Version:	1.6.0
Title:	Stepwise Regression Analysis
Date:	2025-09-30
Description:	Stepwise regression is a statistical technique used for model selection. This package streamlines stepwise regression analysis by supporting multiple regression types(linear, Cox, logistic, Poisson, Gamma, and negative binomial), incorporating popular selection strategies(forward, backward, bidirectional, and subset), and offering essential metrics. It enables users to apply multiple selection strategies and metrics in a single function call, visualize variable selection processes, and export results in various formats. StepReg offers a data-splitting option to address potential issues with invalid statistical inference and a randomized forward selection option to avoid overfitting. We validated StepReg's accuracy using public datasets within the SAS software environment. Additionally, StepReg features an interactive Shiny application to enhance usability and accessibility.
License:	MIT + file LICENSE
BugReports:	https://github.com/JunhuiLi1017/StepReg/issues
VignetteBuilder:	knitr
Suggests:	knitr, testthat, BiocStyle, kableExtra
Imports:	dplyr, ggplot2, ggrepel, MASS, stringr, survival, flextable, cowplot, shiny, ggcorrplot, tidyr, summarytools, shinythemes, rmarkdown, DT, shinycssloaders, shinyjs, pROC, survAUC
Encoding:	UTF-8
RoxygenNote:	7.3.2
NeedsCompilation:	no
Repository:	CRAN
Packaged:	2025-09-30 15:20:08 UTC; lij11
Author:	Junhui Li [cre], Junhui Li [aut], Kai Hu [aut], Xiaohuan Lu [aut], Sushmita N Nayak [ctb], Cesar Bautista Sotelo [ctb], Michael A Lodato [ctb], Wenxin Liu [aut], Lihua Julie Zhu [aut]
Maintainer:	Junhui Li <junhui.li11@umassmed.edu>
Date/Publication:	2025-09-30 16:10:02 UTC

StepReg: Stepwise Regression Analysis

Description

Stepwise regression is a statistical technique used for model selection. This package streamlines stepwise regression analysis by supporting multiple regression types(linear, Cox, logistic, Poisson, Gamma, and negative binomial), incorporating popular selection strategies(forward, backward, bidirectional, and subset), and offering essential metrics. It enables users to apply multiple selection strategies and metrics in a single function call, visualize variable selection processes, and export results in various formats. StepReg offers a data-splitting option to address potential issues with invalid statistical inference and a randomized forward selection option to avoid overfitting. We validated StepReg's accuracy using public datasets within the SAS software environment. Additionally, StepReg features an interactive Shiny application to enhance usability and accessibility.

Author(s)

Maintainer: Junhui Li junhui.li11@umassmed.edu (ORCID)

Authors:

• Junhui Li junhui.li11@umassmed.edu

• Kai Hu kai.hu@umassmed.edu

• Xiaohuan Lu

• Wenxin Liu

• Lihua Julie Zhu

Other contributors:

• Sushmita N Nayak [contributor]

• Cesar Bautista Sotelo [contributor]

• Michael A Lodato [contributor]

See Also

Useful links:

• Report bugs at https://github.com/JunhuiLi1017/StepReg/issues

Launch StepReg Shiny Application

Description

Launches an interactive Shiny application for performing stepwise regression analysis. The application provides a user-friendly interface for data analysis, model selection, and visualization of results.

Usage

StepRegShinyApp()

Details

The application consists of two main steps:

Step 1: Data Preparation

• Upload custom datasets or select from built-in examples

• Configure data import settings (headers, separators, quotes)

• View and modify variable types (numeric, factor, integer, character)

• Generate exploratory data visualizations

Step 2: Model Analysis

• Select regression type:

  • Linear regression

  • Logistic regression

  • Cox proportional hazards

  • Poisson regression

  • Gamma regression

  • Negative binomial regression

• Choose dependent and independent variables

• Specify stepwise selection strategy:

  • Forward selection

  • Backward elimination

  • Bidirectional elimination

  • Best subset selection

• Set model selection criteria (AIC, BIC, etc.)

