gorica
Evaluation of Inequality Constrained Hypotheses Using GORICA
Implements the generalized order-restricted information criterion
approximation (GORICA), an AIC-like information criterion that can be
utilized to evaluate informative hypotheses specifying directional
relationships between model parameters in terms of (in)equality
constraints (see Altinisik, Van Lissa, Hoijtink, Oldehinkel, & Kuiper,
2021),
GORICA: Evaluation of Inequality Constrained Hypotheses Using Generalized AIC
Implements the generalized order-restricted information criterion approximation (GORICA). The GORICA can be utilized to evaluate (in)equality constrained hypotheses. The GORICA is applicable not only to normal linear models, but also to generalized linear models (GLMs), generalized linear mixed models (GLMMs), and structural equation models (SEMs). In addition, the GORICA can be utilized in the context of contingency tables for which (in)equality constrained hypotheses do not necessarily contain linear restrictions on cell probabilities, but instead often contain non-linear restrictions on cell probabilities.
Installation
You can install gorica from GitHub with:
# install.packages("devtools")
devtools::install_github("cjvanlissa/gorica")
Workflow
Add gorica to your existing R workflow, and evaluate informative hypotheses for your familiar R analyses! Here is an example for testing an informative hypothesis about mean differences in an ANOVA:
res <- lm(Sepal.Length ~ -1 + Species, data = iris)
gorica(res, "Speciessetosa < Speciesversicolor = Speciesvirginica; Speciessetosa < Speciesversicolor < Speciesvirginica")
#> Informative hypothesis test for an object of class lm:
#>
#> loglik penalty gorica gorica_weights
#> H1 -14.948 1.500 32.897 0.000
#> H2 5.103 1.834 -6.539 0.762
#> Hu 5.103 3.000 -4.206 0.238
#>
#> Hypotheses:
#> H1: Speciessetosa<Speciesversicolor=Speciesvirginica
#> H2: Speciessetosa<Speciesversicolor<Speciesvirginica
#> Hu: Unconstrained hypothesis
#>