biometryassist: Functions to Assist Design and Analysis of Agronomic Experiments

Description

Provides functions to aid in the design and analysis of agronomic and agricultural experiments through easy access to documentation and helper functions, especially for users who are learning these concepts. While not required for most functionality, this package enhances the ‘asreml' package which provides a computationally efficient algorithm for fitting mixed models using Residual Maximum Likelihood. It is a commercial package that can be purchased as ’asreml-R' from 'VSNi' https://vsni.co.uk/, who will supply a zip file for local installation/updating (see https://asreml.kb.vsni.co.uk/).

Author(s)

Maintainer: Sam Rogers biometrytraining@adelaide.edu.au

Authors:

• Sharon Nielsen biometrytraining@adelaide.edu.au

• Annie Conway biometrytraining@adelaide.edu.au

Other contributors:

• University of Adelaide (https://adelaide.edu.au/) [copyright holder, funder]

• Grains Research and Development Corporation (https://grdc.com.au/) [copyright holder, funder]

See Also

Useful links:

• https://biometryhub.github.io/biometryassist/

• Report bugs at https://github.com/biometryhub/biometryassist/issues

Check Package Availability

Description

Internal function to check if a package is available for loading. This wrapper around .check_package_available makes testing easier by allowing the dependency check to be mocked.

Usage

.check_package_available(pkg)

Arguments

Value

Logical value: TRUE if the package is available, FALSE otherwise

Function to compare package version for mocking

Description

Function to compare package version for mocking

Usage

.compare_version(a, b)

Arguments

Value

Numeric. 0 if the numbers are equal, -1 if b is later and 1 if a is later

Determine if a Colour is Light

Description

Internal helper function to determine whether a colour is light or dark for appropriate font colour selection (black text on light backgrounds, white text on dark backgrounds).

Usage

.is_light_colour(colour)

Arguments

Details

Uses standard luminance calculation: 0.299R + 0.587G + 0.114*B, normalized to 0-1 scale. Coefficients reflect human eye sensitivity to different colours (green > red > blue).

Value

Logical. TRUE if the colour is light (luminance > 0.5), FALSE if dark.

Add buffers to an existing design

Description

Add buffers to an existing design

Usage

add_buffers(design_obj, type)

Arguments

Value

The modified design object with buffers added

Examples

# Create a simple CRD design
des <- design(type = "crd", treatments = c("A", "B"), reps = 3, nrows = 2, ncols = 3, seed = 42)

# Plot the original design
autoplot(des)

# Add edge buffers to the design
des_buf <- add_buffers(des, type = "edge")

# Plot the design with buffers
autoplot(des_buf)

# Add double row buffers
des_row_buf <- add_buffers(des, type = "double row")
autoplot(des_row_buf)

Generate automatic plots for objects generated in biometryassist

Description

Generate automatic plots for objects generated in biometryassist

Usage

autoplot(object, ...)

## S3 method for class 'mct'
autoplot(
  object,
  size = 4,
  label_height = 0.1,
  rotation = 0,
  axis_rotation = rotation,
  label_rotation = rotation,
  type = "point",
  ...
)

## S3 method for class 'design'
autoplot(
  object,
  rotation = 0,
  size = 4,
  margin = FALSE,
  palette = "default",
  row = NULL,
  column = NULL,
  block = NULL,
  treatments = NULL,
  ...
)

Arguments

Value

A ggplot2 object.

See Also

multiple_comparisons() and design()

Examples

dat.aov <- aov(Petal.Width ~ Species, data = iris)
output <- multiple_comparisons(dat.aov, classify = "Species")
autoplot(output, label_height = 0.5)
des.out <- design(type = "crd", treatments = c(1, 5, 10, 20),
                  reps = 5, nrows = 4, ncols = 5, seed = 42, plot = FALSE)
autoplot(des.out)

# Colour blind friendly colours
autoplot(des.out, palette = "colour-blind")

# Alternative colour scheme
autoplot(des.out, palette = "plasma")

# Custom colour palette
autoplot(des.out, palette = c("#ef746a", "#3fbfc5", "#81ae00", "#c37cff"))

# Visualise different components of a split plot design
des.out <- design(type = "split", treatments = c("A", "B"), sub_treatments = 1:4,
reps = 4, nrows = 8, ncols = 4, brows = 4, bcols = 2, seed = 42)

# Show the wholeplot components
autoplot(des.out, treatments = wholeplots)

# Display block level
autoplot(des.out, treatments = block)

Deprecated functions in package biometryassist.

