Score Information Criterion - Regression

Source: R/mc_sic.R

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.

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.

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.

Value

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

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)
#> Automatic initial values selected. 
# Computing SIC
mc_sic(fit0, scope = c("x1","x2"), data = data, response = 1)
#>          SIC Covariates df df_total         Tu    Chisq
#> 1   3.252005         x1  1        2  0.7479951 3.841459
#> 2 -61.062401         x2  1        2 65.0624012 3.841459