mc_sic_covariance: Score Information Criterion - Covariance
In wbonat/mcglm: Multivariate Covariance Generalized Linear Models

Description Usage Arguments Value Author(s) Source See Also Examples

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.

1 2	mc_sic_covariance(object, scope, idx, data, penalty = 2, response, weights)

`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.

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

Wagner Hugo Bonat, wbonat@ufpr.br

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.

mc_sic.

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