mc_sic_covariance: Score Information Criterion - Covariance

In mcglm: Multivariate Covariance Generalized Linear Models

Score Information Criterion - Covariance

Description

Compute the score information criterion (SIC) for an object of mcglm class. The SIC-covariance is useful for selecting the components of the matrix linear predictor. It can be used to construct an stepwise procedure to select the components of the matrix linear predictor.

Usage

mc_sic_covariance(object, scope, idx, data, penalty = 2, response, weights)

Arguments

object	an object of mcglm class.
scope	a list of matrices to be tested.
idx	indicator of matrices belong to the same effect. It is useful for the case where more than one matrix represents the same effect.
data	data set containing all variables involved in the model.
penalty	penalty term (default = 2).
response	index indicating for which response variable SIC-covariance should be computed.
weights	Vector of weights for model fitting.

Value

A data frame containing SIC-covariance values, degree of freedom, Tu-statistics and chi-squared reference values for each matrix in the scope argument.

Author(s)

Wagner Hugo Bonat, [e-mail]

Source

Bonat, et. al. (2016). Modelling the covariance structure in marginal multivariate count models: Hunting in Bioko Island. Journal of Agricultural Biological and Environmental Statistics, 22(4):446–464.

Bonat, W. H. (2018). Multiple Response Variables Regression Models in R: The mcglm Package. Journal of Statistical Software, 84(4):1–30.

See Also

mc_sic.

Examples

set.seed(123)
SUBJECT <- gl(10, 10)
y <- rnorm(100)
data <- data.frame(y, SUBJECT)
Z0 <- mc_id(data)
Z1 <- mc_mixed(~0+SUBJECT, data = data)
# Reference model
fit0 <- mcglm(c(y ~ 1), list(Z0), data = data)
# Testing the effect of the matrix Z1
mc_sic_covariance(fit0, scope = Z1, idx = 1,
data = data, response = 1)
# As expected Tu < Chisq indicating non-significance of Z1 matrix

mcglm documentation built on Sept. 16, 2022, 1:06 a.m.