mc_sic: Score Information Criterion - Regression

In wbonat/mcglm: Multivariate Covariance Generalized Linear Models

Description

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

Usage

mc_sic(object, scope, data, response, penalty = 2, weights)

Arguments

object	an object of mcglm class.
scope	a vector of covariate names to be tested.
data	data set containing all variables involved in the model.
response	index indicating for which response variable the SIC should be computed.
penalty	penalty term (default = 2).
weights	Vector of weights for model fitting.

Value

A data frame containing SIC values, degree of freedom, Tu-statistics and chi-squared reference values.

Author(s)

Wagner Hugo Bonat, [e-mail]

Source

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

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.

See Also

mc_sic_covariance.

Examples

set.seed(123)
x1 <- runif(100, -1, 1)
x2 <- gl(2,50)
beta = c(5, 0, 3)
X <- model.matrix(~ x1 + x2)
y <- rnorm(100, mean = X%*%beta , sd = 1)
data <- data.frame(y, x1, x2)
# Reference model
fit0 <- mcglm(c(y ~ 1), list(mc_id(data)), data = data)
# Computing SIC
mc_sic(fit0, scope = c("x1","x2"), data = data, response = 1)

wbonat/mcglm documentation built on June 23, 2020, 11:06 a.m.