asremlPlus-package: tools:::Rd_package_title("asremlPlus")

Description

tools:::Rd_package_description("asremlPlus")

Version: utils::packageDescription("asremlPlus", fields="Version")

Date: utils::packageDescription("asremlPlus", fields="Date")

Arguments

Index

(i) Data
`Oats.dat`	Data for an experiment to investigate nitrogen response of
	3 oats varieties.
`Wheat.dat`	Data for an experiment to investigate 25 varieties of
	wheat.
`WaterRunoff.dat`	Data for an experiment to investigate the quality of
	water runoff over time
	(ii) Object manipulation
	`as.alldiffs`
Forms an `alldiffs.object` from the supplied
predictions, along with those statistics, associated with the
predictions and their pairwise differnces, that have been supplied.	`asrtests`
Pseudonym for `as.asrtests`.	`as.asrtests`
Forms an `asrtests.object` that stores (i) a fitted asreml object,
(ii) a pseudo-anova table for the fixed terms and
(iii) a history of changes and hypthesis testing
used in obtaining the model.	`as.predictions.frame`
Forms a `predictions.frame` from a data.frame, ensuring that
the correct columns are present.	`facCombine.alldiffs`
Combines several factors into one in the components of
an `alldiffs.object`.	`facRecast.alldiffs`
Reorders and/or revises the factor levels using the order of old levels
in `levels.order` and the new labels for the levels given in `newlabels`.	`facRename.alldiffs`
Renames `factor`s in the `prediction` component of
an `alldiffs.object`.	`getFormulae.asreml`
Gets the formulae from an asreml object.	`is.alldiffs`
A single-line function that tests whether an object is
of class alldiffs.	`is.asrtests`
A single-line function that tests whether an object is
of class asrtests.	`is.predictions.frame`
A single-line function that tests whether an object is
of classes `predictions.frame` and `data.frame`.	`print.alldiffs`
Prints the values in an `alldiffs.object` in a nice format.	`print.asrtests`
Prints the values in an `asrtests.object`.	`print.LSDdata`
Prints the components of a list containing data on the
LSDs for all pairwise differences of predictions.	`print.predictions.frame`
Prints the values in a `predictions.frame`, with or without title and heading.	`print.test.summary`
Prints a data.frame containing a test.summary.	`print.wald.tab`
Prints a data.frame containing a Wald or pseudoanova table.	`printFormulae.asreml`
Prints the formulae from an asreml object.	`sort.alldiffs`
Sorts the components of an `alldiffs.object` according to
the predicted values associated with a factor.	`subset.alldiffs`
Subsets the components in an `alldiffs.object` according
to the supplied condition.	`subset.list`
Forms a `list` that contains a subset of the components of
the supplied `list`.	`validAlldiffs`
Checks that an object is a valid `alldiffs.object`.	`validAsrtests`
Checks that an object is a valid `asrtests.object`.	`validPredictionsFrame`
Checks that an object is a valid `predictions.frame`.
(iii) Model modification
`addSpatialModel.asrtests`	Adds, to a supplied model, a spatial model that accounts for
	local spatial variation.
`addSpatialModelOnIC.asrtests`	Uses information criteria to decide whether to add a spatial
	model to account for local spatial variation.
`changeTerms.asrtests`	Adds and drops the specified sets of terms from one
	or both of the fixed or random model and/or replaces the
	residual (rcov) model with a new model.
`iterate.asrtests`	Subject the fitted asreml.obj stored in an asrtests.object
	to further iterations of the fitting process.
`newfit.asreml`	Refits an asreml model with modified model formula
	using either a call to 'update.asreml' or a direct
	call to 'asreml'.
`reparamSigDevn.asrtests`	Reparamterizes each random (deviations) term
	involving 'devn.fac' to a fixed term and ensures
	that the same term, with 'trend.num' replacing
	'devn.fac', is included if any other term with
	'trend.num' is included in 'terms'.
`rmboundary.asrtests`	Removes any boundary or singular variance components
	from the fit stored in 'asreml.obj' and records their
	removal in an `asrtests.object`.
`setvarianceterms.call`	Allows the setting of bounds and initial values
	for terms in the 'random' and 'residual' arguments of an
	'asreml' call.
	(iv) Model selection
	`addto.test.summary`
Adds a row to a `test.summary` `data.frame`.	`changeModelOnIC.asrtests`
Uses information criteria to decide whether to change an
already fitted model.	`chooseModel.asrtests`
Determines and records the set of significant terms using an
`asrtests.object`, taking into account the hierarchy
or marginality relations of the terms..	`chooseModel.data.frame`
Determines the set of significant terms from results stored
in a `data.frame`, taking into account the marginality
relations of terms and recording the tests used in a
`data.frame`.	`chooseSpatialModelOnIC.asrtests`
Uses information criteria to choose the best fitting
spatial model for accounting for local spatial variation.	`getTestPvalue.asrtests`
Gets the p-value for a test recorded in the test.summary
data.frame of an `asrtests.object`.	`infoCriteria.asreml`
