Package 'biometryassist'

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,
  ...
)

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

Arguments

object	An object to create a plot for. Currently objects from the multiple_comparisons() or design() functions with class "mct" or "design" respectively are supported.
...	Arguments passed to methods.
size	Increase or decrease the text size within the plot for treatment labels. Numeric with default value of 4.
label_height	Height of the text labels above the upper error bar on the plot. Default is 0.1 (10%) of the difference between upper and lower error bars above the top error bar. Values > 1 are interpreted as the actual value above the upper error bar.
rotation	Rotate the x axis labels and the treatment group labels within the plot. Allows for easier reading of long axis or treatment labels. Number between 0 and 360 (inclusive) - default 0
axis_rotation	Enables rotation of the x axis independently of the group labels within the plot.
label_rotation	Enables rotation of the treatment group labels independently of the x axis labels within the plot.
margin	Logical (default FALSE). A value of FALSE will expand the plot to the edges of the plotting area i.e. remove white space between plot and axes.
palette	A string specifying the colour scheme to use for plotting. Default is equivalent to "Spectral". Colour blind friendly palettes can also be provided via options "colour blind" (or "color blind", both equivalent to "viridis"), "magma", "inferno", "plasma" or "cividis". Other palettes from scales::brewer_pal() are also possible.
buffer	A string specifying the buffer plots to include for plotting. Default is NULL (no buffers plotted). Other options are "edge" (outer edge of trial area), "rows" (between rows), "columns" (between columns), "double row" (a buffer row each side of a treatment row) or "double column" (a buffer row each side of a treatment column). "blocks" (a buffer around each treatment block) will be implemented in a future release.
row	A variable to plot a column from object as rows.
column	A variable to plot a column from object as columns.
block	A variable to plot a column from object as blocks.
treatments	A variable to plot a column from object as treatments.

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")

---

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("", " "),
  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

design.obj	An agricolae design object.
nrows	The number of rows in the design.
ncols	The number of columns in the design.
brows	For RCBD only. The number of rows in a block.
bcols	For RCBD only. The number of columns in a block.
byrow	For split-plot only. Logical (default: TRUE). Provides a way to arrange plots within whole-plots when there are multiple possible arrangements.
fac.names	Allows renaming of the A level of factorial designs (i.e. those using agricolae::design.ab()) by passing (optionally named) vectors of new labels to be applied to the factors within a list. See examples and details for more information.
fac.sep	The separator used by fac.names. Used to combine factorial design levels. If a vector of 2 levels is supplied, the first separates factor levels and label, and the second separates the different factors.
plot	Logical (default TRUE). If TRUE, display a plot of the generated design. A plot can always be produced later using autoplot().
rotation	Rotate the text output as Treatments within the plot. Allows for easier reading of long treatment labels. Takes positive and negative values being number of degrees of rotation from horizontal.
size	Increase or decrease the text size within the plot for treatment labels. Numeric with default value of 4.
margin	Logical (default FALSE). Setting to TRUE will add a margin (white space) between plot and axes.
save	One of FALSE (default)/"none", TRUE/"both", "plot" or "workbook". Specifies which output to save.
savename	A filename for the design to be saved to. Default is the type of the design combined with "_design".
plottype	The type of file to save the plot as. Usually one of "pdf", "png", or "jpg". See ggplot2::ggsave() for all possible options.
return.seed	Logical (default TRUE). Output the seed used in the design?
quiet	Logical (default FALSE). Return the objects without printing output.
...	Additional parameters passed to ggplot2::ggsave() for saving the plot.

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("", " "),
  plot = TRUE,
  rotation = 0,
  size = 4,
  margin = FALSE,
  save = FALSE,
  savename = paste0(type, "_design"),
  plottype = "pdf",
  seed = TRUE,
  quiet = FALSE,
  ...
)

