optinterv (version 0.1.0)

optint_by_group: Optimal intervention, by group

Description

Similar to optint, identifies the factors with the greatest potential to increase a pre-specified outcome for each group separately, and thus allowing to detect heterogeneity between groups.

Usage

optint_by_group(Y, X, group, control = NULL, wgt = rep(1, length(Y)),
  method = "non-parametric", lambda = 100, sigma = 1,
  grp.size = 30, n.boot = 1000, alpha = 0.05, plot = TRUE)

Arguments

Y

outcome vector (must be numeric without NA's).

X

numeric data frame or matrix of factors to be considered.

group

vector with group labels (i.e. grouping variable). the function optint implemented for each group separately.

control

numeric data frame or matrix of factors to control for. these are factors that we can't consider while looking for the optimal intervention (e.g. race).

wgt

an optional vector of weights.

method

the method to be used. either "non-parametric" (default), "correlation" or "nearest-neighbors".

lambda

the lagrange multiplier. also known as the shadow price of an intervention.

sigma

distance penalty for the nearest-neighbors method.

grp.size

for the nearest-neighbors method; if the number of examples in each control group is smaller than grp.size, performs weight adjustment using wgt_adjust. else, calculate weights seperatly for each control group.

n.boot

number of bootstrap replications to use for the standard errors / confidence intervals calculation.

alpha

significance level for the confidence intervals.

plot

logical. if TRUE (default), the results are plotted by plot.optint_by_group.

Value

an object of class "optint_by_group". This object is a list containing two components:

est

a matrix of estimates (in their original units), for each group. here estimates are \(E(X | I=1) - E(X | I=0)\), and they are used by plot.optint_by_group.

sd

estimates standard deviation.

Examples

# NOT RUN {
# 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 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 = 3)

# }