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R/rep_glm.R

@@ -9,17 +9,7 @@
 #' @param fixed.var flag to set "vars" as fixed in the model. When FALSE, then true bivariate logistic regression is performed.
 #' @keywords logistic
 #' @export
-#' @examples
+#'
-#'   l<-50
-#'   y<-factor(rep(c("a","b"),l))
-#'   x<-rnorm(length(y), mean=50, sd=10)
-#'   v1<-factor(rep(c("r","s"),length(y)/2))
-#'   v2<-sample(1:100, length(y), replace=FALSE)
-#'   v3<-as.numeric(1:length(y))
-#'   d<-data.frame(y,x,v1,v2,v3)
-#'   preds<-c("v1","v2","x")
-#'   rep_glm(meas="y",vars="v3",string=preds,ci=F,data=d)
-
 
 rep_glm<-function(meas,vars=NULL,string,ci=FALSE,data,fixed.var=FALSE){
 

man/rep_glm.Rd

@@ -23,15 +23,4 @@ rep_glm(meas, vars = NULL, string, ci = FALSE, data,
 \description{
 For bivariate analyses. The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI.
 }
-\examples{
-  l<-50
-  y<-factor(rep(c("a","b"),l))
-  x<-rnorm(length(y), mean=50, sd=10)
-  v1<-factor(rep(c("r","s"),length(y)/2))
-  v2<-sample(1:100, length(y), replace=FALSE)
-  v3<-as.numeric(1:length(y))
-  d<-data.frame(y,x,v1,v2,v3)
-  preds<-c("v1","v2","x")
-  rep_glm(meas="y",vars="v3",string=preds,ci=F,data=d)
-}
 \keyword{logistic}