plot_calibration_test: Plot calibration test results
In ensley-nexant/cfeval: Causal Forest Evaluation and Visualization

Description Usage Arguments Details Value Examples

Display the results of test_calibration in graphical format.

1	plot_calibration_test(clbr, ci = 0.9)

`clbr`	The output of `test_calibration`
`ci`	The size of the confidence intervals. Default is 90%

From the grf documentation:

The forest summary function test_calibration can be used to asses a forest's goodness of fit. A coefficient of 1 for mean.forest.prediction suggests that the mean forest prediction is correct and a coefficient of 1 for differential.forest.prediction suggests that the forest has captured heterogeneity in the underlying signal.

A ggplot2 plot object

## Not run: 
 require(grf)

 n <- 2000; p <- 10

 X <- matrix(rnorm(n * p), n, p)
 W <- rbinom(n, 1, 0.4 + 0.2 * (X[, 1] > 0))
 Y <- pmax(X[, 1], 0) * W + X[, 2] + pmin(X[, 3], 0) + rnorm(n)
 cf <- causal_forest(X, Y, W)

 clbr <- test_calibration(cf)
 plot_calibration_test(clbr, ci = 0.95)

## End(Not run)