one_way_ave: one_way_ave

In prettyglm: Pretty Summaries of Generalized Linear Model Coefficients

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')

prettyglm documentation built on Sept. 8, 2023, 5:56 p.m.