In jared-fowler/prettyglm: Pretty Summaries of Generalized Linear Model Coefficients

The function pretty_relativities() creates a plot to show the fit of the desired predictor. This vignette shows how to create plots for interacted variables.

Pre-processing

A critical step for this package to work is to set all categorical predictors as factors.

library(dplyr)
library(prettyglm)
data('titanic')

# Easy way to convert multiple columns to a factor.
columns_to_factor <- c('Pclass',
                       'Sex',
                       'Cabin', 
                       'Embarked',
                       'Cabintype')
meanage <- base::mean(titanic$Age, na.rm=T)
titanic  <- titanic  %>%
  dplyr::mutate_at(columns_to_factor, list(~factor(.))) %>%
  dplyr::mutate(Age =base::ifelse(is.na(Age)==T,meanage,Age)) 

# Build a basic glm
survival_model2 <- stats::glm(Survived ~ Pclass:Fare +
                                         Age +
                                         Embarked:Sex +
                                         SibSp +
                                         Parch,
                              data = titanic,
                              family = binomial(link = 'logit'))

Plot interacted coefficients

A model relativity is a transform of the model estimate. By default pretty_relativities() uses 'exp(estimate)-1' which is useful for GLM's which use a log or logit link function.

prettyglm currently only supports interactions of two variables.

Factor:Factor Interactions {.tabset}

Standard

You can create these relativity plots as you would for a non-interaction.

pretty_relativities(feature_to_plot= 'Embarked:Sex',
                    model_object = survival_model2,
                    relativity_label = 'Liklihood of Survival'
                    )

Facet

You can also choose to facet the plots by one of the variables.

pretty_relativities(feature_to_plot= 'Embarked:Sex',
                    model_object = survival_model2,
                    relativity_label = 'Liklihood of Survival',
                    iteractionplottype = 'facet',
                    facetorcolourby = 'Sex'
                    )

Colour

You can also choose to colour the plots by one of the variables.

pretty_relativities(feature_to_plot= 'Embarked:Sex',
                    model_object = survival_model2,
                    relativity_label = 'Liklihood of Survival',
                    iteractionplottype = 'colour',
                    facetorcolourby = 'Embarked'
                    )

Continuous:Factor Interactions {.tabset}

Colour

By default continuous and factor interaction plots will colour by the factor variable.

pretty_relativities(feature_to_plot= 'Pclass:Fare',
                    model_object = survival_model2,
                    relativity_label = 'Liklihood of Survival',
                    upper_percentile_to_cut = 0.03
                    )

Facet

You can also facet by the factor variable. ERROR HERRER

pretty_relativities(feature_to_plot= 'Pclass:Fare',
                    model_object = survival_model2,
                    relativity_label = 'Liklihood of Survival',
                    iteractionplottype = 'facet',
                    upper_percentile_to_cut = 0.03,
                    height = 800
                    )