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80 lines
2.1 KiB
R
80 lines
2.1 KiB
R
#' A repeated regression function for change-in-estimate analysis
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#'
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#' For bivariate analyses. From "Modeling and variable selection in epidemiologic analysis." - S. Greenland, 1989.
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#' @param y Effect meassure.
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#' @param v1 Main variable in model
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#' @param string String of columnnames from dataframe to include. Use dput().
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#' @keywords change-in-estimate
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#' @examples
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#' l<-5
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#' y<-factor(rep(c("a","b"),l))
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#' x<-rnorm(length(y), mean=50, sd=10)
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#' v1<-factor(rep(c("r","s"),length(y)/2))
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#' v2<-as.numeric(sample(1:100, length(y), replace=FALSE))
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#' v3<-as.numeric(1:length(y))
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#' d<-data.frame(y,x,v1,v2,v3)
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#' preds<-dput(names(d)[3:ncol(d)])
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#' cie_test(meas="y",vars="x",string=preds,data=d,logistic = TRUE,cut = 0.1)
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#' @export
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cie_test<-function(meas,vars,string,data,logistic=FALSE,cut=0.1){
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require(broom)
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d<-data
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x<-data.frame(d[,c(string)])
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v<-data.frame(d[,c(vars)])
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names(v)<-c(vars)
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y<-d[,c(meas)]
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dt<-cbind(y,v)
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c<-as.numeric(cut)
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if(logistic==FALSE){
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if (is.factor(y)){stop("Logistic is flagged as FALSE, but the provided meassure is formatted as a factor!")}
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e<-as.numeric(round(coef(lm(y~.,data = dt)),3))[1]
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df<-data.frame(pred="base",b=e)
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for(i in 1:ncol(x)){
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dat<-cbind(dt,x[,i])
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m<-lm(y~.,data=dat)
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b<-as.numeric(round(coef(m),3))[1]
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pred<-paste(names(x)[i])
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df<-rbind(df,cbind(pred,b)) }
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di<-as.vector(abs(e-as.numeric(df[-1,2]))/e)
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dif<-c(NA,di)
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t<-c(NA,ifelse(di>=c,"include","drop"))
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r<-cbind(df,dif,t) }
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if(logistic==TRUE){
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if (!is.factor(y)){stop("Logistic is flagged as TRUE, but the provided meassure is NOT formatted as a factor!")}
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e<-as.numeric(round(exp(coef(glm(y~.,family=binomial(),data=dt))),3))[1]
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df<-data.frame(pred="base",b=e)
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for(i in 1:ncol(x)){
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dat<-cbind(dt,x[,i])
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m<-glm(y~.,family=binomial(),data=dat)
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b<-as.numeric(round(exp(coef(m)),3))[1]
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pred<-paste(names(x)[i])
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df<-rbind(df,cbind(pred,b)) }
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di<-as.vector(abs(e-as.numeric(df[-1,2]))/e)
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dif<-c(NA,di)
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t<-c(NA,ifelse(di>=c,"include","drop"))
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r<-cbind(df,dif,t)
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}
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return(r)
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}
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