Week 7 coding

Paths: Graphical models and considerations for causal analysis

The application workshop will use functions from the following packages:

Package : : Function Purpose

ggeffects After fitting a model, it is useful generate model-based estimates (expected values, or adjusted predictions) of the response variable for different combinations of predictor values. Such estimates can be used to make inferences about relationships between variables. The ggeffects package computes marginal means and adjusted predicted values for the response, at the margin of specific values or levels from certain model terms. The package is built around three core functions: predict_response() (understanding results), test_predictions() (testing results for statistically significant differences) and plot() (communicate results).

insight When fitting any statistical model, there are many useful pieces of information that are simultaneously calculated and stored beyond coefficient estimates and general model fit statistics. The goal of insight, then, is to provide tools for intuitive, and consistent access to information contained in model objects. The basic model_info() function provides a clean and consistent overview of model objects (e.g., functional form of the model, the model family, link function, number of observations, variables included in the specification, etc.).

Package : : Function	Purpose
ggeffects	After fitting a model, it is useful generate model-based estimates (expected values, or adjusted predictions) of the response variable for different combinations of predictor values. Such estimates can be used to make inferences about relationships between variables. The ggeffects package computes marginal means and adjusted predicted values for the response, at the margin of specific values or levels from certain model terms. The package is built around three core functions: `predict_response()` (understanding results), `test_predictions()` (testing results for statistically significant differences) and `plot()` (communicate results).
insight	When fitting any statistical model, there are many useful pieces of information that are simultaneously calculated and stored beyond coefficient estimates and general model fit statistics. The goal of insight, then, is to provide tools for intuitive, and consistent access to information contained in model objects. The basic `model_info()` function provides a clean and consistent overview of model objects (e.g., functional form of the model, the model family, link function, number of observations, variables included in the specification, etc.).

Explore the package websites for an overview of the purposes and the main functions of these packages.