i in 1:length(ads)){
if (is.factor(ads[,i])){
rona<-c(rona,names(ads[i]),levels(ads[,i]))}
if (!is.factor(ads[,i])){
rona<-c(rona,names(ads[i]),"Per unit increase")
}
}
if (p.val==TRUE){
ref<-data.frame(c(NA,rona),re[,"N"],re[,"N.out"],re[,"or_ci"],re[,"pv"],re[,"aor_ci"],re[,"apv"])
names(ref)<-c("Variable",paste0("N=",nalt),paste0("N, ",meas," is ",levels(m)[2]),"Crude OR (95 % CI)","p-value","Mutually adjusted OR (95 % CI)","A p-value")
}
else{
ref<-data.frame(c(NA,rona),re[,"N"],re[,"N.out"],re[,"or_ci"],re[,"aor_ci"])
names(ref)<-c("Variable",paste0("N=",nalt),paste0("N, ",meas," is ",levels(m)[2]),"Crude OR (95 % CI)","Mutually adjusted OR (95 % CI)")
}
ls<-list(tbl=ref,miss,nall,nrow(d))
names(ls)<-c("Printable table","Deleted due to missingness in adjusted analysis","Number of outcome observations","Length of dataframe")
}
if(!is.factor(m)){
dfcr<-data.frame(matrix(NA,ncol = 3))
names(dfcr)<-c("pred","dif_ci","pv")
n.mn<-c()
nref<-c()
for(i in 1:ncol(ads)){
dat<-data.frame(m=m,ads[,i])
names(dat)<-c("m",names(ads)[i])
mn<-lm(m~.,data=dat)
n.mn<-c(n.mn,nrow(mn$model))
suppressMessages(ci<-confint(mn))
l<-round(ci[-1,1],dec)
u<-round(ci[-1,2],dec)
dif<-round(coef(mn)[-1],dec)
dif_ci<-paste0(dif," (",l," to ",u,")")
pv<-round(tidy(mn)$p.value[-1],dec+1)
pv<-ifelse(pv<0.001,"<0.001",round(pv,3))
pv <- ifelse(pv<=0.05|pv=="<0.001",paste0("*",pv),
ifelse(pv>0.05&pv<=0.1,paste0(".",pv),pv))
x1<-ads[,i]
if (is.factor(x1)){
pred<-paste0(names(ads)[i],levels(x1)[-1])
}
else {
pred<-names(ads)[i]
}
dfcr<-rbind(dfcr,cbind(pred,dif_ci,pv))
}
## Mutually adjusted ORs
dat<-data.frame(m=m,ads)
ma <- lm(m ~ ., data = dat)
miss<-length(ma$na.action)
actable <- coef(summary(ma))
pa <- actable[,4]
pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
apv<-pa[1:length(coef(ma))]
aco<-round(coef(ma),dec)
suppressMessages(aci<-round(confint(ma),dec))
alo<-aci[,1]
aup<-aci[,2]
amean_ci<-paste0(aco," (",alo," to ",aup,")")
mean_est<-amean_ci[[1]]
nq<-c()
nall<-length(!is.na(dat[,1]))
nalt<-c()
if (n.by.adj==TRUE){
dat2<-ma$model[,-1]
nalt<-nrow(dat2)
for (i in 1:ncol(dat2)){
if (is.factor(dat2[,i])){
vec<-dat2[,i]
ns<-names(dat2)[i]
for (r in 1:length(levels(vec))){
vr<-levels(vec)[r]
n<-length(vec[vec==vr&!is.na(vec)])
rt<-paste0(n," (",round(n/nalt*100,0),"%)")
nq<-rbind(nq,cbind(paste0(ns,levels(vec)[r]),rt))
}}
if (!is.factor(dat2[,i])){
num<-dat2[,i]
n<-as.numeric(length(num[!is.na(num)]))
rt<-paste0(n," (",round(n/nalt*100,0),"%)")
nq<-rbind(nq,cbind(names(dat2)[i],rt))
}}
}
else {
dat2<-dat[!is.na(dat[,1]),][,-1]
nalt<-nall
for (i in 1:ncol(dat2)) {
if (is.factor(dat2[, i])) {
vec <- dat2[, i]
ns <- names(dat2)[i]
for (r in 1:length(levels(vec))) {
vr <- levels(vec)[r]
n <- length(vec[vec == vr & !is.na(vec)])
rt <- paste0(n, " (", round(n/nalt * 100, 0), "%)")
