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https://github.com/agdamsbo/daDoctoR.git
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39 lines
1.6 KiB
R
39 lines
1.6 KiB
R
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#' Extension to the print_pred function, for by stratum analysis.
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#'
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#' Outputs list of results from 'print_pred' for the whole data set and for each stratum defined by 'strat'.
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#' Suitable to assist in determining whether a variable is a confounder or effect modifier.
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#' Ref: https://open.oregonstate.education/epidemiology/chapter/effect-modification/
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#' @param meas binary outcome measure variable, column name in data.frame as a string. Can be numeric or factor. Result is calculated accordingly.
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#' @param adj variables to adjust for, as string.
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#' @param strat stratum to stratify, variable name as string
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#' @param data dataframe of data.
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#' @param include.stratum flag to set if stratum variable should be included in first analysis of non-stratified data.
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#' @keywords stratum
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#' @export
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#' @examples
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#' data('mtcars')
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#' mtcars$vs<-factor(mtcars$vs)
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#' mtcars$am<-factor(mtcars$am)
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#' print_pred_stratum(meas="mpg",strat="vs",adj=c("disp","wt","am"),data=mtcars,include.stratum=TRUE)
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print_pred_stratum<-function(meas,adj,strat,data,dec,include.stratum=TRUE){
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require(daDoctoR)
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require(dplyr)
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if (include.stratum==TRUE){
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ls<-list(all=print_pred(meas = meas,adj=c(strat,adj),data=data))
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}
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if (include.stratum==FALSE) {
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ls<-list(all=print_pred(meas = meas,adj=adj,data=data))
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}
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strt<-data[, c(strat)]
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for (i in 1:length(levels(factor(strt)))){
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d_str<-data[data[[strat]]==levels(data[[strat]])[i], c(meas,adj)]
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ls_str<-list(print_pred(meas = meas,adj=adj,data=d_str))
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names(ls_str)<-levels(factor(strt))[i]
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ls<-append(ls,ls_str)
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}
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return(ls)
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}
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