postOBSP_CI: Post-OBSP confidence intervals (CI)

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

Description

Function postOBSP_CI provides post-OBSP confidence intervals for mixed parameters

Usage

postOBSP_CI(
  X,
  y,
  clusterID,
  X_cluster_full = NULL,
  model = "NERM",
  covariate_selection_matrix = NULL,
  modelset = "all_subsets",
  intercept = FALSE,
  common = NULL,
  boot = 1000,
  alpha = 0.05
)

Arguments

X	Matrix with covariates for fixed effects
y	Vector of responses
clusterID	Vector with cluster labels
X_cluster_full	Matrix with cluster level covariates for fixed effects of the full model. Default: NULL
model	Type of mixed model: NERM, FHM, RIRS (random slopes and random intercepts)
covariate_selection_matrix	Matrix composed of zeros and ones indicating fixed models in each parameter. Default: NULL
modelset	Type of model set: "all_subsets" - all possible models using all covariates "nested" - a sequence of nested models using all covariates "part_subset" - all possible models using a subset of covariates
intercept	Is column of ones, representing the intercept, present in X Default: TRUE
common	A vector indicating variables forced to be present in each model. Default: NULL
boot	Number of bootstrap samples. Default: boot = 1000
alpha	Construct 1 - alpha confidence intervals. Default: alpha = 0.05

Details

Parameter boot is needed for the calculation of the bootstrap post-OBSP MSE of mixed effects.

Value

List with elements:

OBSP_min	Index of the selected model
OBSP_models	cAIC for all considered parameters
postOBSP_up	Upper boundary of CI for mixed effects
postOBSP_do	Lower boundary of CI for mixed effects
mu_hat_sel	Mixed effects of the selected model

Examples

# Define basic parameters -------------------------------------------------
n = 15
m_i = 5
m_total = n * m_i

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

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

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

# Create responses, errors and random effects  -------------------
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

# Post-OBSP inference ----------------------------------------

postOBSP_CI_results = postOBSP_CI(X, y,
                                  clusterID,
                                  X_cluster_full = NULL,
                                  model = "NERM",
                                  covariate_selection_matrix = NULL,
                                  modelset  = "part_subset",
                                  intercept = FALSE,
                                  common = c(1:2),
                                  boot = 250)
                                  
plot(postOBSP_CI_results)                                   

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