prettyglm (version 1.0.1)

pretty_coefficients: pretty_coefficients

Description

Creates a pretty kable of model coefficients including coefficient base levels, type III P.values, and variable importance.

Usage

pretty_coefficients(
  model_object,
  relativity_transform = NULL,
  relativity_label = "relativity",
  type_iii = NULL,
  conf.int = FALSE,
  vimethod = "model",
  spline_seperator = NULL,
  significance_level = 0.05,
  return_data = FALSE,
  ...
)

Value

kable if return_data = FALSE. data.frame if return_data = TRUE.

Arguments

model_object

Model object to create coefficient table for. Must be of type: glm, lm.

relativity_transform

String of the function to be applied to the model estimate to calculate the relativity, for example: 'exp(estimate)-1'. Default is for relativity to be excluded from output.

relativity_label

String of label to give to relativity column if you want to change the title to your use case.

type_iii

Type III statistical test to perform. Default is none. Options are 'Wald' or 'LR'. Warning 'LR' can be computationally expensive. Test performed via Anova

conf.int

Set to TRUE to include confidence intervals in summary table. Warning, can be computationally expensive.

vimethod

Variable importance method to pass to method of vi. Defaults to "model". Currently supports "permute" and "firm", pass any additional arguments to vi in ...

spline_seperator

Separator to look for to identity a spline. If this input is not null, it is assumed any features with this separator are spline columns. For example an age spline from 0 to 25 you could use: AGE_0_25 and "_".

significance_level

Significance level to P-values by in kable. Defaults to 0.05.

return_data

Set to TRUE to return data.frame instead of creating kable.

...

Any additional parameters to be past to vi

Examples

library(dplyr)
library(prettyglm)
data('titanic')
columns_to_factor <- c('Pclass',
                       'Sex',
                       'Cabin',
                       'Embarked',
                       'Cabintype',
                       'Survived')
meanage <- base::mean(titanic$Age, na.rm=TRUE)

titanic  <- titanic  %>%
 dplyr::mutate_at(columns_to_factor, list(~factor(.))) %>%
 dplyr::mutate(Age =base::ifelse(is.na(Age)==TRUE,meanage,Age)) %>%
 dplyr::mutate(Age_0_25 = prettyglm::splineit(Age,0,25),
               Age_25_50 = prettyglm::splineit(Age,25,50),
               Age_50_120 = prettyglm::splineit(Age,50,120)) %>%
 dplyr::mutate(Fare_0_250 = prettyglm::splineit(Fare,0,250),
               Fare_250_600 = prettyglm::splineit(Fare,250,600))

# A simple example
survival_model <- stats::glm(Survived ~
                              Pclass +
                              Sex +
                              Age +
                              Fare +
                              Embarked +
                              SibSp +
                              Parch +
                              Cabintype,
                             data = titanic,
                             family = binomial(link = 'logit'))
pretty_coefficients(survival_model)

# A more complicated example with a spline and different importance method
survival_model3 <- stats::glm(Survived ~
                                        Pclass +
                                        Age_0_25 +
                                        Age_25_50 +
                                        Age_50_120 +
                                        Sex:Fare_0_250 +
                                        Sex:Fare_250_600 +
                                        Embarked +
                                        SibSp +
                                        Parch +
                                        Cabintype,
                              data = titanic,
                              family = binomial(link = 'logit'))
pretty_coefficients(survival_model3,
                    relativity_transform = 'exp(estimate)-1',
                    spline_seperator = '_',
                    vimethod = 'permute',
                    target = 'Survived',
                    metric = "roc_auc",
                    event_level = 'second',
                    pred_wrapper = predict.glm,
                    smaller_is_better = FALSE,
                    train = survival_model3$data, # need to supply training data for vip importance
                    reference_class = 0)