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169
R/cie_test.R
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@ -1,165 +1,82 @@
#' A repeated regression function for change-in-estimate analysis #' A repeated regression function for change-in-estimate analysis
#' #'
#' For bivariate analyses. #' For bivariate analyses. From "Modeling and variable selection in epidemiologic analysis." - S. Greenland, 1989.
#' @param y Effect meassure. #' @param y Effect meassure.
#' @param v1 Main variable in model #' @param v1 Main variable in model
#' @param string String of columnnames from dataframe to include. Use dput(). #' @param string String of columnnames from dataframe to include. Use dput().
#' @keywords change-in-estimate #' @keywords change-in-estimate
#' @export #'
#' @examples #' @examples
#' cie_test() #' l<-5
#' 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<-as.numeric(sample(1:100, length(y), replace=FALSE))
#' v3<-as.numeric(1:length(y))
#' d<-data.frame(y,x,v1,v2,v3)
#' preds<-dput(names(d)[3:ncol(d)])
#' cie_test(meas="y",vars="x",string=preds,data=d,logistic = TRUE,cut = 0.1)
#'
#' @export
#'
cie_test<-function(y,v1,string,data,logistic=FALSE,cut=0.1,v2=NULL,v3=NULL){ cie_test<-function(meas,vars,string,data,logistic=FALSE,cut=0.1){
## Calculating variables, that should be included for a change in estimate analysis.
## v1-3 are possible locked variables, y is the outcome vector.
## String defines variables to test, and is provided as vector of variable names. Use dput().
## From "Modeling and variable selection in epidemiologic analysis." - S. Greenland, 1989.
require(broom) require(broom)
d<-data d<-data
x<-select(d,one_of(c(string))) x<-data.frame(d[,c(string)])
v<-data.frame(d[,c(vars)])
names(v)<-c(vars)
y<-d[,c(meas)]
dt<-cbind(y,v)
c<-as.numeric(cut)
if(logistic==FALSE){ if(logistic==FALSE){
if (is.factor(y)){stop("Some kind of error message would be nice, but y should not be a factor!")}
if (is.null(v2)&is.null(v3)){
e<-as.numeric(round(coef(lm(y~v1)),3))[1]
if (is.factor(y)){stop("Logistic is flagged as FALSE, but the provided meassure is formatted as a factor!")}
e<-as.numeric(round(coef(lm(y~.,data = dt)),3))[1]
df<-data.frame(pred="base",b=e) df<-data.frame(pred="base",b=e)
for(i in 1:ncol(x)){ for(i in 1:ncol(x)){
dat<-cbind(dt,x[,i])
m<-lm(y~v1+x[,i]) m<-lm(y~.,data=dat)
b<-as.numeric(round(coef(m),3))[1] b<-as.numeric(round(coef(m),3))[1]
v<-x[,i]
pred<-paste(names(x)[i]) pred<-paste(names(x)[i])
df<-rbind(df,cbind(pred,b)) df<-rbind(df,cbind(pred,b)) }
}
t<-c(NA,ifelse(abs(e-as.numeric(df[-1,2]))>=(e*cut),"include","drop"))
df<-cbind(df,t)
}
if (!is.null(v2)&is.null(v3)){ di<-as.vector(abs(e-as.numeric(df[-1,2]))/e)
dif<-c(NA,di)
e<-as.numeric(round(coef(lm(y~v1+v2)),3))[1] t<-c(NA,ifelse(di>=c,"include","drop"))
r<-cbind(df,dif,t) }
df<-data.frame(pred="base",b=e)
for(i in 1:ncol(x)){
m<-lm(y~v1+v2+x[,i])
b<-as.numeric(round(coef(m),3))[1]
v<-x[,i]
pred<-paste(names(x)[i])
df<-rbind(df,cbind(pred,b))
}
t<-c(NA,ifelse(abs(e-as.numeric(df[-1,2]))>=(e*cut),"include","drop"))
df<-cbind(df,t)
}
if (!is.null(v2)&!is.null(v3)){
e<-as.numeric(round(coef(lm(y~v1+v2+v3)),3))[1]
df<-data.frame(pred="base",b=e)
for(i in 1:ncol(x)){
m<-lm(y~v1+v2+v3+x[,i])
b<-as.numeric(round(coef(m),3))[1]
