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"),
  set_seed = 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

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.