• Configure significance levels for variable entry/removal

• Generate comprehensive reports and visualizations

Value

Launches the Shiny application in the user's default web browser.

See Also

stepwise for the core stepwise regression function

report for generating analysis reports

plot.StepReg for visualization functions

Examples

## Not run: 
# Launch the StepReg Shiny application
StepRegShinyApp()

## End(Not run)

Fair's Extramarital Affairs Data

Description

Infidelity data, known as Fair's Affairs. Cross-section data from a survey conducted by Psychology Today in 1969. This dataset is sourced from the AER package.

Usage

data(affairs)

Format

A data frame containing 601 observations on 9 variables.

affairs

numeric. How often engaged in extramarital sexual intercourse during the past year? 0 = none, 1 = once, 2 = twice, 3 = 3 times, 7 = 4–10 times, 12 = monthly, 12 = weekly, 12 = daily.

gender

factor indicating gender.

age

numeric variable coding age in years: 17.5 = under 20, 22 = 20–24, 27 = 25–29, 32 = 30–34, 37 = 35–39, 42 = 40–44, 47 = 45–49, 52 = 50–54, 57 = 55 or over.

yearsmarried

numeric variable coding number of years married: 0.125 = 3 months or less, 0.417 = 4–6 months, 0.75 = 6 months–1 year, 1.5 = 1–2 years, 4 = 3–5 years, 7 = 6–8 years, 10 = 9–11 years, 15 = 12 or more years.

children

factor. Are there children in the marriage?

religiousness

numeric variable coding religiousness: 1 = anti, 2 = not at all, 3 = slightly, 4 = somewhat, 5 = very.

education

numeric variable coding level of education: 9 = grade school, 12 = high school graduate, 14 = some college, 16 = college graduate, 17 = some graduate work, 18 = master's degree, 20 = Ph.D., M.D., or other advanced degree.

occupation

numeric variable coding occupation according to Hollingshead classification (reverse numbering).

rating

numeric variable coding self rating of marriage: 1 = very unhappy, 2 = somewhat unhappy, 3 = average, 4 = happier than average, 5 = very happy.

Source

Fair, R.C. (1978). A Theory of Extramarital Affairs. Journal of Political Economy, 86, 45–61.

References

• Greene, W.H. (2003). Econometric Analysis, 5th edition. Upper Saddle River, NJ: Prentice Hall.

• Fair, R.C. (1978). A Theory of Extramarital Affairs. Journal of Political Economy, 86, 45–61.

See Also

Affairs for the original dataset in the AER package

Examples

data(affairs)
summary(affairs)

Credit Card Application Dataset

Description

A dataset containing credit history information for credit card applicants. This dataset is sourced from the AER package.

Usage

data(creditCard)

Format

A data frame with 1,319 observations and 12 variables:

card

Factor. Whether the credit card application was accepted (Yes/No)

reports

Numeric. Number of major derogatory reports on the applicant's credit history

age

Numeric. Age in years plus twelfths of a year (e.g., 30.5 represents 30 years and 6 months)

income

Numeric. Annual income in USD (divided by 10,000)

share

Numeric. Ratio of monthly credit card expenditure to yearly income

expenditure

Numeric. Average monthly credit card expenditure in USD

owner

Factor. Home ownership status (Yes/No)

selfemp

Factor. Self-employment status (Yes/No)

dependents

Numeric. Number of dependents

months

Numeric. Number of months living at current address

majorcards

Numeric. Number of major credit cards held

active

Numeric. Number of active credit accounts

Details

This dataset is commonly used for credit risk analysis and modeling credit card approval decisions. It provides a comprehensive view of various factors that may influence credit card application outcomes.

Source

Greene, W.H. (2003). Econometric Analysis, 5th edition. Upper Saddle River, NJ: Prentice Hall.

