LogisticEnsembles: Automatically Runs 18 Logistic Models-14 Individual Logistic Models and 4 Ensembles of Models

Description

Automatically returns results from 18 logistic models including 14 individual logistic models and 4 logistic ensembles of models. The package also returns 25 plots, 5 tables, and a summary report. The package automatically builds all 18 models, reports all results, and provides graphics to show how the models performed. This can be used for a wide range of data, such as sports or medical data. 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 18 models.

Author(s)

Maintainer: Russ Conte russconte@mac.com [copyright holder]

See Also

Useful links:

• https://github.com/InfiniteCuriosity/LogisticEnsembles

• Report bugs at https://github.com/InfiniteCuriosity/LogisticEnsembles/issues

Cervical_cancer-This data set predicts a patient's risk of cervical cancer based on behavior reports

Description

"The dataset was collected at 'Hospital Universitario de Caracas' in Caracas, Venezuela. The dataset comprises demographic information, habits, and historic medical records of 858 patients. Several patients decided not to answer some of the questions because of privacy concerns (missing values)." I cleaned up the data so there are no missing data points, nor any NAs.

This data set has 858 observations of 34 variables. The 34th column, 'Biopsy' is the target column.

Age

Age

Number.of.sexual.partners

Number of reported sexual partners

First.sexual.intercourse

Age at first sexual intercourse

Num.of.pregnancies

Reported number of pregnancies

Smokes

Whether the subject smokes

Smokes..years.

The number of years the subject reported smoking

Smokes..packs.year.

The number of packs of cigarettes the subject reports smoking each year

Hormonal.Contraceptives

If the subject is using hormonal contraceptives

Hormonal.Contraceptives..years.

Number of years the subject reports using hormonal contraceptives

IUD

Does the subject use an IUD?

IUD..years.

Number of years the subject reports using an IUD

STDs

Does the patient have STDs?

STDs..number.

Number of STDs

STDs.condylomatosis

Does the patient have condylomatosis?

STDs.cervical.condylomatosis

Does the patient have cervical condylomatosis?

STDs.vaginal.condylomatosis

Does the patient have vaginal condylomatosis?

STDs.vulvo.perineal.condylomatosis

Does the patient have vulvo perineal condylomatosis?

STDs.syphilis

Does the patient have Syphilis?

STDs.pelvic.inflammatory.disease

Does the patient have pelvic inflammatory disease?

STDs.genital.herpes

Does the patient have genitial herpes?

STDs.molluscum.contagiosum

Does the patient have molluscum contagiosum?

AIDS

Does the patient have AIDS?

STDs.Hepatitis.B

Does the patient have hepatitis B?

STDs..Number.of.diagnosis

Number of diagnoses of STDs

Dx.Cancer

Does the patient have a diagnosis of cancer?

Dx.CIN

Does the patient have a diagnosis of CIN?

Dx.HPV

Does the patient have a diagnosis of HPV?

Dx

What is the patient's diagnosis?

Hinselmann

Hinselmann

Schiller

Schiller

Citology

Citology

Biopsy

The target column, 1 = yes, 0 = no

Usage

Cervical_cancer

Format

An object of class data.frame with 858 rows and 34 columns.

Source

https://archive.ics.uci.edu/dataset/383/cervical+cancer+risk+factors

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>

German_Credit_Risk-This dataset classifies people described by a set of attributes as good or bad credit risks. #'

Description

This data set originally came from Professor Hofmann, and is available in several locations, including the UCI Machine Learning Repository I cleaned the data set up, which included naming each of the columns, and removing white spaces from the names of the columns.

The data set has 999 observations of 21 columns of data.The 21st column, "Class" is the target column in the data. Acknowledgements https://dutangc.github.io/CASdatasets/reference/credit.html

Attribute1

Status of existing checking account

Attribute2

Duration (in months)

Attribute3

Credit history

Attribute4

Purpose

Attribute5

Credit amount

Attribute6

Savings accounts/bonds

Attribute7

Present employment since

Attribute8

Installment rate in percentage of disposable income

Attribute9

Personal status and sex

Attribute10

Other debtors / guarantors

Attribute11

Present residence since

Attribute12

Property

Attribute13

Age (in years)

Attribute14

Other installment plans

Attribute15

Housing

Attribute16

Number of existing credits at this bank

Attribute17

Job

Attribute18

Number of people being liable to provide maintenance for

Attribute19

Telephone

Attribute20

Foreign worker

Class

1 = Good, 0 = Bad

Usage

German_Credit_Risk

Format

An object of class data.frame with 999 rows and 21 columns.

Source

https://archive.ics.uci.edu/dataset/144/statlog+german+credit+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_data_correlations_greater_than,
  remove_ensemble_correlations_greater_than,
  save_all_trained_models = c("Y", "N"),
  save_all_plots = c("Y", "N"),
  set_seed = c("Y", "N"),
  how_to_handle_strings = c(0("none"), 1("factor levels"), 2("One-hot encoding"),
    3("One-hot encoding with jitter")),
  do_you_have_new_data = c("Y", "N"),
  stratified_column_number,
  use_parallel = c("Y", "N"),
  train_amount,
  test_amount,
  validation_amount
)

Arguments

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.