rfriend (version 1.0.0)

f_aov: Perform multiple aov() functions with optional data transformation, inspection and Post Hoc test.

Description

Performs an Analysis of Variance (ANOVA) on a given dataset with options for (Box-Cox) transformations, normality tests, and post-hoc analysis. Several response parameters can be analysed in sequence and the generated output can be in various formats ('Word', 'pdf', 'Excel').

Usage

f_aov(
  formula,
  data = NULL,
  norm_plots = TRUE,
  ANCOVA = FALSE,
  transformation = TRUE,
  alpha = 0.05,
  adjust = "sidak",
  aov_assumptions_text = TRUE,
  close_generated_files = FALSE,
  open_generated_files = TRUE,
  output_type = "off",
  output_file = NULL,
  output_dir = NULL,
  save_in_wdir = FALSE
)

Value

An object of class 'f_aov' containing results from aov(), normality tests, transformations, and post hoc tests. Using the option "output_type", it can also generate output in the form of: R Markdown code, 'Word', 'pdf', or 'Excel' files. Includes print and plot methods for 'f_aov' objects.

Arguments

formula

A formula specifying the model to be fitted. More response variables can be added using - or + (e.g., response1 + response2 ~ predictor) to do a sequential aov() for each response parameter.

data

A data frame containing the variables in the model.

norm_plots

Logical. If TRUE, plots are included in the output files. Default is TRUE.

ANCOVA

Logical. If TRUE, prevents automatic conversion of predictors to factors, allowing for Analysis of Covariance (ANCOVA). Default is FALSE.

transformation

Logical or character string. If TRUE, or if "bestnormalize", applies bestNormalize() transformation if residuals are not normal. If "boxcox" applies a boxcox transformation. If FALSE no transformation will be applied. Default is TRUE.

alpha

Numeric. Significance level for ANOVA, post hoc tests, and Shapiro-Wilk test. Default is 0.05.

adjust

Character string specifying the method used to adjust p-values for multiple comparisons. Available methods include:

"tukey"

Tukey's Honest Significant Difference method, appropriate for all pairwise comparisons. Controls family-wise error rate.

"sidak"

Šidák correction that controls the family-wise error rate. Less conservative than Bonferroni.

"bonferroni"

Conservative adjustment that multiplies p-values by the number of comparisons.

"none"

No adjustment. Equivalent to Fisher's LSD method.

"fdr"

False Discovery Rate adjustment, controls the expected proportion of false positives among significant results.

Default is "sidak".

aov_assumptions_text

Logical. If TRUE, includes a short explanation about ANOVA assumptions in the output file. Default is TRUE.

close_generated_files

Logical. If TRUE, closes open 'Excel' or 'Word' files depending on the output format. This to be able to save the newly generated file by the f_aov() function. 'Pdf' files should also be closed before using the function and cannot be automatically closed. Default is FALSE.

open_generated_files

Logical. If TRUE, Opens the generated output files ('pdf', 'Word' or 'Excel') files depending on the output format. This to directly view the results after creation. Files are stored in tempdir(). Default is TRUE.

output_type

Character string specifying the output format: "pdf", "word", "excel", "rmd", "console" or "off" (no file generated). The option "console" forces output to be printed. Default is "off".

output_file

Character string specifying the name of the output file. Default is "dataname_aov_output".

output_dir

Character string specifying the name of the directory of the output file. Default is tempdir(). If the output_file already contains a directory name output_dir can be omitted, if used it overwrites the dir specified in output_file.

save_in_wdir

Logical. If TRUE, saves the file in the working directory Default is FALSE, to avoid unintended changes to the global environment. If the output_dir is specified save_in_wdir is overwritten with output_dir.

Author

Sander H. van Delden

Details

The function performs the following steps:

• Check if all specified variables are present in the data.

• Ensure that the response variable is numeric.

• Perform Analysis of Variance (ANOVA) using the specified formula and data.

• If shapiro = TRUE, check for normality of residuals using the Shapiro-Wilk test.

• If residuals are not normal and transformation = TRUE apply a data transformation.

• If significant differences are found in ANOVA, proceed with post hoc tests using estimated marginal means from emmeans() and Sidak adjustment (or another option of adjust =.

More response variables can be added using - or + (e.g., response1 + response2 ~ predictor) to do a sequential aov() for each response parameter captured in one output file.

Outputs can be generated in multiple formats ("pdf", "word", "excel" and "rmd") as specified by output_type. The function also closes any open 'Word' files to avoid conflicts when generating 'Word' documents. If output_type = "rmd" is used it is adviced to use it in a chunk with {r, echo=FALSE, results='asis'}

This function requires [Pandoc](https://github.com/jgm/pandoc/releases/tag) (version 1.12.3 or higher), a universal document converter.

• Windows: Install Pandoc and ensure the installation folder.
  (e.g., "C:/Users/your_username/AppData/Local/Pandoc") is added to your system PATH.

• macOS: If using Homebrew, Pandoc is typically installed in "/usr/local/bin". Alternatively, download the .pkg installer and verify that the binary’s location is in your PATH.

• Linux: Install Pandoc through your distribution’s package manager (commonly installed in "/usr/bin" or "/usr/local/bin") or manually, and ensure the directory containing Pandoc is in your PATH.

• If Pandoc is not found, this function may not work as intended.

Examples

# Make a factor of Species.
iris$Species <- factor(iris$Species)

# The left hand side contains two response variables,
# so two aov's will be conducted, i.e. "Sepal.Width"
# and "Sepal.Length" in response to the explanatory variable: "Species".
f_aov_out <- f_aov(Sepal.Width + Sepal.Length ~ Species,
                   data = iris,
                   # Save output in MS Word file (Default is console)
                   output_type = "word",
                   # Do boxcox transformation for non-normal residual (Default is bestnormalize)
                   transformation = "boxcox",
                   # Do not automatically open the file.
                   open_generated_files = FALSE
                   )

# Print output to the console.
print(f_aov_out)

# Plot residual plots.
plot(f_aov_out)

#To print rmd output set chunck option to results = 'asis' and use cat().
f_aov_rmd_out <- f_aov(Sepal.Width ~ Species, data = iris, output_type = "rmd")
cat(f_aov_rmd_out$rmd)