From dc2d8985c11ae31565d8b9aadbf6dccbc4f418e5 Mon Sep 17 00:00:00 2001
From: agdamsbo <agdamsbo@pm.me>
Date: Wed, 13 Nov 2019 10:05:53 +0100
Subject: [PATCH] u

---
 DESCRIPTION        |  2 +-
 R/strobe_pred.R    | 21 ++++++++++++++-------
 man/strobe_pred.Rd |  2 +-
 3 files changed, 16 insertions(+), 9 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index abccea3..6f1f392 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -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. 
diff --git a/R/strobe_pred.R b/R/strobe_pred.R
index 4323e68..4004710 100644
--- a/R/strobe_pred.R
+++ b/R/strobe_pred.R
@@ -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)
diff --git a/man/strobe_pred.Rd b/man/strobe_pred.Rd
index 067e056..c0c46a8 100644
--- a/man/strobe_pred.Rd
+++ b/man/strobe_pred.Rd
@@ -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.