See Also

CreditCard for the original dataset in the AER package

Examples

data(creditCard)
summary(creditCard)

NCCTG Lung Cancer Data

Description

Survival in patients with advanced lung cancer from the North Central Cancer Treatment Group. Performance scores rate how well the patient can perform usual daily activities.

Usage

data(lung)

Format

A data frame containing 228 observations on 10 variables.

inst

Institution code

time

Survival time in days

status

censoring status 1=censored, 2=dead

age

Age in years

sex

Male=1 Female=2

ph.ecog

ECOG performance score as rated by the physician. 0=asymptomatic, 1= symptomatic but completely ambulatory, 2= in bed < 50% of the day, 3= in bed > 50% of the day but not bedbound, 4 = bedbound

ph.karno

Karnofsky performance score (bad=0-good=100) rated by physician

pat.karno

Karnofsky performance score as rated by patient

meal.cal

Calories consumed at meals

wt.loss

Weight loss in last six months (pounds)

Note

This dataset is sourced from the survival package.

Source

Terry Therneau. (2021). survival: Survival Analysis. R package version 3.4-2. https://CRAN.R-project.org/package=survival

References

• Loprinzi CL. Laurie JA. Wieand HS. Krook JE. Novotny PJ. Kugler JW. Bartel J. Law M. Bateman M. Klatt NE. et al. Prospective evaluation of prognostic variables from patient-completed questionnaires. North Central Cancer Treatment Group. Journal of Clinical Oncology. 12(3):601-7, 1994.

See Also

lung for the original dataset in the survival package

Examples

data(lung)
summary(lung)

Model Performance Summary Across Different Selection Strategies

Description

Creates a summary table showing the performance of the selected models by different combinations of stepwise regression strategies and selection metrics.

Usage

performance(x, ...)

Arguments

Value

A data frame where:

• For linear, poisson, gamma, and negative binomial regression:

  • adj_r2_train/adj_r2_test: Adjusted R-squared measures the proportion of variance explained by the model, adjusted for the number of predictors. Values range from 0 to 1, with higher values indicating better model fit. A good model should have high adjusted R-squared on both training and test data, with minimal difference between them. Large differences suggest overfitting.

  • mse_train/mse_test: Mean Squared Error measures the average squared difference between predicted and actual values. Lower values indicate better model performance. The test MSE should be close to training MSE; significantly higher test MSE suggests overfitting.

  • mae_train/mae_test: Mean Absolute Error measures the average absolute difference between predicted and actual values. Lower values indicate better model performance. Like MSE, test MAE should be close to training MAE to avoid overfitting.

• For logistic regression:

  • accuracy_train/accuracy_test: Accuracy measures the proportion of correct predictions (true positives + true negatives) / total predictions. Values range from 0 to 1, with higher values indicating better classification performance. Test accuracy should be close to training accuracy; large differences suggest overfitting.

  • auc_train/auc_test: Area Under the Curve measures the model's ability to distinguish between classes. Values range from 0.5 (random) to 1.0 (perfect discrimination). AUC > 0.7 is considered acceptable, > 0.8 is good, > 0.9 is excellent. Test AUC should be close to training AUC to avoid overfitting.

  • log_loss_train/log_loss_test: Log Loss (logarithmic loss) penalizes confident wrong predictions more heavily. Lower values indicate better model performance. Values close to 0 are ideal. Test log loss should be close to training log loss; higher test log loss suggests overfitting.

• For Cox regression:

  • c-index_train/c-index_test: Concordance Index (C-index) measures the model's ability to correctly rank survival times. Values range from 0.5 (random) to 1.0 (perfect ranking). C-index > 0.7 is considered acceptable, > 0.8 is good, > 0.9 is excellent. Test C-index should be close to training C-index to avoid overfitting.

  • auc_hc: Harrell's C-index for time-dependent AUC, measuring discrimination at specific time points. Higher values indicate better discrimination ability.

