Package 'prettyglm'

actual_expected_bucketed

Description

Provides a rank plot of the actual and predicted.

Usage

actual_expected_bucketed(
  target_variable,
  model_object,
  data_set = NULL,
  number_of_buckets = 25,
  ylab = "Target",
  width = 800,
  height = 500,
  first_colour = "black",
  second_colour = "#cc4678",
  facetby = NULL,
  prediction_type = "response",
  predict_function = NULL,
  return_data = F
)

Arguments

target_variable	String of target variable name.
model_object	GLM model object.
data_set	Data to score the model on. This can be training or test data, as long as the data is in a form where the model object can make predictions. Currently developing ability to provide custom prediction functions, currently implementation defaults to 'stats::predict'
number_of_buckets	number of buckets for percentile
ylab	Y-axis label.
width	plotly plot width in pixels.
height	plotly plot height in pixels.
first_colour	First colour to plot, usually the colour of actual.
second_colour	Second colour to plot, usually the colour of predicted.
facetby	variable user wants to facet by.
prediction_type	Prediction type to be pasted to predict.glm if predict_function is NULL. Defaults to "response".
predict_function	prediction function to use. Still in development.
return_data	Logical to return cleaned data set instead of plot.

Value

plot Plotly plot by defualt. ggplot if plotlyplot = F. Tibble if return_data = T.

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))

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

prettyglm::actual_expected_bucketed(target_variable = 'Survived',
                                    model_object = survival_model,
                                    data_set = titanic)

---

Bank marketing campaigns data set analysis

Description

It is a dataset that describing Portugal bank marketing campaigns results. Conducted campaigns were based mostly on direct phone calls, offering bank client to place a term deposit. If after all marking efforts client had agreed to place deposit - target variable marked 'yes', otherwise 'no'

Usage

data(bank)

Format

An object of class "data.frame"

job

Type of job

marital

marital status

education

education

default

has credit in default?

housing

has housing loan?

loan

has personal loan?

age

age

y

has the client subscribed a term deposit? (binary: "yes","no")

Details

Sourse of the data https://archive.ics.uci.edu/ml/datasets/bank+marketing

References

This dataset is public available for research. The details are described in S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

Examples

data(bank)
head(bank_data)

---

clean_coefficients

Description

Processing to split out base levels and add variable importance to each term. Inspired by 'tidycat::tidy_categorical()', modified for use in prettyglm..

Usage

clean_coefficients(
  d = NULL,
  m = NULL,
  vimethod = "model",
  spline_seperator = NULL,
  ...
)

Arguments

d	Data frame tibble output from tidy.lm; with one row for each term in the regression, including column 'term'
m	Model object glm
vimethod	Variable importance method. Still in development
spline_seperator	Sting of the spline separator. For example AGE_0_25 would be "_"
...	Any additional parameters to be past to vi

Value

Expanded tibble from the version passed to 'd' including additional columns:

variable	The name of the variable that the regression term belongs to.
level	The level of the categorical variable that the regression term belongs to. Will be an the term name for numeric variables.

Author(s)

Jared Fowler, Guy J. Abel

See Also

tidy.lm

---

cut3

Description

Hmisc::cut2 bones repackaged to remove errors with importing Hmisc

Usage

cut3(
  x,
  cuts,
  m = 150,
  g,
  digits,
  minmax = TRUE,
  oneval = TRUE,
  onlycuts = FALSE,
  formatfun = format,
  ...
)

Arguments

x	numeric vector to classify into intervals.
cuts	cut points.
m	desired minimum number of observations in a group. The algorithm does not guarantee that all groups will have at least m observations.
g	number of quantile groups
digits	number of significant digits to use in constructing levels.
minmax	if cuts is specified but min(x)<min(cuts) or max(x)>max(cuts), augments cuts to include min and max x
oneval	if an interval contains only one unique value, the interval will be labeled with the formatted version of that value instead of the interval endpoints, unless oneval=FALSE
onlycuts	set to TRUE to only return the vector of computed cuts. This consists of the interior values plus outer ranges.
formatfun	format function
...	additional arguments passed to formatfun

Value

vector of cut

---

one_way_ave

Description

Creates a pretty html plot of one way actual vs expected by specified predictor.