Description

The functions listed below are deprecated and will be removed in the future. When possible, alternative functions with similar functionality are also mentioned. Help pages for deprecated functions are available at help("<function>-deprecated").

Usage

resplt(
  model.obj,
  shapiro = TRUE,
  call = FALSE,
  label.size = 10,
  axes.size = 10,
  call.size = 9,
  mod.obj
)

Value

No return value.

A list containing ggplot2 objects which are diagnostic plots. For resplt, use resplot().

resplt

Residual plots of linear models.

Combine plots into final output

Description

Combine plots into final output

Usage

combine_plots(plots, shapiro_result, model_call, call, call.size, label.size)

Arguments

Create buffers for design plots

Description

Create buffers for design plots

Usage

create_buffers(design, type, blocks = FALSE)

Arguments

Value

The original data frame, updated to include buffers

Create diagnostic plots

Description

Create diagnostic plots

Usage

create_diagnostic_plots(group_residuals, axes.size, label.size)

Arguments

Create the folder MacOS needs for licensing

Description

Create the folder MacOS needs for licensing

Usage

create_mac_folder()

Value

logical; TRUE if folder successfully created, otherwise it will error

Produce a graph of design layout, skeletal ANOVA table and data frame with complete design

Description

Produce a graph of design layout, skeletal ANOVA table and data frame with complete design

Usage

des_info(
  design.obj,
  nrows,
  ncols,
  brows = NA,
  bcols = NA,
  byrow = TRUE,
  fac.names = NULL,
  fac.sep = c("", " "),
  buffer = NULL,
  plot = TRUE,
  rotation = 0,
  size = 4,
  margin = FALSE,
  save = FALSE,
  savename = paste0(design.obj$parameters$design, "_design"),
  plottype = "pdf",
  return.seed = TRUE,
  quiet = FALSE,
  ...
)

Arguments

Details

If save = TRUE (or "both"), both the plot and the workbook will be saved to the current working directory, with filename given by savename. If one of either "plot" or "workbook" is specified, only that output is saved. If save = FALSE (the default, or equivalently "none"), nothing will be output.

fac.names can be supplied to provide more intuitive names for factors and their levels in factorial designs. They should be specified in a list format, for example fac.names = list(A_names = c("a", "b", "c"), B_names = c("x", "y", "z")). This will result a design output with a column named A_names with levels ⁠a, b, c⁠ and another named B_names with levels ⁠x, y, z⁠. Only the first two elements of the list will be used.

If fac.sep is a single element (e.g. ""), this is used to separate all factor labels (e.g. A_1_B_1). If it is two elements (e.g. c("", "")), the first element separates the factor names and their levels, and the second level separates the two factors (e.g. A1_B1).

... allows extra arguments to be passed to ggsave for output of the plot. The details of possible arguments can be found in ggplot2::ggsave().

Value

A list containing a data frame with the complete design, a ggplot object with plot layout, the seed (if return.seed = TRUE), and the satab object, allowing repeat output of the satab table via cat(output$satab).

Examples

library(agricolae)

# Completely Randomised Design
trt <- c(1, 5, 10, 20)
rep <- 5
outdesign <- design.crd(trt = trt, r = rep, seed = 42)
des.out <- des_info(design.obj = outdesign, nrows = 4, ncols = 5)

# Randomised Complete Block Design
trt <- LETTERS[1:11]
rep <- 4
outdesign <- design.rcbd(trt = trt, r = rep, seed = 42)
des.out <- des_info(
  design.obj = outdesign, nrows = 11,
  ncols = 4, brows = 11, bcols = 1
)

# Latin Square Design
trt <- c("S1", "S2", "S3", "S4")
outdesign <- design.lsd(trt)
des.out <- des_info(design.obj = outdesign, nrows = 4, ncols = 4)

# Factorial Design (Crossed, Completely Randomised)
trt <- c(3, 2) # Factorial 3 x 2
rep <- 3
outdesign <- design.ab(trt, r = rep, design = "crd")
des.out <- des_info(design.obj = outdesign, nrows = 6, ncols = 3)

