Creating simple relativity plots

Jared Fowler

The function pretty_relativities() creates a plot to show the fit of the desired predictor. A different plot will be generated depending on the class of the variable.

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)) %>% 
  dplyr::mutate(Age_Cat = prettyglm::cut3(Age, levels.mean = TRUE, g =10))

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

Plot 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.

The term ‘relativity’ is some times referred to as “odds-ratio” or “Likelihood”. You can customize the label with the relativity_label input.

• Categorical Variables
• Categorical Variables With Numeric Axis
• Continuous Variables

For categorical variables pretty_relativities() creates an interactive duel axis plot, which plots the fitted relativity on one y axis, and the number of records in that category on the other y axis.

pretty_relativities(feature_to_plot= 'Embarked',
                    model_object = survival_model,
                    relativity_label = 'Liklihood of Survival'
                    )

If you want to plot a categorical variable on a numeric axis. You can set plot_factor_as_numeric equal to TRUE.

pretty_relativities(feature_to_plot= 'Age_Cat',
                    model_object = survival_model,
                    relativity_label = 'Liklihood of Survival',
                    plot_factor_as_numeric = T
                    )

For continuous variables pretty_relativities() will plot the relativity over the variables range, and the density of that variable on a duel axis.

If desired you can cut off the tail end of the distributions with upper_percentile_to_cut or lower_percentile_to_cut.

pretty_relativities(feature_to_plot= 'Fare',
                    model_object = survival_model,
                    relativity_label = 'Liklihood of Survival',
                    upper_percentile_to_cut = 0.1)