  • auc_uno: Uno's C-index for time-dependent AUC, providing an alternative measure of discrimination that may be more robust to censoring patterns.

  • auc_sh: Schemper and Henderson's C-index for time-dependent AUC, offering another perspective on model discrimination performance.

Each cell contains the performance of the model by the corresponding strategy-metric combination. For the subset strategy with Information Criteria (IC), only the single best model across all variable numbers is shown. This does not apply to Significance Level (SL) since F/Rao statistics can only be compared between models with the same number of variables.

Examples

# Load example data
data(mtcars)

# Run stepwise regression with multiple strategies and metrics
formula <- mpg ~ .
result <- stepwise(
  formula = formula,
  data = mtcars,
  type = "linear",
  strategy = c("forward", "backward", "bidirection"),
  metric = c("AIC", "BIC")
)

# Get performance summary
performance(result)

Visualize Stepwise Regression Results

Description

Creates informative visualizations of the variable selection process from a StepReg object. This function generates two types of plots: detailed step-by-step selection process and an overview of the final selected variables.

Usage

## S3 method for class 'StepReg'
plot(
  x,
  strategy = attr(x, "nonhidden"),
  process = c("overview", "detail"),
  num_digits = 6,
  ...
)

Arguments

Details

The function creates different types of visualizations based on the selection strategy:

• For forward/backward/bidirectional selection:

  • detail view shows a heatmap with green tiles for added variables, tan tiles for removed variables, and gray tiles for non-selected variables

  • Overview shows metric values across steps with variable labels

• For subset selection:

  • detail view shows a heatmap of selected variables at each step

  • Overview shows metric values for different subset sizes

Value

A ggplot object showing either:

• For "detail" process: A heatmap showing variable selection status at each step

• For "overview" process: A line plot showing metric values across steps

See Also

stepwise for creating StepReg objects

Examples

## Not run: 
# Load example data
data(mtcars)

# Run stepwise regression with multiple strategies
formula <- mpg ~ .
result <- stepwise(
  formula = formula,
  data = mtcars,
  type = "linear",
  strategy = c("forward", "bidirection", "subset"),
  metric = c("AIC", "BIC", "SL")
)

# Generate default overview plot
plot(result)

# Generate detailed plot for forward selection
plot(result, strategy = "forward", process = "detail")

# Generate overview plot with 3 decimal places
plot(result, strategy = "bidirection", process = "overview", num_digits = 3)

## End(Not run)

Print Stepwise Regression Results

Description

Displays the final model fit statistics from a StepReg object. This function provides a concise summary of the selected model's performance metrics.

Usage

## S3 method for class 'StepReg'
print(x, ...)

Arguments

Details

The print method provides a focused view of the final model's performance, showing the selected variables and their corresponding fit statistics. This is useful for quickly assessing the model's quality without the detailed step-by-step selection process (which can be viewed using stepwise).

Value

Invisibly returns the printed object. The function displays:

• Final model fit statistics for each strategy and metric combination

See Also

stepwise for creating StepReg objects

plot.StepReg for visualization of results

Examples

## Not run: 
# Load example data
data(mtcars)

# Run stepwise regression
formula <- mpg ~ .
result <- stepwise(
  formula = formula,
  data = mtcars,
  type = "linear",
  strategy = "forward",
  metric = "AIC"
)

# Print final model statistics
result

## End(Not run)

Leukemia Remission Dataset

Description

A dataset containing information about leukemia remission and associated risk factors. This dataset is commonly used for demonstrating logistic regression analysis in medical research.