# Factorial Design (Crossed, Completely Randomised), renaming factors
trt <- c(3, 2) # Factorial 3 x 2
rep <- 3
outdesign <- design.ab(trt, r = rep, design = "crd")
des.out <- des_info(design.obj = outdesign, nrows = 6, ncols = 3,
                    fac.names = list(N = c(50, 100, 150),
                                     Water = c("Irrigated", "Rain-fed")))

# Factorial Design (Nested, Latin Square)
trt <- c("A1", "A2", "A3", "A4", "B1", "B2", "B3")
outdesign <- design.lsd(trt)
des.out <- des_info(design.obj = outdesign, nrows = 7, ncols = 7)

# Split plot design
trt1 <- c("A", "B")
trt2 <- 1:4
rep <- 4
outdesign <- design.split(trt1, trt2, r = rep)
des.out <- des_info(design.obj = outdesign, nrows = 8, ncols = 4, brows = 4, bcols = 2)

Create a complete experimental design with graph of design layout and skeletal ANOVA table

Description

Create a complete experimental design with graph of design layout and skeletal ANOVA table

Usage

design(
  type,
  treatments,
  reps,
  nrows,
  ncols,
  brows = NA,
  bcols = NA,
  byrow = TRUE,
  sub_treatments = NULL,
  fac.names = NULL,
  fac.sep = c("", " "),
  buffer = NULL,
  plot = TRUE,
  rotation = 0,
  size = 4,
  margin = FALSE,
  save = FALSE,
  savename = paste0(type, "_design"),
  plottype = "pdf",
  seed = TRUE,
  quiet = FALSE,
  ...
)

Arguments

Details

The designs currently supported by type are Completely Randomised designs (crd), Randomised Complete Block designs (rcbd), Latin Square Designs (lsd), Factorial with crossed structure (use ⁠crossed:<type>⁠ where ⁠<type>⁠ is one of the previous types e.g. crossed:crd) and Split Plot designs (split). Nested factorial designs are supported through manual setup, see Examples.

If save = TRUE (or "both"), both the plot and the workbook will be saved to the current working directory, with filename given by savename. If one of either "plot" or "workbook" is specified, only that output is saved. If save = FALSE (the default, or equivalently "none"), nothing will be output.

fac.names can be supplied to provide more intuitive names for factors and their levels in factorial and split plot designs. They can be specified in a list format, for example fac.names = list(A_names = c("a", "b", "c"), B_names = c("x", "y", "z")). This will result a design output with a column named A_names with levels ⁠a, b, c⁠ and another named B_names with levels ⁠x, y, z⁠. Labels can also be supplied as a character vector (e.g. c("A", "B")) which will result in only the treatment column names being renamed. Only the first two elements of the list will be used, except in the case of a 3-way factorial design.

... allows extra arguments to be passed to ggsave() for output of the plot. The details of possible arguments can be found in ggplot2::ggsave().

Value

A list containing a data frame with the complete design (⁠$design⁠), a ggplot object with plot layout (⁠$plot.des⁠), the seed (⁠$seed⁠, if return.seed = TRUE), and the satab object (⁠$satab⁠), allowing repeat output of the satab table via cat(output$satab).

Examples

# Completely Randomised Design
des.out <- design(type = "crd", treatments = c(1, 5, 10, 20),
                  reps = 5, nrows = 4, ncols = 5, seed = 42)

# Randomised Complete Block Design
des.out <- design("rcbd", treatments = LETTERS[1:11], reps = 4,
                  nrows = 11, ncols = 4, brows = 11, bcols = 1, seed = 42)

# Latin Square Design
# Doesn't require reps argument
des.out <- design(type = "lsd", c("S1", "S2", "S3", "S4"),
                  nrows = 4, ncols = 4, seed = 42)

# Factorial Design (Crossed, Completely Randomised)
des.out <- design(type = "crossed:crd", treatments = c(3, 2),
                  reps = 3, nrows = 6, ncols = 3, seed = 42)

# Factorial Design (Crossed, Completely Randomised), renaming factors
des.out <- design(type = "crossed:crd", treatments = c(3, 2),
                  reps = 3, nrows = 6, ncols = 3, seed = 42,
                  fac.names = list(N = c(50, 100, 150),
                                   Water = c("Irrigated", "Rain-fed")))

