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108 lines
2.7 KiB
R
108 lines
2.7 KiB
R
#' A repeated linear regression function
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#'
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#' For bivariate analyses, to determine which variables to include in adjusted model.
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#' @param meas Effect meassure. Input as c() of columnnames, use dput().
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#' @param vars variables in model. Input as c() of columnnames, use dput().
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#' @param string variables to test. Input as c() of columnnames, use dput().
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#' @param ci flag to get results as OR with 95% confidence interval.
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#' @param data data frame to pull variables from.
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#' @keywords linear regression
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#' @export
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#' @examples
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#' l<-50
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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<-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<-c("v1","v2","v3")
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#' rep_lm(meas="x",vars="y",string=preds,ci=F,data=d)
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rep_lm<-function(meas,vars,string,ci=FALSE,data){
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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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m1<-length(coef(lm(y~.,data = dt)))
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if (is.factor(y)){stop("Some kind of error message would be nice, but y should not be a factor!")}
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if (ci==TRUE){
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df<-data.frame(matrix(NA,ncol = 3))
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names(df)<-c("pred","or_ci","pv")
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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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l<-suppressMessages(round(confint(m)[-c(1:m1),1],2))
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u<-suppressMessages(round(confint(m)[-c(1:m1),2],2))
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or<-round(coef(m)[-c(1:m1)],2)
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or_ci<-paste0(or," (",l," to ",u,")")
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pv<-round(tidy(m)$p.value[-c(1:m1)],3)
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x1<-x[,i]
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if (is.factor(x1)){
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pred<-paste(names(x)[i],levels(x1)[-1],sep = "_")}
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else {pred<-names(x)[i]}
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df<-rbind(df,cbind(pred,or_ci,pv))}}
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if (ci==FALSE){
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df<-data.frame(matrix(NA,ncol = 3))
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names(df)<-c("pred","b","pv")
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for(i in 1:ncol(x)){
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dat<-cbind(dt,x[,i])
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m<-lm(y~.,data=dat)
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b<-round(coef(m)[-c(1:m1)],3)
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pv<-round(tidy(m)$p.value[-c(1:m1)],3)
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x1<-x[,i]
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if (is.factor(x1)){
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pred<-paste(names(x)[i],levels(x1)[-1],sep = "_")
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}
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else {pred<-names(x)[i]}
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df<-rbind(df,cbind(pred,b,pv))
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}}
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pa<-as.numeric(df[,3])
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pa<-ifelse(pa<0.001,"<0.001",pa)
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t <- ifelse(pa<=0.1|pa=="<0.001","include","drop")
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pa <- ifelse(pa<=0.05|pa=="<0.001",paste0("*",pa),
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ifelse(pa>0.05&pa<=0.1,paste0(".",pa),pa))
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r<-data.frame(df[,1:2],pa,t)[-1,]
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return(r)
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}
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l<-50
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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<-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<-c("v1","v2","v3")
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rep_lm(meas="x",vars="y",string=preds,ci=F,data=d)
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