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#' @param v1 Main variable in model
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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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#' @param string String of columnnames from dataframe to include. Use dput().
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#' @keywords change-in-estimate
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#' @keywords change-in-estimate
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#' @export
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#' @examples
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#' @examples
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#' l<-5
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#' l<-5
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#' y<-factor(rep(c("a","b"),l))
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#' y<-factor(rep(c("a","b"),l))
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#' d<-data.frame(y,x,v1,v2,v3)
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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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#' 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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#' 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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cie_test<-function(meas,vars,string,data,logistic=FALSE,cut=0.1){
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#' @param ci flag to get results as OR with 95% confidence interval.
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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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#' @param data data frame to pull variables from.
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#' @keywords logistic regression
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#' @keywords logistic regression
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#' @export
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#' @examples
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#' @examples
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#' l<-5
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#' l<-5
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#' y<-factor(rep(c("a","b"),l))
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#' y<-factor(rep(c("a","b"),l))
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#' d<-data.frame(y,x,v1,v2,v3)
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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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#' preds<-dput(names(d)[3:ncol(d)])
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#' rep_glm(meas="y",vars="x",string=preds,ci=FALSE,data=df)
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#' rep_glm(meas="y",vars="x",string=preds,ci=FALSE,data=df)
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#' @export
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rep_glm<-function(meas,vars,string,ci=FALSE,data){
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rep_glm<-function(meas,vars,string,ci=FALSE,data){
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## x is data.frame of predictors, y is vector of an aoutcome as a factor
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## x is data.frame of predictors, y is vector of an aoutcome as a factor
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