# Factorial Design (Crossed, Randomised Complete Block Design),
# changing separation between factors
des.out <- design(type = "crossed:rcbd", treatments = c(3, 2),
                  reps = 3, nrows = 6, ncols = 3,
                  brows = 6, bcols = 1,
                  seed = 42, fac.sep = c(":", "_"))

# Factorial Design (Nested, Latin Square)
trt <- c("A1", "A2", "A3", "A4", "B1", "B2", "B3")
des.out <- design(type = "lsd", treatments = trt,
                  nrows = 7, ncols = 7, seed = 42)

# Split plot design
des.out <- design(type = "split", treatments = c("A", "B"), sub_treatments = 1:4,
                  reps = 4, nrows = 8, ncols = 4, brows = 4, bcols = 2, seed = 42)

# Alternative arrangement of the same design as above
des.out <- design(type = "split", treatments = c("A", "B"), sub_treatments = 1:4,
                  reps = 4, nrows = 8, ncols = 4, brows = 4, bcols = 2,
                  byrow = FALSE, seed = 42)

Download asreml package file

Description

Download asreml package file

Usage

download_asreml_package(url, verbose = FALSE)

Arguments

Export Experimental Design Layout to Excel

Description

Converts an experimental design dataframe into a spatial layout matrix and exports to Excel with optional colour coding by treatment.

Usage

export_design_to_excel(
  design_df,
  value_column = "treatments",
  filename = "experimental_design.xlsx",
  palette = "default"
)

Arguments

Details

This function takes an experimental design in long format (with row/col coordinates) and converts it to a matrix layout that matches the spatial arrangement of the experiment, then exports to Excel with formatting and optional colour coding.

Valid palette options include:

• "default" - Spectral palette

• ColorBrewer palettes: "brbg", "piyg", "prgn", "puor", "rdbu", "rdgy", "rdylbu", "rdylgn", "spectral", "set3", "paired"

• Viridis palettes: "viridis", "magma", "inferno", "cividis", "plasma", "rocket", "mako", "turbo"

• colour blind friendly: "colour blind", "color blind", "cb" (uses viridis)

• Custom vector of colours (must match number of unique treatments)

Requires the 'openxlsx' package to be installed.

Value

Invisibly returns the layout dataframe

Examples

## Not run: 
# Export with default colours
export_design_to_excel(my_design, "treatments", "my_design.xlsx")

# Export without colours
export_design_to_excel(my_design, "treatments", "my_design.xlsx", palette = NULL)

# Export with custom palette
export_design_to_excel(my_design, "treatments", "my_design.xlsx", palette = "viridis")

## End(Not run)

Extract model information using S3 dispatch

Description

Extract model information using S3 dispatch

Usage

extract_model_info(model.obj, call = FALSE)

Arguments

Find existing asreml file

Description

Find existing asreml file

Usage

find_existing_package()

Format final output based on facet structure

Description

Format final output based on facet structure

Usage

format_output_resplot(output, facet, facet_name, onepage, onepage_cols)

Arguments

Get the version of R and OS

Description

Get the version of R and OS

Usage

get_r_os()

Value

A list with the version of R and the OS in a standard format

Get released versions of ASReml-R in lookup table

Description

Get released versions of ASReml-R in lookup table

Usage

get_version_table(
  url =
    "https://asreml.kb.vsni.co.uk/asreml-r-4-download-success/?site_reference=VS9AF20"
)

Value

A list of data frames containing the version number and release date of released ASReml-R versions for comparison

Handle deprecated parameters

Description

Simple internal function to warn about deprecated parameters

Usage

handle_deprecated_param(
  old_param,
  new_param = NULL,
  custom_msg = NULL,
  call_env = parent.frame()
)

Arguments

Value

Nothing, called for side effects (warnings)

Produce a heatmap of variables in a grid layout.

Description

This function plots a heatmap of variables in a grid layout, optionally grouping them.

Usage

heat_map(
  data,
  value,
  x_axis,
  y_axis,
  grouping = NULL,
  raster = TRUE,
  smooth = FALSE,
  palette = "default",
  ...
)

Arguments

Value

A ggplot2 object.

