#' A repeated regression function for change-in-estimate analysis #' #' For bivariate analyses. From "Modeling and variable selection in epidemiologic analysis." - S. Greenland, 1989. #' @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 data data frame to pull variables from. #' @param logistic flag to set logistic (TRUE) or linear (FALSE,standard) analysis. #' @param cut cut value for gating if including or dropping the tested variable. As suggested bu S. Greenland (1989). #' @keywords estimate-in-estimate #' @export #' @examples #' rep_reg_cie() rep_reg_cie<-function(meas,vars,string,data,logistic=FALSE,cut=0.1){ 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) c<-as.numeric(cut) if(logistic==FALSE){ 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) for(i in 1:ncol(x)){ dat<-cbind(dt,x[,i]) m<-lm(y~.,data=dat) b<-as.numeric(round(coef(m),3))[1] pred<-paste(names(x)[i]) 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) } if(logistic==TRUE){ if (!is.factor(y)){stop("Logistic is flagged as TRUE, but the provided meassure is NOT formatted as a factor!")} e<-as.numeric(round(exp(coef(glm(y~.,family=binomial(),data=dt))),3))[1] df<-data.frame(pred="base",b=e) for(i in 1:ncol(x)){ dat<-cbind(dt,x[,i]) m<-glm(y~.,family=binomial(),data=dat) b<-as.numeric(round(exp(coef(m)),3))[1] pred<-paste(names(x)[i]) 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) } return(r) }