Updated print_log function to actually work..

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: daDoctoR
 Title: Functions For Health Research
-Version: 0.21.10
+Version: 0.21.11
 Year: 2021
 Author: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>
 Maintainer: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>

R/print_diff_bygroup.R

@@ -5,12 +5,12 @@
 #' Printable table of two dimensional regression analysis of group vs variable for outcome measure. By group. Includes p-value
 #' Group and variable has to be dichotomous factor.
 #' @param meas outcome measure variable name in data-data.frame as a string. Can be numeric or factor. Result is calculated accordingly.
-#' @param var binary exposure variable to compare against (active vs placebo). As string.
+#' @param var binary exposure variable to compare against (active vs placebo). As string. Horisontal.
-#' @param group binary group to compare, as string.
+#' @param group binary stratum to compare, as string. Vertical.
 #' @param adj variables to adjust for, as string.
 #' @param data dataframe to subset from.
 #' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1. pval has 3 decimals.
-#' @keywords strobe
+#' @keywords print stratum
 #' @export
 #' @examples
 #'   data('mtcars')

R/print_log.R

@@ -10,17 +10,23 @@
 #' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1.
 #' @keywords logistic
 #' @export
+#' @examples
+#'   ##Example with with sample data
+#'   sz=100
+#'   dta<-data.frame(out=factor(sample(c("yes","no"),sz,replace=TRUE)),variable=factor(sample(c("down","up"),sz,replace=TRUE)),sex=factor(sample(c("male","female"),sz,replace=TRUE,prob=c(0.6,0.4))),age=as.numeric(sample(18:80,sz,replace=TRUE)))
+#'   print_log(meas="out",var="variable",adj=c("sex","age"),data=dta,dec=2)
+
 
 print_log<-function(meas,var,adj,data,dec=2){
   ## Ønskeliste:
   ##
-  ## - Sum af alle, der indgår (Overall N)
   ## - Ryd op i kode, der der er overflødig %-regning, alternativt, så fiks at NA'er ikke skal regnes med.
   ##
 
   require(dplyr)
 
   d<-data
+
   m<-d[,c(meas)]
   v<-d[,c(var)]
 
@@ -34,7 +40,7 @@ print_log<-function(meas,var,adj,data,dec=2){
   ma <- glm(m ~ .,family = binomial(), data = dat)
 
   ctable <- coef(summary(mn))
-  pa <- ctable[, 4]
+  pa <- ctable[,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))
@@ -42,8 +48,8 @@ print_log<-function(meas,var,adj,data,dec=2){
 
   co<-round(exp(coef(mn)),dec)[-1]
   ci<-round(exp(confint(mn)),dec)[-1,]
-  lo<-ci[,1]
+  lo<-ci[1]
-  up<-ci[,2]
+  up<-ci[2]
 
   or_ci<-c("REF",paste0(co," (",lo," to ",up,")"))
 
@@ -132,10 +138,8 @@ print_log<-function(meas,var,adj,data,dec=2){
 
   ls$adjusted<-data.frame(rbind(header,coll))
 
-  fnames<-c("Variable","N","OR (95 % CI)","p value")
+  names(ls$unadjusted)<-c("Variable",paste0("N (n=",nrow(mn$model),")"),"OR (95 % CI)","p value")
-
+  names(ls$adjusted)<-c("Variable",paste0("N (n=",nrow(ma$model),")"),"OR (95 % CI)","p value")
-  names(ls$unadjusted)<-fnames
-  names(ls$adjusted)<-fnames
 
   return(ls)
 }

man/print_diff_bygroup.Rd

@@ -9,9 +9,9 @@ print_diff_bygroup(meas, var, group, adj, data, dec = 2)
 \arguments{
 \item{meas}{outcome measure variable name in data-data.frame as a string. Can be numeric or factor. Result is calculated accordingly.}
 
-\item{var}{binary exposure variable to compare against (active vs placebo). As string.}
+\item{var}{binary exposure variable to compare against (active vs placebo). As string. Horisontal.}
 
-\item{group}{binary group to compare, as string.}
+\item{group}{binary stratum to compare, as string. Vertical.}
 
 \item{adj}{variables to adjust for, as string.}
 
@@ -32,4 +32,5 @@ Group and variable has to be dichotomous factor.
   mtcars$am<-factor(mtcars$am)
   print_diff_bygroup(meas="mpg",var="vs",group = "am",adj=c("disp","wt"),data=mtcars)
 }
-\keyword{strobe}
+\keyword{print}
+\keyword{stratum}

man/print_log.Rd

@@ -23,4 +23,10 @@ New function ready for revision
 \details{
 Printable table of logistic regression analysis. Leaves out other variables from results.
 }
+\examples{
+  ##Example with with sample data
+  sz=100
+  dta<-data.frame(out=factor(sample(c("yes","no"),sz,replace=TRUE)),variable=factor(sample(c("down","up"),sz,replace=TRUE)),sex=factor(sample(c("male","female"),sz,replace=TRUE,prob=c(0.6,0.4))),age=as.numeric(sample(18:80,sz,replace=TRUE)))
+  print_log(meas="out",var="variable",adj=c("sex","age"),data=dta,dec=2)
+}
 \keyword{logistic}