asremlPlus (version 4.1-26)

asremlPlus-package: asremlPlus

Description

asremlPlus

Version: NA

Date: NA

Index

(i) Data
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.	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.predictions.frame
Prints the values in a predictions.frame, with or without title and heading.	print.wald.tab
Prints a Wald or pseudoanova table.	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.	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
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.
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 testing
	chooseModel.asrtests
Determines the set of significant terms taking into
account hierarchy or marginality relations and records
the tests performed in an asrtests.object.	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 a model.	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.	linTransform.alldiffs
Calculates a linear transformation of the
predictions stored in an alldiffs.object.	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.	recalcLSD.alldiffs
Adds or recalculates the LSD 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.
(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.	(i) Data

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 summary of the LSD values, (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.

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, http://asreml.org.

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.

See Also

asreml

Examples

# NOT RUN {
## 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)
# }