Usage

data(remission)

Format

A data frame with 27 observations and 7 variables:

remiss

Binary outcome variable indicating leukemia remission status:

• 1 = Remission occurred

• 0 = No remission

cell

Numeric. Cellularity of the marrow clot section (percentage)

smear

Numeric. Smear differential percentage of blasts

infil

Numeric. Percentage of absolute marrow leukemia cell infiltrate

li

Numeric. Percentage labeling index of the bone marrow leukemia cells

blast

Numeric. Absolute number of blasts in the peripheral blood

temp

Numeric. Highest temperature (in Fahrenheit) before treatment

Details

This dataset is particularly useful for:

• Demonstrating logistic regression analysis

• Studying risk factors for leukemia remission

• Teaching medical statistics and predictive modeling

Source

Lee, E. T. (1974). "A Computer Program for Linear Logistic Regression Analysis." Computer Programs in Biomedicine 4:80–92.

References

• Lee, E. T. (1974). "A Computer Program for Linear Logistic Regression Analysis." Computer Programs in Biomedicine 4:80–92.

• Penn State University Statistics Online. "Logistic Regression Example." https://online.stat.psu.edu/stat501/book/export/html/1011

Examples

## Not run: 
# Load the dataset
data(remission)

# View first few rows
head(remission)

# Summary statistics
summary(remission)

# Run logistic regression
model <- glm(remiss ~ ., data = remission, family = binomial)
summary(model)

## End(Not run)

Generate Stepwise Regression Report

Description

Creates formatted reports from StepReg objects in various document formats. This function generates comprehensive reports containing all tables and results from the stepwise regression analysis.

Usage

report(x, report_name, format = c("html", "docx", "rtf", "pptx"))

Arguments

Details

The generated report includes:

• Summary of model parameters and selection criteria

• Variable types and classifications

• Step-by-step selection process

• Final selected model and fit statistics

• Model coefficients and significance levels

Value

Creates report file(s) in the specified format(s) in the current working directory. The file name will be report_name.format (e.g., "myreport.html", "myreport.docx").

See Also

stepwise for creating StepReg objects

plot.StepReg for visualization of results

Examples

## Not run: 
# Load leukemia remission data
data(remission)

# Run stepwise logistic regression
formula <- remiss ~ .
result <- stepwise(
  formula = formula,
  data = remission,
  type = "logit",
  strategy = c("forward", "bidirection"),
  metric = c("AIC", "BIC")
)

# Generate reports in multiple formats
report(
  x = result,
  report_name = "leukemia_analysis",
  format = c("html", "docx")
)

## End(Not run)

Stepwise Regression Model Selection

Description

Performs stepwise regression model selection using various strategies and selection criteria. Supports multiple regression types including linear, logistic, Cox, Poisson, and Gamma regression.

Usage

stepwise(
  formula,
  data,
  type = c("linear", "logit", "cox", "poisson", "gamma", "negbin"),
  strategy = c("forward", "backward", "bidirection", "subset"),
  metric = c("AIC", "AICc", "BIC", "CP", "HQ", "adjRsq", "SL", "SBC", "IC(3/2)", "IC(1)"),
  sle = 0.15,
  sls = 0.15,
  include = NULL,
  test_method_linear = c("Pillai", "Wilks", "Hotelling-Lawley", "Roy"),
  test_method_glm = c("Rao", "LRT"),
  test_method_cox = c("efron", "breslow", "exact"),
  tolerance = 1e-07,
  weight = NULL,
  best_n = 3,
  test_ratio = 0,
  feature_ratio = 1,
  seed = 123,
  num_digits = 6
)

Arguments

Value

A StepReg class object, which is a structured list containing both the input specifications and the outcomes of the stepwise regression analysis. The key components of this object are detailed below, providing a comprehensive framework for model exploration and validation.

• argument A data.frame containing the user-specified settings and parameters used in the analysis, including the initial formula, regression type, selection strategy, chosen metrics, significance levels (sle/sls), tolerance threshold, test method, and other control parameters.

• variable A data.frame containing information about all variables in the model, including variable names, data types (numeric, factor, etc.), and their roles (Dependent/Independent) in the model.