nq <- rbind(nq, cbind(paste0(ns, levels(vec)[r]), rt))
}
}
if (!is.factor(dat2[, i])) {
num <- dat2[, i]
n <- length(num[!is.na(num)])
rt <- paste0(n, " (", round(n/nalt * 100, 0), "%)")
nq <- rbind(nq, cbind(names(dat2)[i], rt))
}
}
}
rnames<-c()
for (i in 1:ncol(dat2)){
if (is.factor(dat2[,i])){
rnames<-c(rnames,names(dat2)[i],paste0(names(dat2)[i],levels(dat2[,i])))
}
if (!is.factor(dat2[,i])){
rnames<-c(rnames,paste0(names(dat2)[i],".all"),names(dat2)[i])
}
}
res<-cbind(amean_ci,apv)
rest<-data.frame(names=row.names(res),res,stringsAsFactors = F)
numb<-data.frame(names=nq[,1],N=nq[,2],stringsAsFactors = F)
namt<-data.frame(names=rnames,stringsAsFactors = F)
coll<-left_join(left_join(namt,numb,by="names"),rest,by="names")
header<-data.frame(matrix("Adjusted",ncol = ncol(coll)),stringsAsFactors = F)
names(header)<-names(coll)
df<-data.frame(rbind(header,coll),stringsAsFactors = F)
names(dfcr)[1]<-c("names")
suppressWarnings(re<-left_join(df,dfcr,by="names"))
rona<-c()
for (i in 1:length(ads)){
if (is.factor(ads[,i])){
rona<-c(rona,names(ads[i]),levels(ads[,i]))}
if (!is.factor(ads[,i])){
rona<-c(rona,names(ads[i]),"Per unit increase")
}
}
if (p.val==TRUE){
ref<-data.frame(c(NA,rona),re[,2],re[,5],re[,6],re[,3],re[,4])
names(ref)<-c("Variable",paste0("N=",nalt),"Difference (95 % CI)","p-value","Mutually adjusted difference (95 % CI)","A p-value")
}
else{
ref<-data.frame(c(NA,rona),re[,2],re[,5],re[,3])
names(ref)<-c("Variable",paste0("N=",nalt),"Difference (95 % CI)","Mutually adjusted difference (95 % CI)")
}
ls<-list(tbl=ref,miss,nall,nrow(d),mean_est)
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")
}
return(ls)
}
agdamsbo/daDoctoR documentation built on Aug. 30, 2022, 4:29 p.m.
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Defines functions print_pred
Documented in print_pred
#' Regression model of predictors according to STROBE, bi- and multivariable. Printable result.
#'
#' Printable table of regression model according to STROBE for linear or binary outcome-variables.
#' Includes both bivariate and multivariate in the same table.
#' Output is a list, with the first item being the main "output" as a dataframe.
#' 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.
#' For logistic regression gives count of outcome variable pr variable level.
#' @param meas binary outcome measure variable, column name in data.frame as a string. Can be numeric or factor. Result is calculated accordingly.
#' @param adj variables to adjust for, as string.
#' @param data dataframe of data.
#' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1.
#' @param n.by.adj flag to indicate whether to count number of patients in adjusted model or overall for outcome measure not NA.
#' @param p.val flag to include p-values in table, set to FALSE as standard.
