Type: Package
Title: Plotting Trade-Off AUC-Dimensionality
Version: 0.1.0
Depends: SuperLearner, R (≥ 3.5)
Description: Perform and Runtime statistical comparisons between models. This package aims at choosing the best model for a particular dataset, regarding its discriminant power and runtime.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Suggests: spelling, testthat (≥ 3.0.0)
Config/testthat/edition: 3
RoxygenNote: 7.3.2
Imports: dplyr, speedglm, magrittr, purrr, rsample, stringr, tibble, tidyr, ROCR, caret, ez, fastDummies, fuzzySim, ggplot2
URL: https://github.com/luisgarcez11/tradeoffaucdim
BugReports: https://github.com/luisgarcez11/tradeoffaucdim/issues
Language: en-US
NeedsCompilation: no
Packaged: 2025-04-29 18:30:19 UTC; luis_
Author: Garcez Luis [aut, cre]
Maintainer: Garcez Luis <luisgarcez1@gmail.com>
Repository: CRAN
Date/Publication: 2025-05-02 09:40:02 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

lhs

A value or the magrittr placeholder.

rhs

A function call using the magrittr semantics.

Value

The result of calling 'rhs(lhs)'.

Apply Model

Description

Apply model and create column with fit

Usage

apply_model(
  obj,
  models = c("SL.glm", "SL.rpart"),
  test_partition_prop = 0.2,
  perf_measure = "auc"
)

Arguments

obj

object returned from define_indepvars_outcome

models

models to be analyzed

test_partition_prop

test proportion

perf_measure

performance measure

Value

list with fit models and parameters

Examples

apply_model(obj2)

Banana Quality

Description

Banana quality dataset

Usage

bananaquality

Format

An object of class data.frame with 8000 rows and 8 columns.

Banana Quality Subset

Description

Banana quality dataset subset

Usage

bananaquality_sample

Format

An object of class data.frame with 50 rows and 8 columns.

Bootstrap data

Description

Create a list with bootstrap samples

Usage

bootstrap_data(
  data,
  outcome = "Quality",
  indep_vars = c("Size", "Weight", "Sweetness", "Softness", "HarvestTime", "Ripeness",
    "Acidity"),
  n_samples = 50,
  n_maximum_dim = 5
)

Arguments

data

a dataframe to be analyzed

outcome

a string representing the outcome variable

indep_vars

a vector of strings to be considered

n_samples

number of bootstrap samples

n_maximum_dim

maximum number of variables to be considered

Value

list

Examples

bootstrap_data(bananaquality_sample)

Compare test

Description

Performs statistical tests to compare performance and runtime.

Usage

compare_test(obj, x_label_offset = 1, y_label_offset = 10)

Arguments

obj

object returned by plot_curve

x_label_offset

x coordinate to plot p-value

y_label_offset

y coordinate to plot p-value

Value

list with statistical tests performed

Examples

compare_test(obj5)

Define independent variables

Description

Define independent variables to be tested

Usage

define_indepvars(obj, p_in = 0.5, p_out = 0.6)

Arguments

obj

object returned by bootstrap_data

p_in

entry p-value used to determine variable order

p_out

removal p-value used to determine variable order

Value

list

Examples

define_indepvars(obj1)

Example Object returned from bootstrap_data

Description

obj1

Usage

obj1

Format

An object of class list of length 5.

Example Object returned from define_indepvars_outcome

Description

obj2

Usage

obj2

Format

An object of class list of length 7.

Example Object returned from apply_model

Description

obj3

Usage

obj3

Format

An object of class list of length 10.

Example Object returned from summary_statistics

Description

obj4

Usage

obj4

Format

An object of class list of length 11.

Example Object returned from plot_curve

Description

obj5

Usage

obj5

Format

An object of class list of length 15.

Example Object returned from compare_test

Description

obj6

Usage

obj6

Format

An object of class list of length 16.

Plot curve

Description

Return plot features.

Usage

plot_curve(obj)

Arguments

obj

object returned by summary_statistics

Value

list with graphical features

Examples

plot_curve(obj4)

Summary Stats

Description

Return summary statistics

Usage

summary_stats(obj)

Arguments

obj

object returned from apply_model

Value

list with summary statistics and bootstrap confidence intervals

Examples

summary_stats(obj3)

Wrap all pipeline

Description

Wrap all pipeline

Usage

wrapper_aucdim(
  data,
  outcome,
  indep_vars,
  n_samples = 100,
  n_maximum_dim = 5,
  p_in = 0.5,
  p_out = 0.6,
  models = c("SL.glm"),
  test_partition_prop = 0.2,
  perf_measure = "auc",
  x_label_offset = 1,
  y_label_offset = 10
)

Arguments

data

a dataframe to be analyzed

outcome

a string representing the outcome variable

indep_vars

a vector of strings to be considered

n_samples

number of bootstrap samples

n_maximum_dim

maximum number of variables

p_in

entry p-value for choosing variable order

p_out

exclusion p-value for choosing variable order

models

a string representing the models to compare

test_partition_prop

test partition proportion

perf_measure

performance measure to be considered

x_label_offset

x coordinate for plotting

y_label_offset

y coordinate for plotting

Value

a list with the final object