• performance A data.frame providing detailed performance metrics for the selected models across different strategies and metrics. For both training and test datasets (when test_ratio < 1), the output includes model-specific performance indicators:

  • For linear, poisson, gamma, and negative binomial regression:

    • adj_r2_train/adj_r2_test: Adjusted R-squared measures the proportion of variance explained by the model, adjusted for the number of predictors. Values range from 0 to 1, with higher values indicating better model fit. A good model should have high adjusted R-squared on both training and test data, with minimal difference between them. Large differences suggest overfitting.

    • mse_train/mse_test: Mean Squared Error measures the average squared difference between predicted and actual values. Lower values indicate better model performance. The test MSE should be close to training MSE; significantly higher test MSE suggests overfitting.

    • mae_train/mae_test: Mean Absolute Error measures the average absolute difference between predicted and actual values. Lower values indicate better model performance. Like MSE, test MAE should be close to training MAE to avoid overfitting.

  • For logistic regression:

    • accuracy_train/accuracy_test: Accuracy measures the proportion of correct predictions (true positives + true negatives) / total predictions. Values range from 0 to 1, with higher values indicating better classification performance. Test accuracy should be close to training accuracy; large differences suggest overfitting.

    • auc_train/auc_test: Area Under the Curve measures the model's ability to distinguish between classes. Values range from 0.5 (random) to 1.0 (perfect discrimination). AUC > 0.7 is considered acceptable, > 0.8 is good, > 0.9 is excellent. Test AUC should be close to training AUC to avoid overfitting.

    • log_loss_train/log_loss_test: Log Loss (logarithmic loss) penalizes confident wrong predictions more heavily. Lower values indicate better model performance. Values close to 0 are ideal. Test log loss should be close to training log loss; higher test log loss suggests overfitting.

  • For Cox regression:

    • c-index_train/c-index_test: Concordance Index (C-index) measures the model's ability to correctly rank survival times. Values range from 0.5 (random) to 1.0 (perfect ranking). C-index > 0.7 is considered acceptable, > 0.8 is good, > 0.9 is excellent. Test C-index should be close to training C-index to avoid overfitting.

    • auc_hc: Harrell's C-index for time-dependent AUC, measuring discrimination at specific time points. Higher values indicate better discrimination ability.

    • auc_uno: Uno's C-index for time-dependent AUC, providing an alternative measure of discrimination that may be more robust to censoring patterns.

    • auc_sh: Schemper and Henderson's C-index for time-dependent AUC, offering another perspective on model discrimination performance.

• overview A nested list organized by strategy and metric, containing step-by-step summaries of the model-building process. Each element shows which variables were entered or removed at each step along with the corresponding metric values (e.g., AIC, BIC, SBC).

• detail A nested list organized by strategy and metric, providing granular information about each candidate step. This includes which variables were tested, their evaluation statistics, p-values, and whether they were ultimately selected or rejected.

• fitted model object within the strategy-specific list A nested list object organized with a first layer representing the selection strategy (e.g., forward, backward, bidirection, subset) and a second layer representing the metric (e.g., AIC, BIC, SBC). For each strategy-metric combination, the function returns fitted model objects that can be further analyzed using S3 generic functions such as summary(), anova(), or coefficients(). These functions adapt to the model type (e.g., coxph, lm, glm) through call-specific methods. Specific statistics can be directly retrieved using the $ operator, such as result$forward$AIC$coefficients. The level of detail in these analyses depends on the model type: the survival package enriches coxph objects with detailed statistics including hazard ratios, standard errors, z-statistics, p-values, and likelihood ratio tests, while base R functions like lm and glm offer basic output with coefficients by default, requiring summary() or anova() to reveal standard errors, t-values, p-values, and R-squared values.

