In KatarzynaReluga/postcAIC: Post-cAIC selection inference under Linear Mixed Models

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postcAIC

Overview

Package postcAIC implements post-cAIC confidence intervals (CI) for mixed and fixed parameters under linear mixed models. Post-cAIC CI accounts for the data-driven model selection using conditional Akaike information criterion (cAIC).

Reference: Claeskens, Reluga, and Sperlich (2021). Post-selection inference for linear mixed model parameters using the conditional Akaike information criterion.

Available at arXiv.org.

In what follows, we use abbreviation CRS-2021 to refer to the article of Claeskens, Reluga, and Sperlich (2021).

Installation

You can install the most recent version of postcAIC from GitHub with:

# install.packages("devtools")
# install.packages("remotes")

remotes::install_version("tmg", version = "0.3", repos = "http://cran.r-project.org")
devtools::install_github("KatarzynaReluga/postcAIC")

Example

This is a basic example which shows you how to construct post-cAIC confidence intervals for fixed and mixed parameters under nested-error regression model (NERM).

First we generate outcomes, covariates and cluster labels.

``` {r example2, eval = T} library(postcAIC)

Set seed ----------------------------------------------------

set.seed(10)

Set the number of clusters and units in each cluster --------

n = 15 m_i = 5 m_total = n * m_i

Set fixed (regression) parameters and variance parameters ---

beta = c(2.25, -1.1, 2.43, rep(0, 2)) sig_e = 1 sig_u = 1

Load matrix X from data in package postcAIC -----------------

X = simulations_n15_mi5 X_intercept = cbind(rep(1, m_total), X)

Create a factor vector with cluster labels ------------------

clusterID = rep(1:n, each = m_i)

Generate stochastic errors, random effects and outcomes -----

e_ij = rnorm(m_total, 0, sig_e) u_i = rnorm(n, 0, sig_u) u_i_aug = rep(u_i, each = m_i) y = X_intercept %*% beta + u_i_aug + e_ij

Second, we compute cAIC for each model in the considered model set, construct post-cAIC CI for fixed and mixed effects and plot the latter.

``` {r example3, eval = T}
# Compute cAIC for models from the set of models -------------
cAIC_model_set = compute_cAIC_for_model_set(
  X,
  y,
  clusterID,
  model = "NERM",
  covariate_selection_matrix = NULL,
  modelset  = "part_subset",
  common = c(1:2),
  intercept = FALSE
)

# Construct post-cAIC CI for fixed and mixed parameters ------
postcAIC_CI_results = postcAIC_CI(
  cAIC_min = cAIC_model_set$cAIC_min,
  degcAIC_models =  cAIC_model_set$degcAIC_models,

  X_full = cAIC_model_set$X_full,
  X_cluster_full = cAIC_model_set$X_cluster_full,
  sig_u_full = cAIC_model_set$sig_u_full,
  sig_e_full = cAIC_model_set$sig_e_full,
  model = "NERM",
  clusterID,

  beta_sel = cAIC_model_set$beta_sel,
  mu_sel = cAIC_model_set$mu_sel,

  modelset_matrix = cAIC_model_set$modelset_matrix)

plot(postcAIC_CI_results, y_axis_lim = c(-0.5, 6.5))

We compare the performance of post-cAIC CI for mixed effects with other confidence intervals and plot them together. We consider the naive CI which does not take the model selection step into account. In addition, we construct post-OBSP CI which uses the observed best selective prediction (OBSP) of Sugasawa, S., Kawakubo, Y. and Datta, G. S. (2019).

``` {r, eval = T}

Construct naive CI for mixed and fixed parameters -------------------

indices_sel = cAIC_model_set$indices_sel X_cluster_sel = cAIC_model_set$X_cluster_full[, indices_sel] C_cluster_sel = cbind(as.matrix(X_cluster_sel), diag(n))

naive_CI_results = naive_CI( beta_sel = cAIC_model_set$beta_sel, mu_sel = cAIC_model_set$mu_sel, sig_u_sel = cAIC_model_set$sig_u_sel, sig_e_sel = cAIC_model_set$sig_e_sel, sig_u_full = cAIC_model_set$sig_u_full, sig_e_full = cAIC_model_set$sig_e_full,

X_full = cAIC_model_set$X_full, C_cluster_sel, clusterID, indices_sel, type_MSE_mixed = "corrected", x_beta_lin_com = cAIC_model_set$X_cluster_full )

Construct post-OBSP CI for mixed parameters ------------------

postOBSP_CI_results = postOBSP_CI( X, y, clusterID, X_cluster_full = cAIC_model_set$X_cluster_full, model = "NERM", covariate_selection_matrix = NULL, modelset = "part_subset", intercept = FALSE, common = c(1:2), boot = 500 )

results_to_plot = format_results(x = naive_CI_results, y = postcAIC_CI_results, z = postOBSP_CI_results, type = "corrected")

plot(results_to_plot, type = "corrected") ```

Simulation study from CRS-2021

All simulations in Claeskens, Reluga, and Sperlich (2021) can be reproduced using functions in package postcAIC.

Data illustration from CRS-2021

Claeskens, Reluga, and Sperlich (2021) illustrated the performance of post-cAIC CI for mixed and fixed effects using a subset of NHAENS R-package. The subset and the code to construct it can be found in package postcAIC together with the code to analyse the results.

KatarzynaReluga/postcAIC documentation built on Jan. 25, 2022, 12:33 a.m.