An R package that implemnts the method proposed by Danieli, Devi and Fryer (2019), to identify the factors with the greatest potential to increase a pre-specified outcome, using observational data.
Currently, the package can be installed through GitHub:
library(optinterv) #generate data n <- 1000 p <- 10 features <- matrix(rnorm(n*p), ncol = p) men <- matrix(rbinom(n, 1, 0.5), nrow = n) outcome <- 2*(features[,1] > 1) + men*pmax(features[,2], 0) + rnorm(n) outcome <- as.vector(outcome) #find the optimal intervention using the non-parametric method: imp_feat <- optint(Y = outcome, X = features, control = men, method = "non-parametric", lambda = 10, plot = TRUE) #by default, only the significant features are displayed #(see ?plot.optint for further details). #for customized variable importance plot, use plot(): plot(imp_feat, plot.vars = 10) #show summary of the results using summary(): summary(imp_feat) #we can look on the new features distribution more deeply, using plot_change(): plot_change(imp_feat, plot.vars = "sig") #we can explore how the optimal intervention varies between genders using optint_by_group(): men <- as.vector(men) imp_feat_by_gender <- optint_by_group(Y = outcome, X = features, group = men, method = "non-parametric", lambda = 10) #by default, only the significant features are displayed #(see ?plot.optint_by_group for further details). #for customized variable importance plot, use plot(): plot(imp_feat_by_gender, plot.vars = 10)
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