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.
mc_sic_covariance(object, scope, idx, data, penalty = 2, response, weights)
| object | an object of |
|---|---|
| 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. |
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.
A data frame containing SIC-covariance values, degree of freedom, Tu-statistics and chi-squared reference values for each matrix in the scope argument.
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)#> Automatic initial values selected.# Testing the effect of the matrix Z1 mc_sic_covariance(fit0, scope = Z1, idx = 1, data = data, response = 1)#> SIC df df_total Tu Chisq #> 1 3.925097 1 2 0.07490337 3.841459# As expected Tu < Chisq indicating non-significance of Z1 matrix