asremlPluspackage {asremlPlus}  R Documentation 
Assists in automating the selection of terms to include in mixed models when 'asreml' is used to fit the models. Also used to display, in tables and graphs, predictions obtained using any model fitting function and to explore differences between predictions. The content falls into the following natural groupings: (i) Data, (ii) Object manipulation functions, (iii) Model modification functions, (iv) Model testing functions, (v) Model diagnostics functions, (vi) Prediction production and presentation functions, (vii) Response transformation functions, and (viii) Miscellaneous functions (for further details see 'asremlPluspackage' in help). A history of the fitting of a sequence of models is kept in a data frame. Procedures are available for choosing models that conform to the hierarchy or marginality principle and for displaying predictions for significant terms in tables and graphs. The 'asreml' package provides a computationally efficient algorithm for fitting mixed models using Residual Maximum Likelihood. It is a commercial package that can be purchased from 'VSNi' <https://www.vsni.co.uk/> as 'asremlR', who will supply a zip file for local installation/updating (see <https://asreml.kb.vsni.co.uk/>). It is not needed for functions that are methods for 'alldiffs' and 'data.frame' objects. The package 'asremPlus' can also be installed from <http://chris.brien.name/rpackages/>.
Version: 4.232
Date: 20210319
(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 pseudoanova 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 . 

facRecode.alldiffs
 Recodes factor levels using values in a vector. The values in the 
vector do not have to be unique.  
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 singleline function that tests whether an object is 
of class alldiffs.  
is.asrtests
 A singleline function that tests whether an object is 
of class asrtests.  
is.predictions.frame
 A singleline 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.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.  
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.  
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  
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 . 

getTestPvalue.asrtests
 Gets the pvalue 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  
pvalues 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 pvalues in the p.differences components 
of an alldiffs.object as a heat map. 

plotPvalues.data.frame
 Plots the pvalues 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.  
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 pvalues 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
.
Chris Brien [aut, cre] (<https://orcid.org/0000000305811817>)
Maintainer: Chris Brien <chris.brien@adelaide.edu.au>
Butler, D. G., Cullis, B. R., Gilmour, A. R., Gogel, B. J. and Thompson, R. (2018). ASRemlR 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.
asreml
## 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$fit < fitted(current.asr) #### Do diagnostic checking # Do residualsversusfitted values plot with(Wheat.dat, plot(fit, res)) #Produce variogram and variogram faces plot (Stefanaova et al, 2009) plot.varioGram(varioGram(current.asr)) faces < variofaces(current.asr, V=NULL, units="addtores", maxiter=50, update = FALSE) #Get Variety predictions, sorted in increasing order for the predicted values, #and all pairwise prediction differences and pvalues Var.diffs < predictPlus(classify = "Variety", asreml.obj=current.asr, error.intervals="halfLeast", wald.tab=current.asrt$wald.tab, sortFactor = "Variety", tables = "predictions") print(Var.diffs, which = c("differences", "p.differences")) # Plot the Variety predictions, with halfLSD intervals, and the pvalues plotPredictions(Var.diffs$predictions, classify = "Variety", y = "predicted.value", error.intervals = "half") plotPvalues(Var.diffs) ## End(Not run)