#' @keywords logistic
#' @export
print_pred<-function(meas,adj,data,dec=2,n.by.adj=FALSE,p.val=FALSE){
## Wish list:
## - SPEED, maybe flags to include/exclude time consuming tasks ??
## - Include ANOVA in output list, flag to include
require(dplyr)
require(broom)
d<-data
m<-d[,c(meas)]
ads<-d[,c(adj)]
if(is.factor(m)){
## Crude ORs
dfcr<-data.frame(matrix(NA,ncol = 3))
names(dfcr)<-c("pred","or_ci","pv")
n.mn<-c()
nref<-c()
for(i in 1:ncol(ads)){
dat<-data.frame(m=m,ads[,i])
names(dat)<-c("m",names(ads)[i])
mn<-glm(m~.,family = binomial(),data=dat)
n.mn<-c(n.mn,nrow(mn$model))
suppressMessages(ci<-exp(confint(mn)))
l<-round(ci[-1,1],dec)
u<-round(ci[-1,2],dec)
or<-round(exp(coef(mn))[-1],dec)
or_ci<-paste0(or," (",l," to ",u,")")
pv<-round(tidy(mn)$p.value[-1],dec+1)
x1<-ads[,i]
if (is.factor(x1)){
pred<-paste0(names(ads)[i],levels(x1)[-1])
}
else {
pred<-names(ads)[i]
}
dfcr<-rbind(dfcr,cbind(pred,or_ci,pv))
}
## Mutually adjusted ORs
dat<-data.frame(m=m,ads)
ma <- glm(m ~ .,family = binomial(), data = dat)
miss<-length(ma$na.action)
actable <- coef(summary(ma))
pa <- actable[,4]
pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
apv<-pa[1:length(coef(ma))]
aco<-round(exp(coef(ma)),dec)
suppressMessages(aci<-round(exp(confint(ma)),dec))
alo<-aci[,1]
aup<-aci[,2]
aor_ci<-paste0(aco," (",alo," to ",aup,")")
# names(dat2)<-c(var,names(ads))
nq<-c()
nall<-length(!is.na(dat[,1]))
nalt<-c()
if (n.by.adj==TRUE){
dat2<-ma$model
nalt<-nrow(dat2)
for (i in 2:ncol(dat2)) {
if (is.factor(dat2[, i])) {
vec <- dat2[, i]
ns <- names(dat2)[i]
for (r in 1:length(levels(vec))) {
vr <- levels(vec)[r]
## Counting all included in analysis
n <- length(vec[vec == vr & !is.na(vec)])
rt <- paste0(n, " (", round(n/nalt * 100, 0), "%)")
## Counting all included in analysis with outcome
lvl<-levels(dat2[,1])[2]
no <- length(vec[vec == vr & dat2[,1]==lvl & !is.na(vec)])
ro <- paste0(no, " (", round(no/n * 100, 0), "%)")
## Combining
nq <- rbind(nq, cbind(paste0(ns, levels(vec)[r]), rt,ro))
}
}
if (!is.factor(dat2[, i])) {
num <- dat2[, i]
n <- length(num[!is.na(num)])
rt <- paste0(n, " (", round(n/nalt * 100, 0), "%)")
nq <- rbind(nq, cbind(names(dat2)[i], rt,ro="-"))
}
}
}
else {
dat2<-dat[!is.na(dat[,1]),]
nalt<-nall
for (i in 2:ncol(dat2)) {
if (is.factor(dat2[, i])) {
vec <- dat2[, i]
ns <- names(dat2)[i]
for (r in 1:length(levels(vec))) {
vr <- levels(vec)[r]
## Counting all included in analysis
n <- length(vec[vec == vr & !is.na(vec)])
rt <- paste0(n, " (", round(n/nalt * 100, 0), "%)")
## Counting all included in analysis with outcome
lvl<-levels(dat2[,1])[2]
no <- length(vec[vec == vr & dat2[,1]==lvl & !is.na(vec)])
ro <- paste0(no, " (", round(no/n * 100, 0), "%)")
## Combining
nq <- rbind(nq, cbind(paste0(ns, levels(vec)[r]), rt,ro))
