Package {inferstat}

One-way Analysis of Variance

Description

Performs a one-way Analysis of Variance (ANOVA) to compare the means of two or more independent groups.

Usage

anova(
  data,
  response,
  group,
  alpha = 0.05,
  conf.level = 0.95,
  effect_size = c("eta", "omega", "none"),
  posthoc = FALSE,
  plot = TRUE,
  theme = NULL,
  na_action = c("fail", "omit")
)

Arguments

Details

The function returns an object of class "inferstat" containing the ANOVA table, effect size, optional Tukey HSD post hoc comparisons, interpretation, and publication-ready plot.

Value

An object of class "inferstat".

Examples

fit <- anova(
  data = iris,
  response = Sepal.Length,
  group = Species
)

summary(fit)

Check Statistical Assumptions Performs a comprehensive assessment of statistical assumptions.

Description

Check Statistical Assumptions Performs a comprehensive assessment of statistical assumptions.

Usage

check_assumptions(
  data,
  response,
  group = NULL,
  normality = TRUE,
  variance = TRUE,
  outliers = TRUE,
  alpha = 0.05,
  plot = TRUE,
  na_action = c("fail", "omit")
)

Arguments

Value

An object of class "inferstat" containing:

• result — individual assumption test results.

• table — combined summary table.

• assumptions — overall assumption status.

• figure — diagnostic plots.

• interpretation — overall interpretation.

Examples

fit <- check_assumptions(
  data = iris,
  response = Sepal.Length,
  group = Species
)

fit
summary(fit)

Check Normality

Description

Performs the Shapiro-Wilk normality test for a numeric variable.

Usage

check_normality(
  data,
  response,
  method = c("shapiro"),
  alpha = 0.05,
  plot = TRUE,
  plot_type = c("qq", "histogram"),
  type = NULL,
  na_action = c("fail", "omit")
)

Arguments

Value

An object of class "inferstat" containing:

• result — Shapiro-Wilk test results.

• method — test name.

• figure — optional diagnostic plot.

• interpretation — interpretation of the test.

Examples

fit <- check_normality(
  data = iris,
  response = Sepal.Length
)

fit
summary(fit)
plot(fit)

Check Outliers

Description

Detects potential outliers in a numeric variable using one of several commonly used statistical methods.

Usage

check_outliers(
  data,
  response,
  method = c("iqr", "zscore", "modified_zscore", "mad"),
  threshold = NULL,
  plot = TRUE,
  na_action = c("fail", "omit")
)

Arguments

Details

Supported methods include:

• IQR Rule (default)

• Z-score

• Modified Z-score

• MAD

Value

An object of class "inferstat" containing:

• result — summary table.

• n_outliers — number of detected outliers.

• outlier_index — indices of detected outliers.

• outlier_values — detected outlier values.

• figure — optional boxplot.

• interpretation — interpretation of the results.

Check Homogeneity of Variance

Description

Performs a test for homogeneity of variance across groups.

Usage

check_variance(
  data,
  response,
  group,
  method = c("levene", "bartlett", "fligner"),
  alpha = 0.05,
  plot = TRUE,
  na_action = c("fail", "omit")
)

Arguments

Details

Supported methods:

• Levene's Test (default)

• Bartlett's Test

• Fligner-Killeen Test

Value

An object of class "inferstat" containing:

• result — homogeneity of variance test results.

• method — statistical method used.

• figure — optional boxplot.

• interpretation — interpretation of the test.

Examples

fit <- check_variance(
  data = iris,
  response = Sepal.Length,
  group = Species
)

fit
summary(fit)
plot(fit)

Descriptive Statistics

Description

Computes publication-ready descriptive statistics.

Usage

describe(
  data,
  response,
  group = NULL,
  conf.level = 0.95,
  plot = TRUE,
  plot_type = c("histogram", "density", "boxplot", "violin", "qqplot"),
  theme = NULL,
  na_action = c("fail", "omit")
)

Arguments

Value

An object of class "inferstat" containing:

• result: a data frame containing the computed descriptive statistics.

• method: a character string identifying the analysis method.

• figure: a ggplot object when plotting is requested, otherwise NULL.

• interpretation: a character string providing a summary interpretation of the descriptive analysis.

The result component summarizes the distribution, central tendency, variability, and confidence interval of the selected response variable, either overall or by the specified grouping variable.

Examples

fit <- describe(
  data = iris,
  response = Sepal.Length
)

fit
summary(fit)
plot(fit)

Student's t-test

Description

Performs an independent, Welch's or paired Student's t-test.

Usage

t_test(
  data,
  response = NULL,
  group = NULL,
  x = NULL,
  y = NULL,
  paired = FALSE,
  var.equal = TRUE,
  alternative = c("two.sided", "less", "greater"),
  conf.level = 0.95,
  effect_size = c("cohen", "hedges", "glass", "none"),
  plot = TRUE,
  theme = NULL,
  na_action = c("fail", "omit")
)

Arguments

Details

The function automatically returns publication-ready tables, effect sizes, confidence intervals and optional graphics.

Value

An object of class "inferstat".

Examples

iris2 <- subset(
  iris,
  Species != "virginica"
)

fit <- t_test(
  data = iris2,
  response = Sepal.Length,
  group = Species
)

print(fit)
summary(fit)
plot(fit)

Publication Theme for inferstat

Description

A clean, publication-quality ggplot2 theme designed for scientific manuscripts, journal articles, theses, and technical reports.

Usage

theme_publication(
  base_size = 14,
  base_family = "sans",
  base_line_size = 0.6,
  base_rect_size = 0.6
)

Arguments

Details

The theme is based on ggplot2::theme_bw() with modifications to improve readability and produce publication-ready graphics.

Features include:

• White background

• No panel grid

• Bold axis titles

• Black axis text

• Centered bold plot title

• Centered subtitle

• Bold legend title

• Right-positioned legend

• Styled facet strips

This is the default plotting theme used throughout the inferstat package.

Value

A ggplot2 theme.

See Also

ggplot2::theme_bw()

Examples

library(ggplot2)

ggplot(mtcars,
       aes(wt, mpg)) +
  geom_point() +
  theme_publication()