Post-Estimation Functions for Generalized Linear Mixed Models

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Documentation for package ‘catregs’ version 0.2.1

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compare.margins Compares two marginal effects (MEMs or AMEs). Estimate of uncertainty is from a simulated draw from a normal distribution.
count.fit Fits four different count models and compares them.
diagn Computes diagnostics for generalized linear models.
ess A subset of data from the European Social Survey
essUK A subset of data from the European Social Survey
first.diff.fitted Computes the first difference in fitted values, or a series of first differences. Inference in supported via the delta method or bootstrapping.
gss2016 Data from the 2016 General Social Survey.
LF06art Data to replicate Long and Freese's (2006) count models (pp354-414)
LF06travel Travel time example data for alternative-specific outcomes.
list.coef Transform glm and mixed model objects into model summaries that include coefficients, standard errors, exponentiated coefficients, confidence intervals and percent change.
logan Replication data for Logan's (1983) application of conditional logistic regression to mobility processes.
margins.dat Add model predictions, standard errors and confidence intervals to a design matrix for a model object.
margins.dat.clogit Computes predicted probabilities for conditional and rank-order/exploded logistic regression models. Inference is based upon simulation techniques (requires the MASS package). Alternatively, bootstrapping is an option for conditional logistic regression models.
margins.des Creates a design matrix of idealized data for illustrating model predictions.
Mize19AH Add-Health Data analzed in Mize (2019)
Mize19GSS General Social Survey Data analzed in Mize (2019)
rubins.rule Aggregate Standard Errors using Rubin's Rule.
second.diff.fitted Computes the second difference in fitted values. Inference in supported via the delta method or bootstrapping.
wagepan Data to illustrate mixed effects regression models with serial correlation.