Arguments

type	The type of design. Supported design types are crd, rcbd, lsd, ⁠crossed:<type>⁠ where ⁠<type>⁠ is one of the previous types, and split. See Details for more information.
treatments	A vector containing the treatment names or labels.
reps	The number of replicates. Ignored for Latin Square Designs.
nrows	The number of rows in the design.
ncols	The number of columns in the design.
brows	For RCBD and Split Plot designs. The number of rows in a block.
bcols	For RCBD and Split Plot designs. The number of columns in a block.
byrow	For split-plot only. Logical (default TRUE). Provides a way to arrange plots within whole-plots when there are multiple possible arrangements.
sub_treatments	A vector of treatments for sub-plots in a split plot design.
fac.names	Allows renaming of the A level of factorial designs (i.e. those using agricolae::design.ab()) by passing (optionally named) vectors of new labels to be applied to the factors within a list. See examples and details for more information.
fac.sep	The separator used by fac.names. Used to combine factorial design levels. If a vector of 2 levels is supplied, the first separates factor levels and label, and the second separates the different factors.
plot	Logical (default TRUE). If TRUE, display a plot of the generated design. A plot can always be produced later using autoplot().
rotation	Rotate the text output as Treatments within the plot. Allows for easier reading of long treatment labels. Takes positive and negative values being number of degrees of rotation from horizontal.
size	Increase or decrease the text size within the plot for treatment labels. Numeric with default value of 4.
margin	Logical (default FALSE). Expand the plot to the edges of the plotting area i.e. remove white space between plot and axes.
save	One of FALSE (default)/"none", TRUE/"both", "plot" or "workbook". Specifies which output to save.
savename	A file name for the design to be saved to. Default is the type of the design combined with "_design".
plottype	The type of file to save the plot as. Usually one of "pdf", "png", or "jpg". See ggplot2::ggsave() for all possible options.
seed	Logical (default TRUE). If TRUE, return the seed used to generate the design. If a numeric value, use that value as the seed for the design.
quiet	Logical (default FALSE). Hide the output.
...	Additional parameters passed to ggplot2::ggsave() for saving the plot.

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)

---

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

data	A data frame containing the data to be plotted.
value	A column of data, containing the values that vary over the space which produces the colours.
x_axis	The column of data to use as the x axis data.
y_axis	The column of data to use as the y axis data.
grouping	An optional grouping variable to facet the plot by.
raster	Logical (default: TRUE). If TRUE uses ggplot2::geom_raster() for speed. Will not work if the grid is irregular.
smooth	Logical (default: FALSE). If raster is TRUE, interpolation can be applied across the grid to obtain a smoothed grid. Ignored if raster is FALSE.
palette	Colour palette to use. By default it will use the viridis (colour-blind friendly) palette. Other palettes available can be seen with grDevices::hcl.pals().
...	Other arguments passed to facet_wrap()

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
)

update_asreml(...)

Arguments

library	Library location to install ASReml-R. Uses first option in .libPaths() by default.
quiet	Logical (default FALSE). Should package be installed quietly?
force	Logical (default FALSE). Force ASReml-R to install. Useful for upgrading if it is already installed.
keep_file	Should the downloaded asreml package file be kept? Default is FALSE. TRUE downloads to current directory. A file path can also be provided to save to another directory. See Details for more information.
...	other arguments passed to install_asreml()

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)

## End(Not run)

---

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

model.obj	An ASReml-R model object
rand.terms	Random terms from the model. Default is NULL.
resid.terms	Residual terms from the model. Default is NULL.
decimals	Controls rounding of decimal places in output. Default is 3 decimal places.
numeric	Return p-values as numeric? Default is that they are characters, where very small values shown as less than a small number. See details for more.
quiet	Logical (default: FALSE). Hide warnings and messages?

Details

Typically p-values cannot be 0, and are usually just below some threshold of accuracy in calculation of probability.

Value

A dataframe containing the results of the test.

