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agdamsbo 2019-11-13 10:05:53 +01:00
parent 3573b74a1b
commit dc2d8985c1
3 changed files with 16 additions and 9 deletions
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2
DESCRIPTION
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@ -1,7 +1,7 @@
Package: daDoctoR
Type: Package
Title: FUNCTIONS FOR HEALTH RESEARCH
Version: 0.1.0.9025
Version: 0.1.0.9026
Author: c(person("Andreas", "Gammelgaard Damsbo", email = "agdamsbo@pm.me", role = c("cre", "aut")))
Maintainer: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>
Description: I am a Danish medical doctor involved in neuropsychiatric research.

21
R/strobe_pred.R
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@ -6,7 +6,7 @@
#' @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 wether to count number of patients in adjusted model or overall for outcome meassure not NA.
#' @param p.val flag to include p-values in linear regression for now, set to FALSE as standard.
#' @param p.val flag to include p-values in table, set to FALSE as standard.
#' @keywords logistic
#' @export
@ -140,10 +140,10 @@ strobe_pred<-function(meas,adj,data,dec=2,n.by.adj=FALSE,p.val=FALSE){
for (i in 1:ncol(dat2)){
if (is.factor(dat2[,i])){
rnames<-c(rnames,names(dat2)[i],paste0(names(dat2)[i],levels(dat2[,i])))
rnames<-c(rnames,names(dat2)[i],levels(dat2[,i]))
}
if (!is.factor(dat2[,i])){
rnames<-c(rnames,paste0(names(dat2)[i],".all"),names(dat2)[i])
rnames<-c(rnames,names(dat2[i]),"Per unit increase")
}
}
res<-cbind(aor_ci,apv)
@ -163,9 +163,16 @@ strobe_pred<-function(meas,adj,data,dec=2,n.by.adj=FALSE,p.val=FALSE){
suppressWarnings(re<-left_join(df,dfcr,by="names"))
ref<-data.frame(re[,1],re[,2],re[,5],re[,3])
if (p.val==TRUE){
ref<-data.frame(re[,1],re[,2],re[,5],re[,6],re[,3],re[,4])
names(ref)<-c("Variable",paste0("N=",n.meas),"Crude OR (95 % CI)","Mutually adjusted OR (95 % CI)")
names(ref)<-c("Variable",paste0("N=",n.meas),"Crude OR (95 % CI)","p-value","Mutually adjusted OR (95 % CI)","A p-value")
}
else{
ref<-data.frame(re[,1],re[,2],re[,5],re[,3])
names(ref)<-c("Variable",paste0("N=",n.meas),"Crude OR (95 % CI)","Mutually adjusted OR (95 % CI)")
}
ls<-list(tbl=ref,miss,n.meas,nrow(d))
names(ls)<-c("Printable table","Deleted due to missingness in adjusted analysis","Number of outcome observations","Length of dataframe")
@ -297,10 +304,10 @@ strobe_pred<-function(meas,adj,data,dec=2,n.by.adj=FALSE,p.val=FALSE){
for (i in 1:ncol(dat2)){
if (is.factor(dat2[,i])){
rnames<-c(rnames,names(dat2)[i],paste0(names(dat2)[i],levels(dat2[,i])))
rnames<-c(rnames,names(dat2)[i],levels(dat2[,i]))
}
if (!is.factor(dat2[,i])){
rnames<-c(rnames,paste0(names(dat2)[i],".all"),names(dat2)[i])
rnames<-c(rnames,names(dat2[i]),"Per unit increase")
}
}
res<-cbind(amean_ci,apv)

2
man/strobe_pred.Rd
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@ -18,7 +18,7 @@ strobe_pred(meas, adj, data, dec = 2, n.by.adj = FALSE,
\item{n.by.adj}{flag to indicate wether to count number of patients in adjusted model or overall for outcome meassure not NA.}
\item{p.val}{flag to include p-values in linear regression for now, set to FALSE as standard.}
\item{p.val}{flag to include p-values in table, set to FALSE as standard.}
}
\description{
Printable table of regression model according to STROBE. Includes borth 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 continous outcome variable. Linear regression will give estimated adjusted true mean in list.