Usage

one_way_ave(
  feature_to_plot,
  model_object,
  target_variable,
  data_set,
  plot_type = "predictions",
  plot_factor_as_numeric = FALSE,
  ordering = NULL,
  width = 800,
  height = 500,
  number_of_buckets = 30,
  first_colour = "black",
  second_colour = "#cc4678",
  facetby = NULL,
  prediction_type = "response",
  predict_function = NULL,
  upper_percentile_to_cut = 0.01,
  lower_percentile_to_cut = 0
)

Arguments

feature_to_plot	A string of the variable to plot.
model_object	Model object to create coefficient table for. Must be of type: glm, lm
target_variable	String of target variable name in dataset.
data_set	Data set to calculate the actual vs expected for. If no input default is to try and extract training data from model object.
plot_type	one of "Residual", "predictions" or "actuals" defaults to "predictions"
plot_factor_as_numeric	Set to TRUE to return data.frame instead of creating kable.
ordering	Option to change the ordering of categories on the x axis, only for discrete categories. Default to the ordering of the factor. Other options are: 'alphabetical', 'Number of records', 'Average Value'
width	Width of plot
height	Height of plot
number_of_buckets	Number of buckets for continuous variable plots
first_colour	First colour to plot, usually the colour of actual.
second_colour	Second colour to plot, usually the colour of predicted.
facetby	Variable to facet the actual vs expect plots by.
prediction_type	Prediction type to be pasted to predict.glm if predict_function is NULL. Defaults to "response".
predict_function	A custom prediction function can be provided here.It must return a data.frame with an "Actual_Values" column, and a "Predicted_Values" column.
upper_percentile_to_cut	For continuous variables this is what percentile to exclude from the upper end of the distribution. Defaults to 0.01, so the maximum percentile of the variable in the plot will be 0.99. Cutting off some of the distribution can help the views if outlier's are present in the data.
lower_percentile_to_cut	For continuous variables this is what percentile to exclude from the lower end of the distribution. Defaults to 0.01, so the minimum percentile of the variable in the plot will be 0.01. Cutting off some of the distribution can help the views if outlier's are present in the data.

Value

plotly plot of one way actual vs expected.

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))

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

# Continuous Variable Example
one_way_ave(feature_to_plot = 'Age',
            model_object = survival_model,
            target_variable = 'Survived',
            data_set = titanic,
            number_of_buckets = 20,
            upper_percentile_to_cut = 0.1,
            lower_percentile_to_cut = 0.1)

# Discrete Variable Example
one_way_ave(feature_to_plot = 'Pclass',
            model_object = survival_model,
            target_variable = 'Survived',
            data_set = titanic)

# Custom Predict Function and facet
a_custom_predict_function <- function(target, model_object, dataset){
  dataset <- base::as.data.frame(dataset)
  Actual_Values <- dplyr::pull(dplyr::select(dataset, tidyselect::all_of(c(target))))
  if(class(Actual_Values) == 'factor'){
    Actual_Values <- base::as.numeric(as.character(Actual_Values))
  }
  Predicted_Values <- base::as.numeric(stats::predict(model_object, dataset, type='response'))

  to_return <-  base::data.frame(Actual_Values = Actual_Values,
                                 Predicted_Values = Predicted_Values)

  to_return <- to_return %>%
    dplyr::mutate(Predicted_Values = base::ifelse(Predicted_Values > 0.3,0.3,Predicted_Values))
  return(to_return)
}

one_way_ave(feature_to_plot = 'Age',
            model_object = survival_model,
            target_variable = 'Survived',
            data_set = titanic,
            number_of_buckets = 20,
            upper_percentile_to_cut = 0.1,
            lower_percentile_to_cut = 0.1,
            predict_function = a_custom_predict_function,
            facetby = 'Pclass')

---

predict_outcome

Description

Processing to predict response for various actual vs expected plots

Usage

predict_outcome(
  target,
  model_object,
  dataset,
  prediction_type = NULL,
  weights = NULL
)

Arguments

target	String of target variable name.
model_object	Model object. prettyglm currently supports
dataset	This is used to plot the number in each class as a barchart if plotly is TRUE.
prediction_type	type of prediction to be passed to the model object. For ...GLM defaults to ....
weights	weightings to be provided to predictions if required.

Value

dataframe

Returns a dataframe of Actual and Predicted Values

Author(s)

Jared Fowler

See Also

tidy.lm

---

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,
  ...
)

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

Value

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

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)

---

pretty_relativities

Description

Creates a pretty html plot of model relativities including base Levels.

