marginal_effects {ocf}

Marginal Effects for Ordered Correlation Forest

Description

Nonparametric estimation of marginal effects using an ocf object.

Usage

marginal_effects(
  object,
  data = NULL,
  which_covariates = c(),
  eval = "atmean",
  bandwitdh = 0.1,
  inference = FALSE
)

Arguments

object

An ocf object.

data

Data set of class data.frame to estimate marginal effects. It must contain at least the same covariates used to train the forests. If NULL, marginal effects are estimated on object$full_data.

which_covariates

Character vector storing the names of the covariates for which marginal effect estimation is desired. If empty (the default), marginal effects are estimated for all covariates.

eval

Evaluation point for marginal effects. Either "mean", "atmean" or "atmedian".

bandwitdh

How many standard deviations x_up and x_down differ from x.

inference

Whether to extract weights and compute standard errors. The weights extraction considerably slows down the program.

Details

marginal_effects can estimate mean marginal effects, marginal effects at the mean, or marginal effects at the median, according to the eval argument.

The routine assumes that covariates with more than ten unique values are continuous. Otherwise, covariates are assumed to be discrete.

Value

Object of class ocf.marginal.

Author(s)

Riccardo Di Francesco

See Also

ocf

Examples

## Load data from orf package.
set.seed(1986)

library(orf)
data(odata)
odata <- odata[1:100, ] # Subset to reduce elapsed time.

y <- as.numeric(odata[, 1])
X <- as.matrix(odata[, -1])

## Fit ocf. Use large number of trees.
forests <- ocf(y, X, n.trees = 4000)

## Marginal effects at the mean.
me <- marginal_effects(forests, eval = "atmean")
print(me)
summary(me)

## LATEX.
print(me, latex = TRUE)

## Compute standard errors. This requires honest forests.
honest_forests <- ocf(y, X, n.trees = 4000, honesty = TRUE)
honest_me <- marginal_effects(honest_forests, eval = "atmean", inference = TRUE)
honest_me$standard.errors
honest_me$p.values # These are not corrected for multiple hypotheses testing!

print(honest_me, latex = TRUE)