Package {aanova}

Analysis of Covariance (ANCOVA) with Adjusted Means Visualization

Description

This function performs an Analysis of Covariance to test group differences in a continuous response variable while controlling for a continuous covariate. It extracts the ANCOVA table, estimated marginal means (EMMs), and generates a publication-ready regression scatter plot with parallel trend lines.

Usage

ancova_analysis(
  data,
  response_var,
  factor_var,
  covariate_var,
  factor_levels = NULL,
  color_palette = "Set1"
)

Arguments

Value

A list containing the ANCOVA model summary, EMMs, and the ggplot object.

Flexible Correlation Matrix and Heatmap Generator

Description

Computes correlation matrices (Pearson, Spearman, or Kendall) and generates publication-ready heatmaps with customizable shapes (circles, squares), layout views (full, lower, upper), and optional text labels.

Usage

correlation_heatmap(
  data,
  numeric_vars = NULL,
  method = "pearson",
  shape = "circle",
  view = "lower",
  show_text = NULL,
  color_palette = "RdBu"
)

Arguments

Value

A list containing the correlation matrix, p-value matrix, and the ggplot object.

Generalized Linear Models (GLMs) for Fisheries Count and Binary Data

Description

This function fits Generalized Linear Models (e.g., Poisson, Quasipoisson, Binomial) to non-normal ecological data, extracts model summaries, incident rate ratios (IRR) or odds ratios, and generates a publication-ready forest plot visualizing effect sizes.

Usage

glm_analysis(
  data,
  response_var,
  predictor_vars,
  family_type = "poisson",
  color_palette = "Set1"
)

Arguments

Value

A list containing the model summary, coefficient table, and the ggplot object.

Length-Weight ANCOVA Dataset for Hilsa Shad

Description

A simulated dataset containing body weight, total length, and habitat types of Hilsa shad (Tenualosa ilisha) to demonstrate Analysis of Covariance (ANCOVA).

Usage

hilsa_ancova

Format

A data frame with 90 rows and 3 variables:

Habitat

A factor representing the environment (Marine, Estuary, River).

Total_Length_cm

A numeric vector representing total fish length in centimeters (covariate).

Weight_g

A numeric vector representing fish body weight in grams (response).

Catch Count Dataset for Hilsa Shad GLMs

Description

A simulated dataset containing catch counts, fishing effort, and environmental variables for Hilsa shad (Tenualosa ilisha) to demonstrate Generalized Linear Models (GLMs).

Usage

hilsa_catch

Format

A data frame with 120 rows and 4 variables:

Habitat

A factor representing the environment (Marine, Estuary, River).

Season

A factor representing the season (Monsoon, Dry).

Fishing_Hours

A numeric vector representing fishing effort in hours.

Catch_Count

An integer vector representing the number of fish caught (count response).

Environmental and Catch Parameters for Correlation Analysis

Description

A simulated dataset containing oceanographic measurements and catch weights to demonstrate customizable correlation matrix heatmaps.

Usage

hilsa_env

Format

A data frame with 100 rows and 6 variables:

SST_C

Sea Surface Temperature in degrees Celsius.

Salinity_ppt

Water salinity in parts per thousand.

Depth_m

Water depth in meters.

DO_mgL

Dissolved oxygen in milligrams per liter.

Chlorophyll_a

Chlorophyll-a concentration.

Catch_kg

Total fish catch in kilograms.

Morphometric Measurements of Hilsa Shad

Description

A simulated dataset containing multiple continuous morphological traits of Hilsa shad (Tenualosa ilisha) across different aquatic habitats to demonstrate Multivariate Analysis of Variance (MANOVA).

Usage

hilsa_morphology

Format

A data frame with 90 rows and 4 variables:

Habitat

A factor representing the environment (Marine, Estuary, River).

Body_Depth_cm

A numeric vector representing maximum body depth in centimeters.

Head_Length_cm

A numeric vector representing head length in centimeters.

Fin_Length_cm

A numeric vector representing pectoral fin length in centimeters.

Morphometric Regression Dataset for Hilsa Shad

Description

A simulated dataset containing length and weight measurements of Hilsa shad (Tenualosa ilisha) across various habitats for evaluating linear, logarithmic, and polynomial regression models.

Usage

hilsa_regression

Format

A data frame with 100 rows and 3 variables:

Total_Length_cm

A numeric vector representing total fish length in centimeters.

Weight_g

A numeric vector representing fish body weight in grams.

Habitat

A factor representing the environment (River, Estuary, Marine).

Comprehensive Body Weights of Hilsa Shad (Three-Way)

Description

A simulated dataset containing body weights of Hilsa shad (Tenualosa ilisha) across multiple habitats, fishing seasons, and size classes to demonstrate three-way ANOVA interaction models and faceted visualization.

Usage

hilsa_three_way

Format

A data frame with 120 rows and 4 variables:

Habitat

A factor representing the environment (Marine, Estuary, River).

Season

A factor representing the season (Monsoon, Dry).

Size_Class

A factor representing growth stage (Juvenile, Adult).