Examples

set.seed(42)
dat <- expand.grid(x = 1:5, y = 1:6)
dat$value <- rnorm(30)
dat$groups <- sample(rep(LETTERS[1:6], times = 5))

heat_map(dat, value, x, y)

# Column names can be quoted, but don't need to be.
heat_map(dat, "value", "x", "y", "groups")

# Different palettes are available
heat_map(dat, value, x, y, palette = "Spectral")

# Arguments in ... are passed through to facet_wrap
heat_map(dat, value, x, y, groups, labeller = ggplot2:::label_both)
heat_map(dat, value, x, y, groups, scales = "free_y")
heat_map(dat, value, x, y, groups, nrow = 1)

Install or update the ASReml-R package

Description

Helper functions for installing or updating the ASReml-R package, intended to reduce the difficulty of finding the correct version for your operating system and R version.

Usage

install_asreml(
  library = .libPaths()[1],
  quiet = FALSE,
  force = FALSE,
  keep_file = FALSE,
  check_version = TRUE
)

update_asreml(...)

Arguments

Details

The ASReml-R package file is downloaded from a shortlink, and if keep_file is TRUE, the package archive file will be saved in the current directory. If a valid path is provided in keep_file, the file will be saved to that path, but all directories are assumed to exist and will not be created. If keep_file does not specify an existing, valid path, an error will be shown after package installation.

Value

Silently returns TRUE if asreml installed successfully or already present, FALSE otherwise. Optionally prints a confirmation message on success.

Examples

## Not run: 
# Example 1: download and install asreml
install_asreml()

# Example 2: install asreml and save file for later
install_asreml(keep_file = TRUE)

# Example 3: install with verbose debugging
install_asreml(quiet = "verbose")

## End(Not run)

Install the ASReml package

Description

Install the ASReml package

Usage

install_asreml_package(save_file, library, quiet, os, verbose = FALSE)

Arguments

Value

TRUE if successful, FALSE otherwise

Install required dependencies

Description

Install required dependencies

Usage

install_dependencies(quiet, library, verbose = FALSE)

Arguments

List available biometryassist templates

Description

list_templates() returns a character vector of available analysis templates included with the biometryassist package.

Usage

list_templates()

Value

character() Vector of available template file names.

See Also

use_template() to copy and use a template

Examples

# See what templates are available
list_templates()

Conduct a log-likelihood test for comparing terms in ASReml-R models

Description

Conduct a log-likelihood test for comparing terms in ASReml-R models

Usage

logl_test(
  model.obj,
  rand.terms = NULL,
  resid.terms = NULL,
  decimals = 3,
  numeric = FALSE,
  quiet = FALSE
)

Arguments

Value

A data frame of terms and corresponding log-likelihood ratio test p-values.

Manage the downloaded file

Description

Manage the downloaded file

Usage

manage_file(save_file, keep_file, filename, verbose = FALSE)

Arguments

Value

logical; TRUE if file successfully handled, FALSE otherwise

Perform Multiple Comparison Tests on a statistical model

Description

A function for comparing and ranking predicted means with Tukey's Honest Significant Difference (HSD) Test.

Usage

multiple_comparisons(
  model.obj,
  classify,
  sig = 0.05,
  int.type = "ci",
  trans = NULL,
  offset = NULL,
  power = NULL,
  decimals = 2,
  descending = FALSE,
  groups = TRUE,
  plot = FALSE,
  label_height = 0.1,
  rotation = 0,
  save = FALSE,
  savename = "predicted_values",
  order,
  pred.obj,
  pred,
  ...
)

Arguments

Details

Offset

Some transformations require that data has a small offset to be applied, otherwise it will cause errors (for example taking a log of 0, or the square root of negative values). In order to correctly reverse this offset, if the trans argument is supplied, a value should also be supplied in the offset argument. By default the function assumes no offset was required for a transformation, implying a value of 0 for the offset argument. If an offset value is provided, use the same value as provided in the model, not the inverse. For example, if adding 0.1 to values for a log transformation, add 0.1 in the offset argument.

Power

The power argument allows the specification of arbitrary powers to be back transformed, if they have been used to attempt to improve normality of residuals.

