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Last data update: 2014.03.03

R: Score Information Criterion - Regression

mc_sic

R Documentation

Score Information Criterion - Regression

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)

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).

Value

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

Author(s)

Wagner Hugo Bonat, wbonat@ufpr.br

Source

Bonat, W. H. (2016). Multiple Response Variables Regression Models in R: The mcglm Package. Journal of Statistical Software, submitted.

Bonat, et. al. (2016). Modelling the covariance structure in marginal multivariate count models: Hunting in Bioko Island. Environmetrics, submitted.

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)