Examples

## Not run: 
library(asreml)
dat <- asreml::oats
dat <- dat[order(dat$Row, dat$Column),]

#Fit ASReml Model
model.asr <- asreml(yield ~ Nitrogen + Variety + Nitrogen:Variety,
                    random = ~ Blocks + Blocks:Wplots,
                    residual = ~ ar1(Row):ar1(Column),
                    data = dat)
oats.logl <- logl_test(
  model.obj = model.asr, rand.terms = c("Blocks", "Blocks:Wplots"),
  resid.terms = c("ar1(Row)", "ar1(Column)")
)
oats.logl

## End(Not run)

---

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 = NA,
  offset = NA,
  power = NA,
  decimals = 2,
  descending = FALSE,
  plot = FALSE,
  label_height = 0.1,
  rotation = 0,
  save = FALSE,
  savename = "predicted_values",
  order,
  pred.obj,
  pred,
  ...
)

Arguments

model.obj	An ASReml-R or aov model object. Will likely also work with lme (nlme::lme()), lmerMod (lme4::lmer()) models as well.
classify	Name of predictor variable as string.
sig	The significance level, numeric between 0 and 1. Default is 0.05.
int.type	The type of confidence interval to calculate. One of ci, ⁠1se⁠ or ⁠2se⁠. Default is ci.
trans	Transformation that was applied to the response variable. One of log, sqrt, logit, power or inverse. Default is NA.
offset	Numeric offset applied to response variable prior to transformation. Default is NA. Use 0 if no offset was applied to the transformed data. See Details for more information.
power	Numeric power applied to response variable with power transformation. Default is NA. See Details for more information.
decimals	Controls rounding of decimal places in output. Default is 2 decimal places.
descending	Logical (default FALSE). Order of the output sorted by the predicted value. If TRUE, largest will be first, through to smallest last.
plot	Automatically produce a plot of the output of the multiple comparison test? Default is FALSE. This is maintained for backwards compatibility, but the preferred method now is to use ⁠autoplot(<multiple_comparisons output>)⁠. See autoplot.mct() for more details.
label_height	Height of the text labels above the upper error bar on the plot. Default is 0.1 (10%) of the difference between upper and lower error bars above the top error bar.
rotation	Rotate the text output as Treatments within the plot. Allows for easier reading of long treatment labels. Number between 0 and 360 (inclusive) - default 0
save	Logical (default FALSE). Save the predicted values to a csv file?
savename	A file name for the predicted values to be saved to. Default is predicted_values.
order	Deprecated. Use descending instead.
pred.obj	Deprecated. Predicted values are calculated within the function from version 1.0.1 onwards.
pred	Deprecated. Use classify instead.
...	Other arguments passed through to predict.asreml().

Details

Some transformations require that data has a small offset applied, otherwise it will cause errors (for example taking a log of 0, or square root of negative values). In order to correctly reverse this offset, if the trans argument is supplied, an offset value must also be supplied. If there was no offset required for a transformation, then use a value of 0 for the offset argument.

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–and-why-they-should-not-be-used(d649ca20-d15f-11db-8e26-000ea68e967b).html

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
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)



## 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)

---

Print output of multiple_comparisons

Description

Print output of multiple_comparisons

Usage

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

Arguments

x	An mct object to print to the console.
...	Other 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)

---

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,
  mod.obj
)

Arguments

model.obj	An aov, lm, lme (nlme::lme()), lmerMod (lme4::lmer()), asreml or mmer (sommer) model object.
shapiro	(Logical) Display the Shapiro-Wilks test of normality on the plot?
call	(Logical) Display the model call on the plot?
label.size	A numeric value for the size of the label (A,B,C) font point size.
axes.size	A numeric value for the size of the axes label font size in points.
call.size	A numeric value for the size of the model displayed on the plot.
mod.obj	Deprecated to be consistent with other functions. Please use model.obj instead.

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)

---

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

data	A data frame containing the variables to be plotted.
response	The response variable to plot.
exp_var	The explanatory (or grouping) variable(s) to plot. Up to three can be provided.
resp_units	A string providing units to display on the response variable (y) axis. Will use the empty string by default so axes will have no units by default.

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")

---

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"
)

Arguments

model.obj	An asreml model object.
row	A row variable.
column	A column variable.
horizontal	Logical (default TRUE). The direction the plots are arranged. The default TRUE places the plots above and below, while FALSE will place them side by side.
palette	A string specifying the colour scheme to use for plotting. The default value ("default") is equivalent to "rainbow". Colour blind friendly palettes can also be provided via options "colo(u)r blind" (both equivalent to "viridis"), "magma", "inferno", "plasma", "cividis", "rocket", "mako" or "turbo". The "Spectral" palette from scales::brewer_pal() is also possible.

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)

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