#' ## Confidence Intervals & Comparison Intervals

The function provides several options for confidence intervals via the int.type argument:

• tukey (default): Tukey comparison intervals that are consistent with the multiple comparison test. These intervals are wider than regular confidence intervals and are designed so that non-overlapping intervals correspond to statistically significant differences in the Tukey HSD test. This ensures visual consistency between the intervals and letter groupings.

• ci: Traditional confidence intervals for individual means. These estimate the precision of each individual mean but may not align with the multiple comparison results. Non-overlapping traditional confidence intervals do not necessarily indicate significant differences in multiple comparison tests.

• ⁠1se⁠ and ⁠2se⁠: Intervals of ±1 or ±2 standard errors around each mean.

By default, the function displays regular confidence intervals (int.type = "ci"), which estimate the precision of individual treatment means. However, when performing multiple comparisons, these regular confidence intervals may not align with the letter groupings from Tukey's HSD test. Specifically, you may observe non-overlapping confidence intervals for treatments that share the same letter group (indicating no significant difference).

This occurs because regular confidence intervals and Tukey's HSD test serve different purposes:

• Regular confidence intervals estimate individual mean precision

• Tukey's HSD controls the family-wise error rate across all pairwise comparisons

To resolve this visual inconsistency, you can use Tukey comparison intervals (int.type = "tukey"). These intervals are specifically designed for multiple comparisons and will be consistent with the letter groupings: non-overlapping Tukey intervals indicate significant differences, while overlapping intervals suggest no significant difference.

The function will issue a message if it detects potential inconsistencies between non-overlapping confidence intervals and letter groupings, suggesting the use of Tukey intervals for clearer interpretation. For multiple comparison contexts, Tukey comparison intervals are recommended as they provide visual consistency with the statistical test being performed and avoid the common confusion where traditional confidence intervals don't overlap but groups share the same significance letter.

Value

A list containing a data frame with predicted means, standard errors, confidence interval upper and lower bounds, and significant group allocations (named predicted_values), as well as a plot visually displaying the predicted values (named predicted_plot). If some of the predicted values are aliased, a warning is printed, and the aliased treatment levels are returned in the output (named aliased).

References

Jørgensen, E. & Pedersen, A. R. (1997). How to Obtain Those Nasty Standard Errors From Transformed Data - and Why They Should Not Be Used. https://pure.au.dk/portal/en/publications/how-to-obtain-those-nasty-standard-errors-from-transformed-data-a

Examples

# Fit aov model
model <- aov(Petal.Length ~ Species, data = iris)

# Display the ANOVA table for the model
anova(model)

# Determine ranking and groups according to Tukey's Test (with Tukey intervals)
pred.out <- multiple_comparisons(model, classify = "Species")

# Display the predicted values table
pred.out

# Show the predicted values plot
autoplot(pred.out, label_height = 0.5)

# Use traditional confidence intervals instead of Tukey comparison intervals
pred.out.ci <- multiple_comparisons(model, classify = "Species", int.type = "ci")
pred.out.ci

# AOV model example with transformation
my_iris <- iris
my_iris$Petal.Length <- exp(my_iris$Petal.Length) # Create exponential response
exp_model <- aov(Petal.Length ~ Species, data = my_iris)

resplot(exp_model) # Residual plot shows problems

# Fit a new model using a log transformation of the response
log_model <- aov(log(Petal.Length) ~ Species, data = my_iris)

resplot(log_model) # Looks much better

# Display the ANOVA table for the model
anova(log_model)

# Back transform, because the "original" data was exponential
pred.out <- multiple_comparisons(log_model, classify = "Species",
                                   trans = "log")

# Display the predicted values table
pred.out

# Show the predicted values plot
autoplot(pred.out, label_height = 15)

## Not run: 
# ASReml-R Example
library(asreml)

#Fit ASReml Model
model.asr <- asreml(yield ~ Nitrogen + Variety + Nitrogen:Variety,
                    random = ~ Blocks + Blocks:Wplots,
                    residual = ~ units,
                    data = asreml::oats)

wald(model.asr) # Nitrogen main effect significant

#Determine ranking and groups according to Tukey's Test
pred.out <- multiple_comparisons(model.obj = model.asr, classify = "Nitrogen",
                    descending = TRUE, decimals = 5)

pred.out

# Example using a box-cox transformation
set.seed(42) # See the seed for reproducibility
resp <- rnorm(n = 50, 5, 1)^3
trt <- as.factor(sample(rep(LETTERS[1:10], 5), 50))
block <- as.factor(rep(1:5, each = 10))
ex_data <- data.frame(resp, trt, block)