v<-x[,i]
pred<-paste(names(x)[i])
df<-rbind(df,cbind(pred,b))
}
t<-c(NA,ifelse(abs(e-as.numeric(df[-1,2]))>=(e*cut),"include","drop"))
df<-cbind(df,t)
}}
if(logistic==TRUE){ if(logistic==TRUE){
if (!is.factor(y)){stop("Some kind of error message would be nice, but y should be a factor!")} if (!is.factor(y)){stop("Logistic is flagged as TRUE, but the provided meassure is NOT formatted as a factor!")}
if (is.null(v2)&is.null(v3)){ e<-as.numeric(round(exp(coef(glm(y~.,family=binomial(),data=dt))),3))[1]
e<-as.numeric(round(exp(coef(glm(y~v1,family=binomial()))),3))[1]
df<-data.frame(pred="base",b=e) df<-data.frame(pred="base",b=e)
for(i in 1:ncol(x)){ for(i in 1:ncol(x)){
dat<-cbind(dt,x[,i])
m<-glm(y~v1+x[,i],family=binomial()) m<-glm(y~.,family=binomial(),data=dat)
b<-as.numeric(round(exp(coef(m)),3))[1] b<-as.numeric(round(exp(coef(m)),3))[1]
v<-x[,i]
pred<-paste(names(x)[i]) pred<-paste(names(x)[i])
df<-rbind(df,cbind(pred,b)) df<-rbind(df,cbind(pred,b)) }
di<-as.vector(abs(e-as.numeric(df[-1,2]))/e)
dif<-c(NA,di)
t<-c(NA,ifelse(di>=c,"include","drop"))
r<-cbind(df,dif,t)
} }
t<-c(NA,ifelse(abs(e-as.numeric(df[-1,2]))>=(e*cut),"include","drop")) return(r)
df<-cbind(df,t)
}
if (!is.null(v2)&is.null(v3)){
e<-as.numeric(round(exp(coef(glm(y~v1+v2,family=binomial()))),3))[1]
df<-data.frame(pred="base",b=e)
for(i in 1:ncol(x)){
m<-glm(y~v1+v2+x[,i],family=binomial())
b<-as.numeric(round(exp(coef(m)),3))[1]
v<-x[,i]
pred<-paste(names(x)[i])
df<-rbind(df,cbind(pred,b))
}
t<-c(NA,ifelse(abs(e-as.numeric(df[-1,2]))>=(e*cut),"include","drop"))
df<-cbind(df,t)
}
if (!is.null(v2)&!is.null(v3)){
e<-as.numeric(round(exp(coef(glm(y~v1+v2+v3,family=binomial()))),3))[1]
df<-data.frame(pred="base",b=e)
for(i in 1:ncol(x)){
m<-glm(y~v1+v2+v3+x[,i],family=binomial())
b<-as.numeric(round(exp(coef(m)),3))[1]
v<-x[,i]
pred<-paste(names(x)[i])
df<-rbind(df,cbind(pred,b))
}
t<-c(NA,ifelse(abs(e-as.numeric(df[-1,2]))>=(e*cut),"include","drop"))
df<-cbind(df,t)
}}
result<-df
return(df)
} }

20
man/cie_test.Rd
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@ -4,20 +4,28 @@
\alias{cie_test} \alias{cie_test}
\title{A repeated regression function for change-in-estimate analysis} \title{A repeated regression function for change-in-estimate analysis}
\usage{ \usage{
cie_test(y, v1, string, data, logistic = FALSE, cut = 0.1, v2 = NULL, cie_test(meas, vars, string, data, logistic = FALSE, cut = 0.1)
v3 = NULL)
} }
\arguments{ \arguments{
\item{string}{String of columnnames from dataframe to include. Use dput().}
\item{y}{Effect meassure.} \item{y}{Effect meassure.}
\item{v1}{Main variable in model} \item{v1}{Main variable in model}
\item{string}{String of columnnames from dataframe to include. Use dput().}
} }
\description{ \description{
For bivariate analyses. For bivariate analyses. From "Modeling and variable selection in epidemiologic analysis." - S. Greenland, 1989.
} }
\examples{ \examples{
cie_test() l<-5
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<-as.numeric(sample(1:100, length(y), replace=FALSE))
v3<-as.numeric(1:length(y))
d<-data.frame(y,x,v1,v2,v3)
preds<-dput(names(d)[3:ncol(d)])
cie_test(meas="y",vars="x",string=preds,data=d,logistic = TRUE,cut = 0.1)
} }
\keyword{change-in-estimate} \keyword{change-in-estimate}