Weight_g

A numeric vector representing fish body weight in grams.

Multi-Factor Body Weights of Hilsa Shad

Description

A simulated dataset containing the body weights of Hilsa shad (Tenualosa ilisha) across different habitats and fishing seasons to demonstrate two-way ANOVA interaction effects.

Usage

hilsa_two_way

Format

A data frame with 90 rows and 3 variables:

Habitat

A factor representing the environment (Marine, Estuary, River).

Season

A factor representing the season (Monsoon, Dry).

Weight_g

A numeric vector representing fish body weight in grams.

Body Weights of Hilsa Shad Across Habitats

Description

A simulated dataset containing the body weights of Hilsa shad (Tenualosa ilisha) sampled from three distinct ecological environments: Marine, Estuary, and River. This dataset is designed to demonstrate one-way ANOVA and variance visualization.

Usage

hilsa_weight

Format

A data frame with 90 rows and 2 variables:

Habitat

A factor representing the sampling environment (Marine, Estuary, River).

Weight_g

A numeric vector representing the body weight of the fish in grams.

Multivariate Analysis of Variance (MANOVA) with Alternative Visualizations

Description

This function performs a one-way MANOVA across multiple continuous response variables grouped by a categorical factor, extracts multivariate test statistics, univariate ANOVA breakdowns, and generates either faceted boxplots or a Canonical Discriminant Analysis (LDA) multivariate scatter plot.

Usage

manova_analysis(
  data,
  response_vars,
  factor_var,
  factor_levels = NULL,
  plot_type = "boxplot",
  color_palette = "Set1"
)

Arguments

Value

A list containing the MANOVA test summary, univariate ANOVA summaries, and the ggplot object.

Integrated Mantel Test and Correlation Heatmap

Description

Computes internal correlations among environmental variables and performs Mantel tests linking community composition to environmental parameters using base vegan and ggplot2.

Usage

mantel_heatmap_analysis(
  comm_data,
  env_data,
  method = "pearson",
  spec_dist = "bray",
  env_dist = "euclidean",
  color_palette = "RdBu"
)

Arguments

Value

A list containing the correlation matrix, Mantel test results, and the ggplot object.

One-Way Analysis of Variance (ANOVA) with Visualization

Description

This function performs a one-way ANOVA, calculates Tukey's HSD post-hoc test, extracts Estimated Marginal Means (EMMs), and generates a publication-ready plot.

Usage

one_way_anova(
  data,
  factor_var,
  numeric_var,
  factor_levels = NULL,
  plot_type = "boxplot",
  error_type = "se",
  sig_display = "letters",
  y_limits = NULL,
  add_jitter = TRUE,
  show_mean = TRUE,
  mean_color = "darkred",
  color_palette = "Set1"
)

Arguments

Value

A list containing the ANOVA summary, Tukey HSD results, EMMs, a clean summary table, and the ggplot object.

Flexible Regression Analysis with Shaded Confidence Intervals and Statistics

Description

This function fits linear, logarithmic, or polynomial regression models to data, calculates coefficients, R-squared, and p-values, and generates a publication-ready plot with confidence interval shading and equation annotations.

Usage

regression_analysis(
  data,
  x_var,
  y_var,
  fit_type = "linear",
  group_var = NULL,
  color_palette = "Set1"
)

Arguments

Value

A list containing model summaries, regression parameters, and the ggplot object.

Three-Way Analysis of Variance (ANOVA) with Faceted Interaction Visualization

Description

This function performs a three-way ANOVA with full interaction effects, calculates Tukey's HSD post-hoc tests, extracts Estimated Marginal Means (EMMs), and generates faceted publication-ready interaction plots with compact letter displays.

Usage

three_way_anova(
  data,
  factor1_var,
  factor2_var,
  factor3_var,
  numeric_var,
  factor1_levels = NULL,
  factor2_levels = NULL,
  factor3_levels = NULL,
  plot_type = "boxplot",
  error_type = "se",
  y_limits = NULL,
  add_jitter = TRUE,
  show_mean = TRUE,
  mean_color = "darkred",
  color_palette = "Set1"
)

Arguments

Value

A list containing the ANOVA summary, Tukey HSD results, EMMs, summary statistics table, and the ggplot object.

Two-Way Analysis of Variance (ANOVA) with Interaction Visualization

Description

This function performs a two-way ANOVA with interaction effects, calculates Tukey's HSD post-hoc tests, extracts Estimated Marginal Means (EMMs), and generates publication-ready interaction plots with compact letter displays.

Usage

two_way_anova(
  data,
  factor1_var,
  factor2_var,
  numeric_var,
  factor1_levels = NULL,
  factor2_levels = NULL,
  plot_type = "boxplot",
  error_type = "se",
  y_limits = NULL,
  add_jitter = TRUE,
  show_mean = TRUE,
  mean_color = "darkred",
  color_palette = "Set1"
)

Arguments

Value

A list containing the ANOVA summary, Tukey HSD results, EMMs, summary statistics table, and the ggplot object.