}
}
if (!is.factor(dat2[, i])) {
num <- dat2[, i]
n <- length(num[!is.na(num)])
rt <- paste0(n, " (", round(n/nalt * 100, 0), "%)")
nq <- rbind(nq, cbind(names(dat2)[i], rt,ro="-"))
}
}
}
rnames<-c()
for (i in 1:ncol(dat2)){
if (is.factor(dat2[,i])){
rnames<-c(rnames,names(dat2)[i],paste0(names(dat2)[i],levels(dat2[,i])))
}
if (!is.factor(dat2[,i])){
rnames<-c(rnames,paste0(names(dat2)[i],".all"),names(dat2)[i])
}
}
res<-cbind(aor_ci,apv)
rest<-data.frame(names=row.names(res),res,stringsAsFactors = F)
numb<-data.frame(names=nq[,1],N=nq[,2],N.out=nq[,3],stringsAsFactors = F)
namt<-data.frame(names=tail(rnames,-3),stringsAsFactors = F)
coll<-left_join(left_join(namt,numb,by="names"),rest,by="names")
header<-data.frame(matrix(paste0("Chance of ",meas," is ",levels(m)[2]),ncol = ncol(coll)),stringsAsFactors = F)
names(header)<-names(coll)
df<-data.frame(rbind(header,coll),stringsAsFactors = F)
names(dfcr)[1]<-c("names")
suppressWarnings(re<-left_join(df,dfcr,by="names"))
rona<-c()
for (i in 1:length(ads)){
if (is.factor(ads[,i])){
rona<-c(rona,names(ads[i]),levels(ads[,i]))}
if (!is.factor(ads[,i])){
rona<-c(rona,names(ads[i]),"Per unit increase")
}
}
if (p.val==TRUE){
ref<-data.frame(c(NA,rona),re[,"N"],re[,"N.out"],re[,"or_ci"],re[,"pv"],re[,"aor_ci"],re[,"apv"])
names(ref)<-c("Variable",paste0("N=",nalt),paste0("N, ",meas," is ",levels(m)[2]),"Crude OR (95 % CI)","p-value","Mutually adjusted OR (95 % CI)","A p-value")
}
else{
ref<-data.frame(c(NA,rona),re[,"N"],re[,"N.out"],re[,"or_ci"],re[,"aor_ci"])
names(ref)<-c("Variable",paste0("N=",nalt),paste0("N, ",meas," is ",levels(m)[2]),"Crude OR (95 % CI)","Mutually adjusted OR (95 % CI)")
}
ls<-list(tbl=ref,miss,nall,nrow(d))
names(ls)<-c("Printable table","Deleted due to missingness in adjusted analysis","Number of outcome observations","Length of dataframe")
}
if(!is.factor(m)){
dfcr<-data.frame(matrix(NA,ncol = 3))
names(dfcr)<-c("pred","dif_ci","pv")
n.mn<-c()
nref<-c()
for(i in 1:ncol(ads)){
dat<-data.frame(m=m,ads[,i])
names(dat)<-c("m",names(ads)[i])
mn<-lm(m~.,data=dat)
n.mn<-c(n.mn,nrow(mn$model))
suppressMessages(ci<-confint(mn))
l<-round(ci[-1,1],dec)
u<-round(ci[-1,2],dec)
dif<-round(coef(mn)[-1],dec)
dif_ci<-paste0(dif," (",l," to ",u,")")
pv<-round(tidy(mn)$p.value[-1],dec+1)
pv<-ifelse(pv<0.001,"<0.001",round(pv,3))
pv <- ifelse(pv<=0.05|pv=="<0.001",paste0("*",pv),
ifelse(pv>0.05&pv<=0.1,paste0(".",pv),pv))
x1<-ads[,i]
if (is.factor(x1)){
pred<-paste0(names(ads)[i],levels(x1)[-1])
}
else {
pred<-names(ads)[i]
}
dfcr<-rbind(dfcr,cbind(pred,dif_ci,pv))
}
## Mutually adjusted ORs
dat<-data.frame(m=m,ads)
ma <- lm(m ~ ., data = dat)
miss<-length(ma$na.action)
actable <- coef(summary(ma))
pa <- actable[,4]
pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