# Change one treatment random values to get significant difference
ex_data$resp[ex_data$trt=="A"] <- rnorm(n = 5, 7, 1)^3

model.asr <- asreml(resp ~ trt,
                    random = ~ block,
                    residual = ~ units,
                    data = ex_data)

resplot(model.asr)

# Perform Box-Cox transformation and get maximum value
out <- MASS::boxcox(ex_data$resp~ex_data$trt)
out$x[which.max(out$y)] # 0.3838

# Fit cube root to the data
model.asr <- asreml(resp^(1/3) ~ trt,
                    random = ~ block,
                    residual = ~ units,
                    data = ex_data)
resplot(model.asr) # residual plots look much better

#Determine ranking and groups according to Tukey's Test
pred.out <- multiple_comparisons(model.obj = model.asr,
                                 classify = "trt",
                                 trans = "power", power = (1/3))

pred.out
autoplot(pred.out)

## End(Not run)

Compare installed version of ASReml-R with available versions

Description

Compare installed version of ASReml-R with available versions

Usage

newer_version()

Value

TRUE if a newer version is available online, FALSE otherwise

Parse version table from web scraping

Description

Parse version table from web scraping

Usage

parse_version_table(tables, headers)

Arguments

Value

Combined data frame of version information

Get Predictions for Statistical Models

Description

A generic function to get predictions for statistical models.

Usage

get_predictions(model.obj, classify, pred.obj = NULL, ...)

## Default S3 method:
get_predictions(model.obj, ...)

## S3 method for class 'asreml'
get_predictions(model.obj, classify, pred.obj = NULL, ...)

## S3 method for class 'lm'
get_predictions(model.obj, classify, ...)

## S3 method for class 'lmerMod'
get_predictions(model.obj, classify, ...)

## S3 method for class 'lmerModLmerTest'
get_predictions(model.obj, classify, ...)

Arguments

Value

A list containing predictions, standard errors, degrees of freedom, response variable label, and aliased names.

Print output of multiple_comparisons

Description

Print output of multiple_comparisons

Usage

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

Arguments

Value

The original object invisibly.

See Also

multiple_comparisons()

Examples

dat.aov <- aov(Petal.Width ~ Species, data = iris)
output <- multiple_comparisons(dat.aov, classify = "Species")
print(output)

Process aliased treatments in predictions

Description

Process aliased treatments in predictions

Usage

process_aliased(
  pp,
  sed,
  classify,
  exclude_cols = c("predicted.value", "std.error", "df", "Names")
)

Arguments

Value

List containing processed predictions, sed matrix and aliased names

Function to suppress output if desired, especially useful for ASReml output

Description

Function to suppress output if desired, especially useful for ASReml output

Usage

quiet(x)

Arguments

Value

The invisible output of the function called.

Remove existing ASReml installation

Description

Remove existing ASReml installation

Usage

remove_existing_asreml(verbose = FALSE)

Arguments

Produce residual plots of linear models

Description

Produces plots of residuals for assumption checking of linear (mixed) models.

Usage

resplot(
  model.obj,
  shapiro = TRUE,
  call = FALSE,
  label.size = 10,
  axes.size = 10,
  call.size = 9,
  onepage = FALSE,
  onepage_cols = 3,
  mod.obj
)

Arguments

Value

A ggplot2 object containing the diagnostic plots.

Examples

dat.aov <- aov(Petal.Length ~ Petal.Width, data = iris)
resplot(dat.aov)
resplot(dat.aov, call = TRUE)

Residual plots of linear models.

Description

Produces plots of residuals for assumption checking of linear (mixed) models.

Usage

resplt(model.obj, shapiro = TRUE, call = FALSE, label.size = 10,
axes.size = 10, call.size = 9, mod.obj)

Arguments

Value

A list containing ggplot2 objects which are diagnostic plots.

See Also

biometryassist-deprecated

Produces a skeletal ANOVA table

Description

Produces a skeletal ANOVA table

Usage

satab(design.obj)

Arguments

Value

Prints skeletal ANOVA table to console output.

