predict.DIV: Prediction Function for a DIV Model Object

In DistributionIV: Distributional Instrumental Variable (DIV) Model

Prediction Function for a DIV Model Object

Description

This function computes predictions from a trained DIV model. It allows for estimation of the interventional mean and quantiles, as well as sampling from the fitted interventional distribution. If the model includes exogenous predictors, it allows for estimation of the conditional interventional mean and quantiles, as well as sampling from the fitted conditional interventional distribution.

Usage

## S3 method for class 'DIV'
predict(
  object,
  Xtest,
  Wtest = NULL,
  type = c("mean", "sample", "quantile")[1],
  trim = 0.05,
  quantiles = 0.1 * (1:9),
  nsample = 200,
  drop = TRUE,
  ...
)

Arguments

object	A trained DIV model returned from div or divfit functions.
Xtest	A matrix or data frame representing predictors in the test set.
Wtest	A matrix or data frame representing exogenous predictors in the test set. If the model includes exogenous predictors, 'Wtest' has to be specified for computation of conditional treatment estimates or to draw samples from the conditional interventional distribution.
type	The type of prediction to make: * 'mean': for point estimates (the default). * 'sample': for samples from the estimated distribution. * 'quantile'/'quantiles': for quantiles of the estimated distribution.
trim	The proportion of extreme values to trim when calculating the mean (default: 0.05).
quantiles	The quantiles to estimate if type is 'quantile' (default: 0.1*(1:9)).
nsample	The number of samples to draw if type is 'sample' (default: 200).
drop	A boolean indicating whether to drop dimensions of length 1 from the output (default: TRUE).
...	additional arguments (currently ignored).

Value

A vector or matrix/array of predictions.

Examples

## Not run: 
# NOTE: This example requires torch runtime.
# Please install it with: torch::install_torch()

# Simulate data -------------------------------------------------------------
p_Z <- 4
p_X <- 2

set.seed(2209)
n_train <- 1000
Z <- matrix(rnorm(n_train * p_Z, mean = 2), ncol = p_Z)
H <- rnorm(n_train, mean = 0, sd = 1.5)
X1 <- 0.1 * (Z[, 1] + rnorm(n_train, sd = 0.1)) ^ 2 +
 (Z[, 2] + rnorm(n_train, sd = 1)) ^ 2 + H + rnorm(n_train, sd = 0.1)
X2 <- 0.5 * (Z[, 3] + Z[, 4]) ^ 2 + 0.1 * H ^ 2 + rnorm(n_train, sd = 0.1)
X <- matrix(cbind(X1, X2), ncol = p_X)
Y <- 0.5 * X[, 1] + 0.2 * (X[, 2] + rnorm(n_train, sd = 0.2) + H) ^ 2 +
 rnorm(n_train, sd = 0.1)
n_test <- n_train
Ztest <- matrix(rnorm(n_test * p_Z, mean = 2), ncol = p_Z)
Htest <- rnorm(n_test, mean = 0, sd = 1.5)
X1test <- 0.1 * (Ztest[, 1] + rnorm(n_test, sd = 0.1)) ^ 2 +
 (Ztest[, 2] + rnorm(n_test, sd = 1)) ^ 2 + Htest + rnorm(n_test, sd = 0.1)
X2test <- 0.5 * (Ztest[, 3] + Ztest[, 4]) ^ 2 + 0.1 * Htest ^ 2 + rnorm(n_test, sd = 0.1)
Xtest <- matrix(cbind(X1test, X2test), ncol = p_X)
Ytest <- 0.5 * Xtest[, 1] + 0.2 * (Xtest[, 2] + rnorm(n_test, sd = 0.2) + Htest) ^ 2 +
 rnorm(n_test, sd = 0.1)

# Fit DIV model -------------------------------------------------------------
# Consider increasing number of epochs. Here: num_epochs = 100 for fast computation only.
DIV_model <- div(Z = Z, X = X, Y = Y, num_epochs = 100)
print(DIV_model)

# Prediction on test data ---------------------------------------------------
Yhat <- predict(object = DIV_model, Xtest = Xtest, type = "mean")
cat("\n Correlation between predicted and realized values: ", signif(cor(Yhat, Ytest), 3))
plot(Yhat, Ytest, xlab = "model prediction", ylab = "observation")
# Quantile prediction -------------------------------------------------------
Yhat_quant <- predict(object = DIV_model, Xtest = Xtest, type = "quantile")
ord <- order(Yhat)
matplot(Yhat[ord], Yhat_quant[ord,], type = "l", col = 2, lty = 1,
xlab = "model prediction", ylab = "observation")
points(Yhat[ord], Ytest[ord], pch = 20, cex = 0.5)

#' # Sampling from estimated model ---------------------------------------------
Ysample <- predict(object = DIV_model, Xtest = Xtest, type = "sample", nsample = 1)

#' # Plots of realized & sampled values against first variable -----------------
oldpar <- par(no.readonly = TRUE)
par(mfrow = c(1, 2))
plot(Xtest[, 1], Ytest, xlab = "Predictor Variable 1", ylab = "Observation")
plot(Xtest[, 1], Ysample, xlab = "Predictor Variable 1", ylab = "Model sample")
par(oldpar)

## End(Not run)

DistributionIV documentation built on Sept. 9, 2025, 5:52 p.m.