Author(s)

Junhui Li, Kai Hu, Xiaohuan Lu

References

• Alsubaihi et al. (2002) Variable strategy in multivariable regression using sas/iml

• Darlington (1968) Multiple regression in psychological research and practice

• Dharmawansa et al. (2014) Roy's largest root under rank-one alternatives

• Hannan & Quinn (1979) The determination of the order of an autoregression

• Hotelling (1992) The Generalization of Student's Ratio

• Hocking (1976) The analysis and strategy of variables in linear regression

• Hurvich & Tsai (1989) Regression and time series model strategy in small samples

• Judge (1985) The Theory and practice of econometrics

• Mallows (1973) Some comments on cp

• Mardia et al. (1979) Multivariate analysis

• Mckeon (1974) F approximations to the distribution of hotelling's t20

• Mcquarrie & Tsai (1998) Regression and Time Series Model strategy

• Pillai (1955) Some new test criteria in multivariate analysis

• Sparks et al. (1985) On variable strategy in multivariate regression

• Sawa (1978) Information criteria for discriminating among alternative regression models

• Schwarz (1978) Estimating the dimension of a model

Examples

# Multivariate linear regression with bidirectional selection
data(mtcars)
formula <- cbind(mpg, drat) ~ . + 0
result1 <- stepwise(
  formula = formula,
  data = mtcars,
  type = "linear",
  strategy = "bidirection",
  metric = "AIC"
)

summary(result1$bidirection$AIC)
anova(result1$bidirection$AIC)
coefficients(result1$bidirection$AIC)

# Linear regression with multiple strategies and metrics
formula <- mpg ~ . + 1
result2 <- stepwise(
  formula = formula,
  data = mtcars,
  type = "linear",
  strategy = c("forward", "bidirection"),
  metric = c("AIC", "SBC", "SL", "AICc", "BIC", "HQ")
)

summary(result2$forward$AIC)
anova(result2$forward$AIC)
coefficients(result2$forward$AIC)

# Logistic regression with significance level criteria
data(remission)
formula <- remiss ~ .
result3 <- stepwise(
  formula = formula,
  data = remission,
  type = "logit",
  strategy = "forward",
  metric = "SL",
  sle = 0.05,
  sls = 0.05
)

summary(result3$forward$SL)
anova(result3$forward$SL)
coefficients(result3$forward$SL)

# Linear regression with continuous-nested-within-class effects
mtcars$am <- factor(mtcars$am)
formula <- mpg ~ am + cyl + wt:am + disp:am + hp:am
result4 <- stepwise(
  formula = formula,
  data = mtcars,
  type = "linear",
  strategy = "bidirection",
  metric = "AIC"
)

summary(result4$bidirection$AIC)
anova(result4$bidirection$AIC)
coefficients(result4$bidirection$AIC)

Tobacco Leaf Chemical Composition Dataset

Description

A dataset containing chemical composition measurements from 25 tobacco leaf samples. This dataset is commonly used for demonstrating multivariate regression analysis and exploring relationships between chemical components and burn rate.

Usage

data(tobacco)

Format

A data frame with 25 rows and 9 variables:

cigarette

Numeric. Rate of cigarette burn in inches per 1000 seconds.

sugar

Numeric. Percentage of sugar content in the leaf.

nicotine

Numeric. Percentage of nicotine content.

nitrogen

Numeric. Percentage of nitrogen content.

chlorine

Numeric. Percentage of chlorine content.

potassium

Numeric. Percentage of potassium content.

phosphorus

Numeric. Percentage of phosphorus content.

calcium

Numeric. Percentage of calcium content.

magnesium

Numeric. Percentage of magnesium content.

Details

This dataset is particularly useful for:

• Demonstrating multivariate regression analysis

• Studying relationships between chemical composition and burn rate

• Analyzing correlations between different chemical components

• Teaching statistical methods in agricultural research

Source

Anderson, R. L. and Bancroft, T. A. (1952), Statistical Theory in Research, McGraw-Hill Book Company, Inc., New York, NY.

Examples

# Load the dataset
data(tobacco)

# View the first few rows
head(tobacco)

# Summary statistics
summary(tobacco)

# Correlation analysis
cor(tobacco)