Usage

pretty_relativities(
  feature_to_plot,
  model_object,
  plot_approx_ci = TRUE,
  relativity_transform = "exp(estimate)-1",
  relativity_label = "Relativity",
  ordering = NULL,
  plot_factor_as_numeric = FALSE,
  width = 800,
  height = 500,
  iteractionplottype = NULL,
  facetorcolourby = NULL,
  upper_percentile_to_cut = 0.01,
  lower_percentile_to_cut = 0,
  spline_seperator = NULL
)

Arguments

feature_to_plot	A string of the variable to plot.
model_object	Model object to create coefficient table for. Must be of type: glm, lm
plot_approx_ci	Set to TRUE to include confidence intervals in summary table. Warning, can be computationally expensive.
relativity_transform	String of the function to be applied to the model estimate to calculate the relativity, for example: 'exp(estimate)'. Default is for relativity to be 'exp(estimate)-1'.
relativity_label	String of label to give to relativity column if you want to change the title to your use case, some users may prefer to refer to this as odds ratio.
ordering	Option to change the ordering of categories on the x axis, only for discrete categories. Default to the ordering of the fitted factor. Other options are: 'alphabetical', 'Number of records', 'Average Value'
plot_factor_as_numeric	Set to TRUE to return data.frame instead of creating kable.
width	Width of plot
height	Height of plot
iteractionplottype	If plotting the relativity for an interaction variable you can "facet" or "colour" by one of the interaction variables. Defaults to null.
facetorcolourby	If iteractionplottype is not Null, then this is the variable in the interaction you want to colour or facet by.
upper_percentile_to_cut	For continuous variables this is what percentile to exclude from the upper end of the distribution. Defaults to 0.01, so the maximum percentile of the variable in the plot will be 0.99. Cutting off some of the distribution can help the views if outlier's are present in the data.
lower_percentile_to_cut	For continuous variables this is what percentile to exclude from the lower end of the distribution. Defaults to 0.01, so the mimimum percentile of the variable in the plot will be 0.01. Cutting off some of the distribution can help the views if outlier's are present in the data.
spline_seperator	string of the spline separator. For example AGE_0_25 would be "_".

Value

plotly plot of fitted relativities.

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))

survival_model3 <- stats::glm(Survived ~
                                Pclass:Embarked +
                                Age_0_25  +
                                Age_25_50 +
                                Age_50_120  +
                                Sex:Fare_0_250 +
                                Sex:Fare_250_600 +
                                SibSp +
                                Parch +
                                Cabintype,
                              data = titanic,
                              family = binomial(link = 'logit'))

# categorical factor
pretty_relativities(feature_to_plot = 'Cabintype',
                    model_object = survival_model3)

# continuous factor
pretty_relativities(feature_to_plot = 'Parch',
                    model_object = survival_model3)

# splined continuous factor
pretty_relativities(feature_to_plot = 'Age',
                    model_object = survival_model3,
                    spline_seperator = '_',
                    upper_percentile_to_cut = 0.01,
                    lower_percentile_to_cut = 0.01)

# factor factor interaction
pretty_relativities(feature_to_plot = 'Pclass:Embarked',
                    model_object = survival_model3,
                    iteractionplottype = 'colour',
                    facetorcolourby = 'Pclass')

# Continuous spline and categorical by colour
pretty_relativities(feature_to_plot = 'Sex:Fare',
                    model_object = survival_model3,
                    spline_seperator = '_')

# Continuous spline and categorical by facet
pretty_relativities(feature_to_plot = 'Sex:Fare',
                    model_object = survival_model3,
                    spline_seperator = '_',
                    iteractionplottype = 'facet')

---

splineit

Description

Splines a continuous variable

Usage

splineit(var, min, max)

Arguments

var	Continuous vector to spline.
min	Min of spline.
max	Max of spline.

Value

Splined Column

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))

---

Titanic Data

Description

The sinking of the Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew. While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others. In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).

Usage

data(titanic)

Format

An object of class "data.frame"

survival

Survival

pclass

Ticket class

sex

Sex

Age

Age in years

sibsp

number of siblings / spouses

parch

number of parents / children

ticket

Ticket number

fare

Passenger fare

cabin

Cabin Number

cabintype

Type of cabin

embarked

Port of Embarkation

References

This data set sourced from https://www.kaggle.com/c/titanic/data?select=train.csv

Examples

data(titanic)
head(titanic)

---