Computes AIC and BIC for models.	`infoCriteria.list`
Computes AIC and BIC for models.	`recalcWaldTab.asrtests`
Recalculates the denDF, F.inc and P values for a table
of Wald test statistics obtained using 'wald.asreml'.	`REMLRT.asreml`
Performs a REML ratio test.	`bootREMLRT.asreml`
Performs a REML ratio test using the parametric
bootstrap.	`testranfix.asrtests`
Tests for a single fixed or random term in model
fitted using 'asreml' and records the result in an
`asrtests.object`.	`testresidual.asrtests`
Fits a new residual formula using 'asreml', tests
whether the change is significant and records the
result in an `asrtests.object`.	`testswapran.asrtests`
Tests, using a REMLRT, the significance of the difference
between the current random model and one in which oldterms
are dropped and newterms are added. The result is recorded
in an `asrtests.object`.
(v) Model diagnostics and simulation
`plotVariofaces`	Plots empirical variogram faces, including envelopes,
	from supplied residuals as described by Stefanova, Smith
	& Cullis (2009).
`variofaces.asreml`	Calculates and plots empirical variogram faces, including
	envelopes, as described by Stefanova, Smith & Cullis (2009).
`estimateV.asreml`	Forms the estimated variance, random or residual matrix for
	the observations from the variance parameter estimates.
`simulate.asreml`	Produce sets of simulated data from a multivariate normal
	distribtion and save quantites related to the simulated data.
	(vi) Prediction production and presentation
	`addBacktransforms.alldiffs`
Adds or recalculates the backtransforms component of an
`alldiffs.object`.	`allDifferences.data.frame`
Using supplied predictions and standard errors of pairwise
differences or the variance matrix of predictions, forms
all pairwise differences between the set of predictions, and
p-values for the differences.	`exploreLSDs`
Explores the computed LSD values for pairwise differences
between predictions.	`linTransform.alldiffs`
Calculates a linear transformation of the
predictions stored in an `alldiffs.object`.	`pairdiffsTransform.alldiffs`
Calculates the differences between nominated pairs of
predictions stored in an `alldiffs.object`	`pickLSDstatistics`
Pick LSDstatistics whose values minimize the number of
errors in pairwise comparisons of predictions.	`plotLSDerrors.data.frame`
Plots a map of the supplied errors that occur in using the
computedLSD values for pairwise differences between
predictions.	`plotLSDerrors.alldiffs`
Plots a map of the errors that occur in using the computed
LSD values for pairwise differences between predictions.	`plotLSDs.data.frame`
Plots a heat map of computed LSD values for pairwise
differences between predictions.	`plotLSDs.alldiffs`
Plots a heat map of computed LSD values for pairwise
differences between predictions.	`plotPredictions.data.frame`
Plots the predictions for a term, possibly with
error bars.	`plotPvalues.alldiffs`
Plots the p-values in the p.differences components
of an `alldiffs.object` as a heat map.	`plotPvalues.data.frame`
Plots the p-values in data.frame as a heat map.	`predictPlus.asreml`
Forms the predictions and associated statistics for
a term, using an asreml object and a wald.tab and
taking into account that a numeric vector
and a factor having parallel values may occur in the
model. It stores the results in an object of class
'alldifffs' and may print the results. It can be
when there are not parallel values.	`predictPresent.asreml`
Forms the predictions for each of one or more terms
and presents them in tables and/or graphs.	`ratioTransform.alldiffs`
Calculates the ratios of noinated pairs of predictions
stored in an `alldiffs.object`.	`recalcLSD.alldiffs`
Adds or recalculates the `LSD.frame` that is a
component of an `alldiffs.object`.	`redoErrorIntervals.alldiffs`
Adds or replaces the error intervals stored in the
prediction component of an `alldiffs.object`.	`renewClassify.alldiffs`
Renews the components in an `alldiffs.object`
according to a new classify.	`sort.alldiffs`
Sorts the components in an `alldiffs.object`
according to the predicted values associated with a factor.	`subset.alldiffs`
Subsets the components in an `alldiffs.object` according
to the supplied condition.	`sort.predictions.frame`
Sorts a `predictions.frame` according to the
predicted values. associated with a factor.	(vii) Response transformation
	`angular`
Applies the angular transformation to proportions.	`angular.mod`
Applies the modified angular transformation to a
vector of counts.	`powerTransform`
Performs a combination of a linear and a power
transformation on a variable. The transformed
variable is stored in the 'data.frame data'.
(viii) Miscellaneous
`getASRemlVersionLoaded`	Finds the version of asreml that is loaded and
	returns the initial characters in version.
`loadASRemlVersion`	Ensures that a specific version of asreml is loaded.
`num.recode`	Recodes the unique values of a vector using the values
	in a new vector.
`permute.square`	Permutes the rows and columns of a square matrix.
`permute.to.zero.lowertri`	Permutes a square matrix until all the lower
	triangular elements are zero.

