Creating a coefficient table

Jared Fowler

The function pretty_coefficients() allows you to create a pretty table of model coefficients. The table created will automatically include categorical variable base levels, and highlight insignificant p-values.

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

Table of model coefficients

• Simple Example
• Type III Significance Tests
• Changing Variable Importance Method

The simplest way to call this function is just with the model object.

pretty_coefficients(model_object = survival_model)

You can also complete a type III test on the coefficients by specifying a type_iii argument. Warning Wald type III tests will fail if there are aliased coefficients in the model.

You can change the significance level highlighted in the table with significance_level.

pretty_coefficients(survival_model, 
                    type_iii = 'Wald', 
                    significance_level = 0.1)

By default pretty_coefficients shows “model” variable importance. But vimethod also accepts “permute” and “firm” methods from . Additional parameters for these methods should also be passed into pretty_coefficients().

pretty_coefficients(model_object = survival_model,
                    type_iii = 'Wald', 
                    significance_level = 0.1, 
                    vimethod = 'permute', 
                    target = 'Survived', 
                    metric = 'auc',
                    pred_wrapper = predict.glm, 
                    reference_class = 0)