# Creating a coefficient table

#### Jared Fowler

`creating_pretty_coefficients.Rmd`

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

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)
```