fix?

R/cie_test.R

@@ -160,6 +160,6 @@ df<-cbind(df,t)
        t<-c(NA,ifelse(abs(e-as.numeric(df[-1,2]))>=(e*cut),"include","drop"))
 df<-cbind(df,t)
 }}
-
+result<-df
     return(df)
 }

R/rep_biv.R

@@ -34,5 +34,5 @@ if (me=="pval"&log==TRUE){
 if (method=="cie"){
   daDoctoR::cie_test(y=a,v1=b,string=s,data=dat,logistic=log,cut=ct)
 }
-return(df)
+return(result)
 }

R/rep_glm.R

@@ -1,4 +1,4 @@
-#' A repeated logistic regression function 
+#' A repeated logistic regression function
 #'
 #' For bivariate analyses.
 #' @param y Effect meassure.
@@ -12,80 +12,80 @@ rep_glm<-function(y,v1,string,ci=FALSE,data,v2=NULL,v3=NULL){
 ## x is data.frame of predictors, y is vector of an aoutcome as a factor
 ## output is returned as coefficient, or if or=TRUE as OR with 95 % CI.
 ## The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI.
-  
+
   require(broom)
-  
+
   d<-data
   x<-select(d,one_of(c(string)))
   m1<-length(coef(glm(y~v1,family = binomial())))
-  
+
 if (!is.factor(y)){stop("Some kind of error message would be nice, but y should be a factor!")}
-  
+
     if (ci==TRUE){
-      
+
              df<-data.frame(matrix(ncol = 4))
   names(df)<-c("pred","or_ci","pv","t")
-      
+
       for(i in 1:ncol(x)){
      m<-glm(y~v1+x[,i],family = binomial())
-     
+
      l<-suppressMessages(round(exp(confint(m))[-c(1:m1),1],2))
      u<-suppressMessages(round(exp(confint(m))[-c(1:m1),2],2))
      or<-round(exp(coef(m))[-c(1:m1)],2)
-     
+
      or_ci<-paste0(or," (",l," to ",u,")")
-     
+
      pv<-round(tidy(m)$p.value[-c(1:m1)],3)
      pv<-ifelse(pv<0.001,"<0.001",pv)
-        
+
          t <- ifelse(pv<=0.1|pv=="<0.001","include","drop")
-        
+
      pv <- ifelse(pv<=0.05|pv=="<0.001",paste0("*",pv),
                   ifelse(pv>0.05&pv<=0.1,paste0(".",pv),pv))
 
-     
+
     v<-x[,i]
-     
+
     if (is.factor(v)){
        pred<-paste(names(x)[i],levels(v)[-1],sep = "_")
     }
-       
+
        else {pred<-names(x)[i]}
-      
+
       df<-rbind(df,cbind(pred,or_ci,pv,t))
-             
+
     }}
-    
+
   if (ci==FALSE){
-     
+
        df<-data.frame(matrix(ncol = 4))
   names(df)<-c("pred","b","pv","t")
-     
+
   for(i in 1:ncol(x)){
      m<-glm(y~v1+x[,i],family = binomial())
-     
+
      b<-round(coef(m)[-c(1:m1)],3)
-     
+
      pv<-round(tidy(m)$p.value[-c(1:m1)],3)
      pv<-ifelse(pv<0.001,"<0.001",pv)
-    
+
     t <- ifelse(pv<=0.1|pv=="<0.001","include","drop")
-    
+
      pv <- ifelse(pv<=0.05|pv=="<0.001",paste0("*",pv),
                   ifelse(pv>0.05&pv<=0.1,paste0(".",pv),pv))
-    
+
-     
+
      v<-x[,i]
-     
+
      if (is.factor(v)){
        pred<-paste(names(x)[i],levels(v)[-1],sep = "_")
      }
-       
+
        else {pred<-names(x)[i]}
-     
+
      df<-rbind(df,cbind(pred,b,pv,t))
-    
+
   }}
-  
+  result<-df
   return(df)
 }

R/rep_lm.R

@@ -1,4 +1,4 @@
-#' A repeated linear regression function 
+#' A repeated linear regression function
 #'
 #' For bivariate analyses.
 #' @param y Effect meassure.
@@ -7,83 +7,83 @@
 #' @export
 #' @examples
 #' rep_lm()
- 
+
 rep_lm<-function(y,v1,string,ci=FALSE,data,v2=NULL,v3=NULL){
 ## x is data.frame of predictors, y is vector of an aoutcome as a factor
 ## output is returned as coefficient, or if ci=TRUE as coefficient with 95 % CI.
 ## The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI.
-  
+
   require(broom)
-  
+
   d<-data
   x<-select(d,one_of(c(string)))
   m1<-length(coef(lm(y~v1)))
-  
+
 if (is.factor(y)){stop("Some kind of error message would be nice, but y should not be a factor!")}
-  
+
     if (ci==TRUE){
-      
+
              df<-data.frame(matrix(ncol = 4))
   names(df)<-c("pred","co_ci","pv","t")
-      
+
       for(i in 1:ncol(x)){
      m<-lm(y~v1+x[,i])
-     
+
      l<-suppressMessages(round(confint(m)[-c(1:m1),1],2))
      u<-suppressMessages(round(confint(m)[-c(1:m1),2],2))
      co<-round(coef(m)[-c(1:m1)],2)
-     
+
      co_ci<-paste0(co," (",l," to ",u,")")
-     
+
      pv<-round(tidy(m)$p.value[-c(1:m1)],3)
      pv<-ifelse(pv<0.001,"<0.001",pv)
-        
+
         t <- ifelse(pv<=0.1|pv=="<0.001","include","drop")
-        
+
      pv <- ifelse(pv<=0.05|pv=="<0.001",paste0("*",pv),
                   ifelse(pv>0.05&pv<=0.1,paste0(".",pv),pv))
 
-     
+
     v<-x[,i]
-     
+
     if (is.factor(v)){
        pred<-paste(names(x)[i],levels(v)[-1],sep = "_")
     }
-       
+
        else {pred<-names(x)[i]}
-      
+
       df<-rbind(df,cbind(pred,co_ci,pv,t))
-      
+
     }}
-    
+
   if (ci==FALSE){
-     
+
        df<-data.frame(matrix(ncol = 4))
   names(df)<-c("pred","b","pv","t")
-     
+
   for(i in 1:ncol(x)){
      m<-lm(y~v1+x[,i])
-     
+
      b<-round(coef(m)[-c(1:m1)],3)
-     
+
      pv<-round(tidy(m)$p.value[-c(1:m1)],3)
      pv<-ifelse(pv<0.001,"<0.001",pv)
-    
+
     t <- ifelse(pv<=0.1|pv=="<0.001","include","drop")
-    
+
      pv <- ifelse(pv<=0.05|pv=="<0.001",paste0("*",pv),
                   ifelse(pv>0.05&pv<=0.1,paste0(".",pv),pv))
-     
+
      v<-x[,i]
-     
+
      if (is.factor(v)){
        pred<-paste(names(x)[i],levels(v)[-1],sep = "_")
      }
-       
+
        else {pred<-names(x)[i]}
-     
+
      df<-rbind(df,cbind(pred,b,pv,t))
   }}
-  
+  result<-df
   return(df)
 }