plot_change: Plot the change in the distribution of X

In optinterv: Optimal Intervention

Description

Illustrates how the intervention changes the distribution of X by plotting barchart (for categorical variables) or denisty plot (for continouous variables), before and after the intervention.

Usage

plot_change(x, plot.vars = "sig", graph.col = c("red", "blue"),
  alpha = 0.05, line.type = c(1, 2), n.val = 10)

Arguments

x	an optint object.
plot.vars	which variables to plot? either a number (n) - indicating to plot the first n variables, "sig" (default) - plot only significant variables, or a vector with names of variables to plot.
graph.col	graph color/s.
alpha	significance level for the confidence intervals. also used in order to determine which variables are significant.
line.type	line type for densityplot
n.val	variable with more values than 'n.val' will be displayed by density plot, while variable with fewer values will be displayed by barchart.

Examples

# generate data
n <- 50
p <- 3
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,
                   n.boot = 100, n.perm = 100)

#we can look on the new features distribution more deeply, using plot_change():
plot_change(imp_feat, plot.vars = "sig")

optinterv documentation built on March 26, 2020, 7:05 p.m.