| 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 |
rhs |
A function call using the |
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 |
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 |
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 |
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 |
Value
list with graphical features
Examples
plot_curve(obj4)
Summary Stats
Description
Return summary statistics
Usage
summary_stats(obj)
Arguments
obj |
object returned from |
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