library(marginaleffects)
library(marginaleffects)
library(magrittr)
set.seed(1024)
mod < lm(Sepal.Length ~ Sepal.Width * Species, data = iris)
# bootstrap
avg_predictions(mod, by = "Species") %>%
inferences(method = "boot")
avg_predictions(mod, by = "Species") %>%
inferences(method = "rsample")
# Fractional (bayesian) bootstrap
avg_slopes(mod, by = "Species") %>%
inferences(method = "fwb") %>%
posterior_draws("rvar") %>%
data.frame()
# Simulationbased inference
slopes(mod) %>%
inferences(method = "simulation") %>%
head()
(EXPERIMENTAL) Bootstrap, Conformal, and SimulationBased Inference
Description
Warning: This function is experimental. It may be renamed, the user interface may change, or the functionality may migrate to arguments in other marginaleffects
functions.
Apply this function to a marginaleffects
object to change the inferential method used to compute uncertainty estimates.
Usage
inferences(
x,
method,
R = 1000,
conf_type = "perc",
conformal_test = NULL,
conformal_calibration = NULL,
conformal_score = "residual_abs",
...
)
Arguments
x

Object produced by one of the core marginaleffects functions.

method

String

R

Number of resamples, simulations, or crossvalidation folds. 
conf_type

String: type of bootstrap interval to construct.

conformal_test

Data frame of test data for conformal prediction. 
conformal_calibration

Data frame of calibration data for split conformal prediction (method=“conformal_split ).

conformal_score

String. Warning: The

…


Details
When method=“simulation”
, we conduct simulationbased inference following the method discussed in Krinsky & Robb (1986):

Draw
R
sets of simulated coefficients from a multivariate normal distribution with mean equal to the original model’s estimated coefficients and variance equal to the model’s variancecovariance matrix (classical, "HC3", or other). 
Use the
R
sets of coefficients to computeR
sets of estimands: predictions, comparisons, slopes, or hypotheses. 
Take quantiles of the resulting distribution of estimands to obtain a confidence interval and the standard deviation of simulated estimates to estimate the standard error.
When method=“fwb”
, drawn weights are supplied to the model fitting function’s weights
argument; if the model doesn’t accept noninteger weights, this method should not be used. If weights were included in the original model fit, they are extracted by weights()
and multiplied by the drawn weights. These weights are supplied to the wts
argument of the estimation function (e.g., comparisons()
).
Value
A marginaleffects
object with simulation or bootstrap resamples and objects attached.
References
Krinsky, I., and A. L. Robb. 1986. “On Approximating the Statistical Properties of Elasticities.” Review of Economics and Statistics 68 (4): 715–9.
King, Gary, Michael Tomz, and Jason Wittenberg. "Making the most of statistical analyses: Improving interpretation and presentation." American journal of political science (2000): 347361
Dowd, Bryan E., William H. Greene, and Edward C. Norton. "Computation of standard errors." Health services research 49.2 (2014): 731750.
Angelopoulos, Anastasios N., and Stephen Bates. 2022. "A Gentle Introduction to Conformal Prediction and DistributionFree Uncertainty Quantification." arXiv. https://doi.org/10.48550/arXiv.2107.07511.
Barber, Rina Foygel, Emmanuel J. Candes, Aaditya Ramdas, and Ryan J. Tibshirani. 2020. “Predictive Inference with the Jackknife+.” arXiv. http://arxiv.org/abs/1905.02928.