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.
<- c('job',
columns_to_factor 'marital',
'education',
'default',
'housing',
'loan')
<- bank_data %>%
bank_data ::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 dplyr
```

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.

```
<- stats::glm(T_DEPOSIT ~ marital +
deposit_model :loan +
default+
loan
age,data = bank_data,
family = binomial)
```

`pretty_coefficients()`

`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 the`relativity_label`

input.)You can return the data set instead of

`kable`

but setting`Return_Data = TRUE`

`pretty_coefficients(deposit_model, type_iii = 'Wald')`

`pretty_relativities()`

- 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()`

`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()`

`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)
```