diff --git a/DESCRIPTION b/DESCRIPTION
index 2eb2583..b67c3cb 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: daDoctoR
 Type: Package
 Title: FUNCTIONS FOR HEALTH RESEARCH
-Version: 0.1.0.9031
+Version: 0.1.0.9032
 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 b3a37bb..264e428 100644
--- a/R/strobe_pred.R
+++ b/R/strobe_pred.R
@@ -1,6 +1,9 @@
 #' Regression model of predictors according to STROBE, bi- and multivariate.
 #'
-#' 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.
+#' Printable table of regression model according to STROBE for linear or binary outcome-variables.
+#' 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.
 #' @param meas binary outcome meassure 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.
@@ -152,7 +155,7 @@ strobe_pred<-function(meas,adj,data,dec=2,n.by.adj=FALSE,p.val=FALSE){
 
   coll<-left_join(left_join(namt,numb,by="names"),rest,by="names")
 
-  header<-data.frame(matrix("Adjusted",ncol = ncol(coll)),stringsAsFactors = F)
+  header<-data.frame(matrix(paste0("Chance of ",meas," is ",levels(m)[-1]),ncol = ncol(coll)),stringsAsFactors = F)
   names(header)<-names(coll)
 
   df<-data.frame(rbind(header,coll),stringsAsFactors = F)
diff --git a/man/strobe_pred.Rd b/man/strobe_pred.Rd
index c0c46a8..b0e694b 100644
--- a/man/strobe_pred.Rd
+++ b/man/strobe_pred.Rd
@@ -21,6 +21,9 @@ strobe_pred(meas, adj, data, dec = 2, n.by.adj = FALSE,
 \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.
+Printable table of regression model according to STROBE for linear or binary outcome-variables.
+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.
 }
 \keyword{logistic}