Shapiro-Wilk test

Description

Shapiro-Wilk test

Usage

shapiro_test(group_residuals)

Arguments

Visualise a graphical summary of variables from a data frame

Description

Variables are plotted in different ways according to the number of explanatory variables provided as input.

Usage

summary_graph(data, response, exp_var, resp_units = "")

Arguments

Details

With a single explanatory variable, a boxplot grouped by exp_var is produced. With two explanatory variables, a dot-plot with lines connecting the mean of each group is produced, with the first element of exp_var used as the x axis variable, and the second is used to colour the points. Three explanatory variables produces the same as two, but with the third used to facet the plot.

Value

A ggplot2 plot object

Examples

summary_graph(iris, "Petal.Length", "Species", "mm")

# Multiple explanatory variables can be provided as a vector
summary_graph(npk, "yield", c("N", "P"), "lb/plot")

summary_graph(npk, "yield", c("N", "P", "K"), "lb/plot")

Use biometryassist analysis templates

Description

use_template() copies a pre-built analysis template from the biometryassist package to your working directory and optionally opens it for editing. These templates provide standardized approaches for common agronomic analyses.

Usage

use_template(
  template_name = "mixed_model_template.R",
  dest_dir = ".",
  open = TRUE,
  overwrite = FALSE,
  output_name = NULL
)

Arguments

Details

This function is designed to help users get started with biometryassist analyses by providing tested, documented templates. The templates include:

• Suggested package loading

• Commented code explaining steps

• Example data exploration

• Common analysis workflows

If a file with the same name already exists in the destination directory, the function will not overwrite it unless overwrite = TRUE is specified. In this case, it will still open the existing file if open = TRUE.

Value

The file path to the copied template (invisibly). Called primarily for its side effects of copying and optionally opening the template file.

See Also

• list_templates() to see available templates

Examples

## Not run: 
# Copy and open the default analysis template
use_template()

# Copy a specific template without opening
use_template("anova_template.R", open = FALSE)

# Copy to a specific directory
use_template("mixed_model_template.R", dest_dir = "analyses")

# Overwrite an existing file
use_template("mixed_model_template.R", overwrite = TRUE)

## End(Not run)

Calculate the variogram data frame for a model

Description

Calculate the variogram data frame for a model

Usage

vario_df(model.obj, Row = NA, Column = NA)

Arguments

Value

A data frame with the variogram for a model. The data frame contains the spatial coordinates (typically row and column), the gamma for that position and the number of points with the separation.

Examples

## Not run: 
library(asreml)
oats <- asreml::oats
oats <- oats[order(oats$Row, oats$Column),]
model.asr <- asreml(yield ~ Nitrogen + Variety + Nitrogen:Variety,
                    random = ~ Blocks + Blocks:Wplots,
                    residual = ~ ar1(Row):ar1(Column),
                    data = oats)
vario_df(model.asr)

## End(Not run)

Display variogram plots for spatial models

Description

Produces variogram plots for checking spatial trends.

Usage

variogram(
  model.obj,
  row = NA,
  column = NA,
  horizontal = TRUE,
  palette = "default",
  onepage = FALSE
)

Arguments

Value

A ggplot2 object.

References

S. P. Kaluzny, S. C. Vega, T. P. Cardoso, A. A. Shelly, "S+SpatialStats: User’s Manual for Windows® and UNIX®" Springer New York, 2013, p. 68, https://books.google.com.au/books?id=iADkBwvario_pointsQBAJ.

A. R. Gilmour, B. R. Cullis, A. P. Verbyla, "Accounting for Natural and Extraneous Variation in the Analysis of Field Experiments." Journal of Agricultural, Biological, and Environmental Statistics 2, no. 3, 1997, pp. 269–93, https://doi.org/10.2307/1400446.

Examples

## Not run: 
library(asreml)
oats <- asreml::oats
oats <- oats[order(oats$Row, oats$Column),]
model.asr <- asreml(yield ~ Nitrogen + Variety + Nitrogen:Variety,
                    random = ~ Blocks + Blocks:Wplots,
                    residual = ~ ar1(Row):ar1(Column),
                    data = oats)
variogram(model.asr)

## End(Not run)