u

NAMESPACE

@@ -1,7 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
 export(age_calc)
-export(cie_test)
 export(col_fact)
 export(col_num)
 export(cpr_check)
@@ -15,3 +14,4 @@ export(rep_biv)
 export(rep_epi_tests)
 export(rep_glm)
 export(rep_lm)
+export(rep_reg_cie)

R/rep_glm.R

@@ -3,24 +3,31 @@
 #' @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.
 #' @param meas Effect meassure. Input as c() of columnnames, use dput().
 #' @param vars variables in model. Input as c() of columnnames, use dput().
-#' @param string variables to test. Input as c() of columnnames, use dput().
+#' @param str variables to test. Input as c() of columnnames, use dput().
 #' @param ci flag to get results as OR with 95% confidence interval.
-#' @param data data frame to pull variables from.
+#' @param dta data frame to pull variables from.
 #' @keywords logistic regression
 #' @export
 #' @examples
-#' rep_glm()
+#'   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,string,ci=FALSE,data){
-  ## 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.
-  ##
 
   require(broom)
 
   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)
   m1<-length(coef(glm(y~.,family = binomial(),data = dt)))
@@ -57,11 +64,9 @@ rep_glm<-function(meas,vars,string,ci=FALSE,data){
 
     for(i in 1:ncol(x)){
       dat<-cbind(dt,x[,i])
-
       m<-glm(y~.,family = binomial(),data=dat)
 
       b<-round(coef(m)[-c(1:m1)],3)
-
       pv<-round(tidy(m)$p.value[-c(1:m1)],3)
 
       x1<-x[,i]
@@ -89,3 +94,6 @@ rep_glm<-function(meas,vars,string,ci=FALSE,data){
   return(r)
 }
 
+
+
+

R/rep_lm.R

@@ -9,7 +9,15 @@
 #' @keywords linear regression
 #' @export
 #' @examples
-#' rep_lm()
+#'   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<-function(meas,vars,string,ci=FALSE,data){
 
@@ -18,6 +26,7 @@ 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)
   m1<-length(coef(lm(y~.,data = dt)))
@@ -26,7 +35,7 @@ rep_lm<-function(meas,vars,string,ci=FALSE,data){
 
   if (ci==TRUE){
 
-    df<-data.frame(matrix(ncol = 3))
+    df<-data.frame(matrix(NA,ncol = 3))
     names(df)<-c("pred","or_ci","pv")
 
     for(i in 1:ncol(x)){
@@ -49,7 +58,7 @@ rep_lm<-function(meas,vars,string,ci=FALSE,data){
 
   if (ci==FALSE){
 
-    df<-data.frame(matrix(ncol = 3))
+    df<-data.frame(matrix(NA,ncol = 3))
     names(df)<-c("pred","b","pv")
 
     for(i in 1:ncol(x)){
@@ -85,3 +94,14 @@ 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/rep_reg_cie.R

@@ -10,9 +10,9 @@
 #' @keywords change-in-estimate
 #' @export
 #' @examples
-#' cie_test()
+#' rep_reg_cie()
 
-cie_test<-function(meas,vars,string,data,logistic=FALSE,cut=0.1){
+rep_reg_cie<-function(meas,vars,string,data,logistic=FALSE,cut=0.1){
 
   require(broom)
 

man/rep_glm.Rd

@@ -11,17 +11,25 @@ rep_glm(meas, vars, string, ci = FALSE, data)
 
 \item{vars}{variables in model. Input as c() of columnnames, use dput().}
 
-\item{string}{variables to test. Input as c() of columnnames, use dput().}
-
 \item{ci}{flag to get results as OR with 95% confidence interval.}
 
-\item{data}{data frame to pull variables from.}
+\item{str}{variables to test. Input as c() of columnnames, use dput().}
+
+\item{dta}{data frame to pull variables from.}
 }
 \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{
-rep_glm()
+  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}
 \keyword{regression}

man/rep_lm.Rd

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

man/rep_reg_cie.Rd

@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/cie_test.R
+% Please edit documentation in R/rep_reg_cie.R
-\name{cie_test}
+\name{rep_reg_cie}
-\alias{cie_test}
+\alias{rep_reg_cie}
 \title{A repeated regression function for change-in-estimate analysis}
 \usage{
-cie_test(meas, vars, string, data, logistic = FALSE, cut = 0.1)
+rep_reg_cie(meas, vars, string, data, logistic = FALSE, cut = 0.1)
 }
 \arguments{
 \item{meas}{Effect meassure. Input as c() of columnnames, use dput().}
@@ -23,6 +23,6 @@ cie_test(meas, vars, string, data, logistic = FALSE, cut = 0.1)
 For bivariate analyses. From "Modeling and variable selection in epidemiologic analysis." - S. Greenland, 1989.
 }
 \examples{
-cie_test()
+rep_reg_cie()
 }
 \keyword{change-in-estimate}