trying to fix warnings

R/rep_lm.R

@@ -9,15 +9,7 @@
 #' @keywords linear regression
 #' @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","v3")
-#'   rep_lm(meas="x",vars="y",string=preds,ci=F,data=d)
+#' rep_lm()
 
 rep_lm<-function(meas,vars,string,ci=FALSE,data){
 
@@ -26,12 +18,14 @@ rep_lm<-function(meas,vars,string,ci=FALSE,data){
   d<-data
   x<-data.frame(d[,c(string)])
   v<-data.frame(d[,c(vars)])
-  names(v)<-c(vars)
+
   y<-d[,c(meas)]
-  dt<-cbind(y,v)
+  dt<-cbind(y=y,v)
   m1<-length(coef(lm(y~.,data = dt)))
 
-  if (is.factor(y)){stop("Some kind of error message would be nice, but y should not be a factor!")}
+  names(v)<-c(vars)
+
+  if (is.factor(y)){stop("y should not be a factor!")}
 
   if (ci==TRUE){
 
@@ -42,8 +36,9 @@ rep_lm<-function(meas,vars,string,ci=FALSE,data){
       dat<-cbind(dt,x[,i])
       m<-lm(y~.,data=dat)
 
-      l<-suppressMessages(round(confint(m)[-c(1:m1),1],2))
-      u<-suppressMessages(round(confint(m)[-c(1:m1),2],2))
+      ci<-suppressMessages(confint(m))
+      l<-round(ci[-c(1:m1),1],2)
+      u<-round(ci[-c(1:m1),2],2)
       or<-round(coef(m)[-c(1:m1)],2)
       or_ci<-paste0(or," (",l," to ",u,")")
       pv<-round(tidy(m)$p.value[-c(1:m1)],3)
@@ -56,7 +51,7 @@ rep_lm<-function(meas,vars,string,ci=FALSE,data){
 
       df<-rbind(df,cbind(pred,or_ci,pv))}}
 
-  if (ci==FALSE){
+  else {
 
     df<-data.frame(matrix(NA,ncol = 3))
     names(df)<-c("pred","b","pv")
@@ -65,28 +60,20 @@ rep_lm<-function(meas,vars,string,ci=FALSE,data){
       dat<-cbind(dt,x[,i])
 
       m<-lm(y~.,data=dat)
-
       b<-round(coef(m)[-c(1:m1)],3)
-
       pv<-round(tidy(m)$p.value[-c(1:m1)],3)
-
       x1<-x[,i]
 
       if (is.factor(x1)){
         pred<-paste(names(x)[i],levels(x1)[-1],sep = "_")
       }
-
       else {pred<-names(x)[i]}
-
       df<-rbind(df,cbind(pred,b,pv))
-
     }}
 
   pa<-as.numeric(df[,3])
+  t <- ifelse(pa<=0.1,"include","drop")
   pa<-ifelse(pa<0.001,"<0.001",pa)
-
-  t <- ifelse(pa<=0.1|pa=="<0.001","include","drop")
-
   pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
                ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
 
@@ -94,14 +81,3 @@ rep_lm<-function(meas,vars,string,ci=FALSE,data){
 
   return(r)
 }
-
-
-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","v3")
-rep_lm(meas="x",vars="y",string=preds,ci=F,data=d)

R/strobe_diff_twodim.R

@@ -100,6 +100,6 @@ strobe_diff_twodim<-function(meas,group,adj,data,dec=2){
     ls<-list(logistic.regression=df)
   }
 
-  invisible(ls$adjustments<-dput(names(ads)))
+  ls$adjustments<-names(ads)
   return(ls)
 }

R/strobe_olr.R

@@ -12,11 +12,11 @@
 #' strobe_olr()
 
 strobe_olr<-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.
-  #'
+  ## 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(MASS)
   require(dplyr)
 

man/rep_lm.Rd

@@ -21,15 +21,7 @@ rep_lm(meas, vars, string, ci = FALSE, data)
 For bivariate analyses, to determine which variables to include in adjusted model.
 }
 \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","v3")
-  rep_lm(meas="x",vars="y",string=preds,ci=F,data=d)
+rep_lm()
 }
 \keyword{linear}
 \keyword{regression}

man/strobe_olr.Rd

@@ -20,5 +20,8 @@ strobe_olr(meas, var, adj, data, dec = 2)
 \description{
 Printable table of linear regression analysis of group vs var for meas. By group.
 }
+\examples{
+strobe_olr()
+}
 \keyword{olr}
 \keyword{strobe}

packing.R

@@ -0,0 +1,20 @@
+library(roxygen2)
+library(devtools)
+
+# Packing for publication
+
+setwd("/Users/andreas/Documents/GitHub/daDoctoR"); document()
+
+
+setwd(".."); install("daDoctoR")
+
+# Install from GitHub
+
+setwd("/"); devtools::install_github('agdamsbo/daDoctoR'); library(daDoctoR)
+
+"https://hilaryparker.com/2014/04/29/writing-an-r-package-from-scratch/"
+
+# Fixes
+
+remove.packages("daDoctoR"); .rs.restartR()
+