One of the main advantages of using Generalised Linear Models is their interpretability. The goal of prettyglm is to provide a set of functions which easily create beautiful coefficient summaries which can readily be shared and explained.
prettyglm
was created to solve some common faced when building
Generalised Linear Models, such as displaying categorical base levels,
and visualizing the number of records in each category on a duel axis.
Since then a number of other functions which are useful when fitting
glms have been added.
If you don’t find the function you are looking for here consider
checking out some other great packages which help visualize the output
from glms:tidycat
, jtools
or GGally
You can install the latest CRAN release with:
install.packages('prettyglm')
Please see the website prettyglm for more detailed documentation and examples.
To explore the functionality of prettyglm we will use a data set sourced
from
kaggle
which contains information about a Portugal banks marketing campaigns
results. The campaign was based mostly on direct phone calls, offering
clients a term deposit. The target variable y
indicates if the client
agreed to place the deposit after the phone call.
A critical step for this package to work well is to set all categorical predictors as factors.
library(prettyglm)
library(dplyr)
data("bank")
# Easiest way to convert multiple columns to a factor.
columns_to_factor <- c('job',
'marital',
'education',
'default',
'housing',
'loan')
bank_data <- bank_data %>%
dplyr::filter(loan != 'unknown') %>%
dplyr::filter(default != 'yes') %>%
dplyr::mutate(age = as.numeric(age)) %>%
dplyr::mutate_at(columns_to_factor, list(~factor(.))) %>% # multiple columns to factor
dplyr::mutate(T_DEPOSIT = as.factor(base::ifelse(y=='yes',1,0))) #convert target to 0 and 1 for performance plots
For this example we will build a glm using stats::glm()
, however
prettyglm
is working to support parsnip
and workflow
model objects
which use the glm model engine.
deposit_model <- stats::glm(T_DEPOSIT ~ marital +
default:loan +
loan +
age,
data = bank_data,
family = binomial)
-
pretty_coefficients()
automatically includes categorical variable base levels. -
You can complete a type III test on the coefficients by specifying a
type_iii
argument. -
You can include a “relativity” column in the output by including a
relativity_transform
input. (Note “relativity” is sometimes referred to as “likelihood” or “odds-ratio”, you can change the title of this column with therelativity_label
input.) -
You can return the data set instead of
kable
but settingReturn_Data = TRUE
pretty_coefficients(deposit_model, type_iii = 'Wald')
- 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. pretty_relativities()
automatically extracts the training data from the model object and plots the number of records on the second y axis.
pretty_relativities(feature_to_plot = 'marital',
model_object = deposit_model)
- If the variable you are plotting is a continuous variable
prettyglm
will plot the density on a second axis, and attempt to plot the fit with confidence intervals.
pretty_relativities(feature_to_plot = 'age',
model_object = deposit_model)
- For interactions you can colour or facet by one of the variables.
pretty_relativities(feature_to_plot = 'default:loan',
model_object = deposit_model,
iteractionplottype = 'colour',
facetorcolourby = 'loan')
one_way_ave()
creates one-way model performance plots.
For discrete variables the number of records in each group will be plotted on a second axis.
one_way_ave(feature_to_plot = 'education',
model_object = deposit_model,
target_variable = 'T_DEPOSIT',
data_set = bank_data)
For continuous variables the stats::density()
will be plotted on a
second axis.
one_way_ave(feature_to_plot = 'age',
model_object = deposit_model,
target_variable = 'T_DEPOSIT',
data_set = bank_data)
actual_expected_bucketed()
creates actual vs expected performance
plots by predicted band.
actual_expected_bucketed(target_variable = 'T_DEPOSIT',
model_object = deposit_model,
data_set = bank_data)