| Type: | Package |
| Title: | Automatically Runs 36 Logistic Models (Individual and Ensembles) |
| Version: | 0.5.0 |
| Description: | Automatically returns 36 logistic models including 23 individual models and 13 ensembles of models of logistic data. The package also returns 10 plots, 5 tables, and a summary report. The package automatically builds all 36 models, reports all results, and provides graphics to show how the models performed. This can be used for a wide range of data sets. The package includes medical data (the Pima Indians data set), and information about the performance of Lebron James. The package can be used to analyze many other examples, such as stock market data. The package automatically returns many values for each model, such as True Positive Rate, True Negative Rate, False Positive Rate, False Negative Rate, Positive Predictive Value, Negative Predictive Value, F1 Score, Area Under the Curve. The package also returns 36 Receiver Operating Characteristic (ROC) curves for each of the 36 models. |
| License: | MIT + file LICENSE |
| Depends: | adabag, arm, brnn, C50, car, corrplot, Cubist, doParallel, dplyr, e1071, gam, gbm, ggplot2, ggplotify, graphics, gridExtra, gt, ipred, klaR, MachineShop, magrittr, MASS, mda, parallel, pls, pROC, purrr, R (≥ 2.10), randomForest, ranger, reactable, reactablefmtr, readr, rpart, scales, stats, tidyr, tree, utils, xgboost |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.3.2 |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| URL: | https://github.com/InfiniteCuriosity/LogisticEnsembles |
| BugReports: | https://github.com/InfiniteCuriosity/LogisticEnsembles/issues |
| NeedsCompilation: | no |
| Packaged: | 2025-03-30 23:39:17 UTC; russellconte |
| Author: | Russ Conte [aut, cre, cph] |
| Maintainer: | Russ Conte <russconte@mac.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-04-01 16:10:01 UTC |
Diabetes—A logistic data set, determining whether a woman tested positive for diabetes. 100 percent accurate results are possible using the logistic function in the Ensembles package.
Description
"This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset."
This data set is from www.kaggle.com. The original notes on the website state: Context "This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage." Content "The datasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on. Acknowledgements Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., & Johannes, R.S. (1988). Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In Proceedings of the Symposium on Computer Applications and Medical Care (pp. 261–265). IEEE Computer Society Press.
- Pregnancies
Number of time pregnant
- Glucose
Plasma glucose concentration a 2 hours in an oral glucose tolerance test
- BloodPressure
Diastolic blood pressure (mm Hg)
- SkinThickness
Triceps skin fold thickness (mm)
- Insulin
2-Hour serum insulin (mu U/ml)
- BMI
Body mass index (weight in kg/(height in m)^2)
- DiabetesPedigreeFunction
Diabetes pedigree function
- Age
Age (years)
- Outcome
Class variable (0 or 1) 268 of 768 are 1, the others are 0
Usage
Diabetes
Format
An object of class data.frame with 768 rows and 9 columns.
Source
<https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database/data>
Lebron—A logistic data set, with the result indicating whether or not Lebron scored on each shot in the data set.
Description
This dataset opens the door to the intricacies of the 2023 NBA season, offering a profound understanding of the art of scoring in professional basketball.
Usage
Lebron
Format
An object of class data.frame with 1533 rows and 12 columns.
Details
- top
The vertical position on the court where the shot was taken
- left
The horizontal position on the court where the shot was taken
- date
The date when the shot was taken. (e.g., Oct 18, 2022)
- qtr
The quarter in which the shot was attempted, typically represented as "1st Qtr," "2nd Qtr," etc.
- time_remaining
The time remaining in the quarter when the shot was attempted, typically displayed as minutes and seconds (e.g., 09:26).
- result
Indicates whether the shot was successful, with "TRUE" for a made shot and "FALSE" for a missed shot
- shot_type
Describes the type of shot attempted, such as a "2" for a two-point shot or "3" for a three-point shot
- distance_ft
The distance in feet from the hoop to where the shot was taken
- lead
Indicates whether the team was leading when the shot was attempted, with "TRUE" for a lead and "FALSE" for no lead
- lebron_team_score
The team's score (in points) when the shot was taken
- opponent_team_score
The opposing team's score (in points) when the shot was taken
- opponent
The abbreviation for the opposing team (e.g., GSW for Golden State Warriors)
- team
The abbreviation for LeBron James's team (e.g., LAL for Los Angeles Lakers)
- season
The season in which the shots were taken, indicated as the year (e.g., 2023)
- color
Represents the color code associated with the shot, which may indicate shot outcomes or other characteristics (e.g., "red" or "green")
@source <https://www.kaggle.com/datasets/dhavalrupapara/nba-2023-player-shot-dataset>
logistic—function to perform logistic analysis and return the results to the user.
Description
logistic—function to perform logistic analysis and return the results to the user.
Usage
Logistic(
data,
colnum,
numresamples,
remove_VIF_greater_than,
remove_ensemble_correlations_greater_than,
save_all_trained_models = c("Y", "N"),
save_all_plots = c("Y", "N"),
how_to_handle_strings = c("0", "1"),
do_you_have_new_data = c("Y", "N"),
use_parallel = c("Y", "N"),
train_amount,
test_amount,
validation_amount
)
Arguments
data |
data can be a CSV file or within an R package, such as MASS::Pima.te |
colnum |
the column number with the logistic data |
numresamples |
the number of resamples |
remove_VIF_greater_than |
Removes features with VIGF value above the given amount (default = 5.00) |
remove_ensemble_correlations_greater_than |
Enter a number to remove correlations in the ensembles |
save_all_trained_models |
"Y" or "N". Places all the trained models in the Environment |
save_all_plots |
Options to save all plots |
how_to_handle_strings |
0: No strings, 1: Factor values |
do_you_have_new_data |
"Y" or "N". If "Y", then you will be asked for the new data |
use_parallel |
"Y" or "N" for parallel processing |
train_amount |
set the amount for the training data |
test_amount |
set the amount for the testing data |
validation_amount |
Set the amount for the validation data |
Value
a real number
SAHeart data
Description
This is the South African heart disease data originally published in Elements of Statistical Learning, see https://rdrr.io/cran/ElemStatLearn/man/SAheart.html
Usage
SAHeart
Format
SAHeart
- sbp
Systolic blood pressure
- tobacco
cumulative tobacco (kg)
- ldl
low density lipoprotein cholesterol
- adiposity
a numeric vector
- famhist
family history of heart disease, a factor with levels Absent Present
- typea
type-A behavior
- obesity
a numeric vector
- alcohol
current alcohol consumption
- age
age at onset
- chd
response, coronary heart disease
Source
Rousseauw, J., du Plessis, J., Benade, A., Jordaan, P., Kotze, J. and Ferreira, J. (1983). Coronary risk factor screening in three rural communities, South African Medical Journal 64: 430–436.