apv<-pa[1:length(coef(ma))]
aco<-round(coef(ma),dec)
suppressMessages(aci<-round(confint(ma),dec))
alo<-aci[,1]
aup<-aci[,2]
amean_ci<-paste0(aco," (",alo," to ",aup,")")
mean_est<-amean_ci[[1]]
nq<-c()
nall<-length(!is.na(dat[,1]))
nalt<-c()
if (n.by.adj==TRUE){
dat2<-ma$model[,-1]
nalt<-nrow(dat2)
for (i in 1:ncol(dat2)){
if (is.factor(dat2[,i])){
vec<-dat2[,i]
ns<-names(dat2)[i]
for (r in 1:length(levels(vec))){
vr<-levels(vec)[r]
n<-length(vec[vec==vr&!is.na(vec)])
rt<-paste0(n," (",round(n/nalt*100,0),"%)")
nq<-rbind(nq,cbind(paste0(ns,levels(vec)[r]),rt))
}}
if (!is.factor(dat2[,i])){
num<-dat2[,i]
n<-as.numeric(length(num[!is.na(num)]))
rt<-paste0(n," (",round(n/nalt*100,0),"%)")
nq<-rbind(nq,cbind(names(dat2)[i],rt))
}}
}
else {
dat2<-dat[!is.na(dat[,1]),][,-1]
nalt<-nall
for (i in 1:ncol(dat2)) {
if (is.factor(dat2[, i])) {
vec <- dat2[, i]
ns <- names(dat2)[i]
for (r in 1:length(levels(vec))) {
vr <- levels(vec)[r]
n <- length(vec[vec == vr & !is.na(vec)])
rt <- paste0(n, " (", round(n/nalt * 100, 0), "%)")
nq <- rbind(nq, cbind(paste0(ns, levels(vec)[r]), rt))
}
}
if (!is.factor(dat2[, i])) {
num <- dat2[, i]
n <- length(num[!is.na(num)])
rt <- paste0(n, " (", round(n/nalt * 100, 0), "%)")
nq <- rbind(nq, cbind(names(dat2)[i], rt))
}
}
}
rnames<-c()
for (i in 1:ncol(dat2)){
if (is.factor(dat2[,i])){
rnames<-c(rnames,names(dat2)[i],paste0(names(dat2)[i],levels(dat2[,i])))
}
if (!is.factor(dat2[,i])){
rnames<-c(rnames,paste0(names(dat2)[i],".all"),names(dat2)[i])
}
}
res<-cbind(amean_ci,apv)
rest<-data.frame(names=row.names(res),res,stringsAsFactors = F)
numb<-data.frame(names=nq[,1],N=nq[,2],stringsAsFactors = F)
namt<-data.frame(names=rnames,stringsAsFactors = F)
coll<-left_join(left_join(namt,numb,by="names"),rest,by="names")
header<-data.frame(matrix("Adjusted",ncol = ncol(coll)),stringsAsFactors = F)
names(header)<-names(coll)
df<-data.frame(rbind(header,coll),stringsAsFactors = F)
names(dfcr)[1]<-c("names")
suppressWarnings(re<-left_join(df,dfcr,by="names"))
rona<-c()
for (i in 1:length(ads)){
if (is.factor(ads[,i])){
rona<-c(rona,names(ads[i]),levels(ads[,i]))}
if (!is.factor(ads[,i])){
rona<-c(rona,names(ads[i]),"Per unit increase")
}
}
if (p.val==TRUE){
ref<-data.frame(c(NA,rona),re[,2],re[,5],re[,6],re[,3],re[,4])
names(ref)<-c("Variable",paste0("N=",nalt),"Difference (95 % CI)","p-value","Mutually adjusted difference (95 % CI)","A p-value")
}
else{
ref<-data.frame(c(NA,rona),re[,2],re[,5],re[,3])
names(ref)<-c("Variable",paste0("N=",nalt),"Difference (95 % CI)","Mutually adjusted difference (95 % CI)")
}
ls<-list(tbl=ref,miss,nall,nrow(d),mean_est)
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")
}
return(ls)
}
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