Example of linear model base covariate adjustment

covariate_adjust.R

#get linear model residuals
#' @import dplyr
#' @export
dave_lm_adjust<-function(data,formula,test_vars,adjust=TRUE,progress=TRUE){
  
  if (progress == TRUE){ pb <- txtProgressBar(min = 0, max = ncol(data), style = 3)} else {pb<-NULL}
  
  out <- lapply(1:length(test_vars), function(i) {
    if (progress == TRUE) {
      setTxtProgressBar(pb, i)
    }
    
    
    mod <-
      tryCatch(
        lm(as.formula(paste0(
          "data[,'", test_vars[i], "']~", formula
        )), data = data),
        error = function(e) {
          NULL
        }
      )
    
    if (is.null(mod)) {
      out<-NA
    } else {
      out<-residuals(mod)
    }
    
    if (progress == TRUE) {
      close(pb)
    }
    out
  }) %>%
    do.call("cbind", .)
  
  
  if(adjust){
    min <- apply(out, 2, min, na.rm = T)
    out <- lapply(1:ncol(out), function(i)
    {
      out[, i, drop = F] + abs(min[i])
    }) %>%
      do.call("cbind", .)
  }
  
  
  colnames(out)<-test_vars
  
  return(out)
  
}

#------------------------------
#example of linear model based adjustment
#------------------------------
data(iris)
head(iris)

library(ggplot2)
ggplot(iris,aes(x=Sepal.Length,y=Petal.Width, color=Species)) +
  geom_point() +
  theme_minimal()


#variables to adjust
library(dplyr)

iris2<-dave_lm_adjust(iris, formula='Species',test_vars = colnames(iris)) %>%
  data.frame()
#replace adjusted Species factor with the original 
iris2$Species<-iris$Species

ggplot(iris2,aes(x=Sepal.Length,y=Petal.Width, color=Species)) +
  geom_point() +
  theme_minimal()