The functions whose names end in 'alldiffs" utilize an alldiffs.object that stores: (i) a predictions.frame, being a data frame containing predicted values, variables indexing them and their standard errores and estimability status; the lower and upper limits of error intervals will be included when these are requested, (ii) optionally, square matrices containing all pairwise differences, the standard errors and p-values of the differences, and a data.frame containing LSD values and their summary statistics, (iii) optionally, the variance matrix of the predictions, and (iv) if the response was trandformed for analysis, a data frame with backtransforms of the predicted values.

The functions whose names end in 'asrtests', which are most of the model functions, utilize an asrtests.object that stores: (i) the currently fitted model in asreml.obj, (ii) the table of test statistics for the fixed effects in wald.tab, and (iii) a data frame that contains a history of the changes made to the model in test.summary.

Author

tools:::Rd_package_author("asremlPlus")

Maintainer: tools:::Rd_package_maintainer("asremlPlus")

References

Butler, D. G., Cullis, B. R., Gilmour, A. R., Gogel, B. J. and Thompson, R. (2018). ASReml-R Reference Manual Version 4. VSN International Ltd, https://asreml.kb.vsni.co.uk/.

Stefanova, K. T., Smith, A. B. & Cullis, B. R. (2009) Enhanced diagnostics for the spatial analysis of field trials. Journal of Agricultural, Biological, and Environmental Statistics, 14, 392--410.

Examples

Run this code

if (FALSE) {
## Analyse wheat dat using asreml and asremlPlus (see also the Wheat Vignette)
## Set up for analysis
library(dae)
library(asreml)
library(asremlPlus)
## use ?Wheat.dat for data set details
data(Wheat.dat)

# Fit initial model
current.asr <- asreml(yield ~ Rep + WithinColPairs + Variety, 
                      random = ~ Row + Column + units,
                      residual = ~ ar1(Row):ar1(Column), 
                      data=Wheat.dat)
summary(current.asr)

# Intialize a testing sequence by loading the current fit into an asrtests object
current.asrt <- as.asrtests(current.asr, NULL, NULL)

# Check for and remove any boundary terms
current.asrt <- rmboundary(current.asrt)

#Unbind Rep, Row and Column components and reload into an asrtests object
current.asr <- setvarianceterms(current.asr$call, 
                                terms = c("Rep", "Rep:Row", "Rep:Column"), 
                                bounds = "U")
current.asrt <- as.asrtests(current.asr, NULL, NULL)
current.asrt <- rmboundary(current.asrt)
summary(current.asrt$asreml.obj)$varcomp
print(current.asrt, which = "testsummary")
print(current.asrt, which = "pseudoanova")

# Check term for within Column pairs (a post hoc covariate)
current.asrt <- testranfix(current.asrt, "WithinColPairs", drop.fix.ns=TRUE)
  
# Test nugget term
current.asrt <- testranfix(current.asrt, "units", positive=TRUE)
  
# Test Row autocorrelation
current.asrt <- testresidual(current.asrt, "~ Row:ar1(Column)", 
                             label="Row autocorrelation", simpler=TRUE)
  
# Test Col autocorrelation (depends on whether Row autocorrelation retained)
(p <- getTestPvalue(current.asrt, label = "Row autocorrelation"))
{ if (p <= 0.05)
    current.asrt <- testresidual(current.asrt, "~ ar1(Row):Column", 
                                 label="Col autocorrelation", 
                                 simpler=TRUE, update=FALSE)
  else
    current.asrt <- testresidual(current.asrt, "~ Row:Column", 
                                 label="Col autocorrelation", 
                                 simpler=TRUE, update=FALSE)
}

# Output the results
print(current.asrt, which = "test")
info <- infoCriteria(current.asrt$asreml.obj)
summary(current.asrt$asreml.obj)$varcomp

# Get current fitted asreml object and update to include standardized residuals
current.asr <- current.asrt$asreml.obj
current.asr <- update(current.asr, aom=TRUE)
Wheat.dat$res <- residuals(current.asr, type = "stdCond")
Wheat.dat$fit <- fitted(current.asr)

#### Do diagnostic checking  
# Do residuals-versus-fitted values plot
with(Wheat.dat, plot(fit, res))
  
#Produce variogram and variogram faces plot (Stefanaova et al, 2009)
plot.varioGram(varioGram(current.asr))
faces <- variofaces(current.asr, V=NULL, units="addtores", 
                    maxiter=50, update = FALSE)

#Get Variety predictions, sorted in increasing order for the predicted values, 
#and all pairwise prediction differences and p-values
Var.diffs <- predictPlus(classify = "Variety", 
                         asreml.obj=current.asr, 
                         error.intervals="halfLeast",
                         wald.tab=current.asrt$wald.tab, 
                         sortFactor = "Variety",
                         tables = "predictions")
print(Var.diffs, which = c("differences", "p.differences"))

# Plot the Variety predictions, with halfLSD intervals, and the p-values
plotPredictions(Var.diffs$predictions, 
                classify = "Variety", y = "predicted.value", 
                error.intervals = "half")
plotPvalues(Var.diffs)
}