reparamSigDevn.asrtests {asremlPlus}  R Documentation 
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
.This function 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
. It also
ansures that any term with spl{trend.num}
replacing
devn.fac
in a term being reparameterized is removed from the model.
## S3 method for class 'asrtests' reparamSigDevn(asrtests.obj,terms = NULL, trend.num = NULL, devn.fac = NULL, allow.unconverged = TRUE, checkboundaryonly = FALSE, denDF = "numeric", IClikelihood = "none", trace = FALSE, update = TRUE, set.terms = NULL, ignore.suffices = TRUE, bounds = "P", initial.values = NA,...)
asrtests.obj 
an 
terms 
A character string vector giving the terms that are to be reparameterized. 
trend.num 
A character string giving the name of the numeric covariate that
corresponds to 
devn.fac 
A character string giving the name of the factor that corresponds to

allow.unconverged 
A 
checkboundaryonly 
If 
denDF 
Specifies the method to use in computing approximate denominator
degrees of freedom when 
IClikelihood 
A 
trace 
If TRUE then partial iteration details are displayed when ASRemlR functions are invoked; if FALSE then no output is displayed. 
update 
If 
set.terms 
A character vector specifying the terms that are to have bounds and/or initial values set prior to fitting. 
ignore.suffices 
A logical vector specifying whether the suffices of the

bounds 
A 
initial.values 
A character vector specifying the initial values for
the terms specified in 
... 
further arguments passed to 
An asrtests.object
containing the components (i) asreml.obj
,
(ii) wald.tab
, and (iii) test.summary
.
Chris Brien
Kenward, M. G., & Roger, J. H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics, 53, 983997.
as.asrtests
, changeTerms.asrtests
,
testranfix.asrtests
, testresidual.asrtests
,
newfit.asreml
, chooseModel.asrtests
## Not run: data(WaterRunoff.dat) asreml.options(keep.order = TRUE) #required for asremlR4 only current.asr < asreml(fixed = log.Turbidity ~ Benches + Sources + Type + Species + Sources:Type + Sources:Species + Sources:Species:xDay + Sources:Species:Date, data = WaterRunoff.dat, keep.order = TRUE) current.asrt < as.asrtests(current.asr, NULL, NULL) #Examine terms that describe just the interactions of Date and the treatment factors terms.treat < c("Sources", "Type", "Species", "Sources:Type", "Sources:Species") date.terms < sapply(terms.treat, FUN=function(term){paste("Date:",term,sep="")}, simplify=TRUE) date.terms < c("Date", date.terms) date.terms < unname(date.terms) treat.marginality < matrix(c(1,0,0,0,0,0, 1,1,0,0,0,0, 1,0,1,0,0,0, 1,0,1,1,0,0, 1,1,1,0,1,0, 1,1,1,1,1,1), nrow=6) rownames(treat.marginality) < date.terms colnames(treat.marginality) < date.terms choose < chooseModel(current.asrt, treat.marginality, denDF="algebraic") current.asrt < choose$asrtests.obj current.asr < current.asrt$asreml.obj sig.date.terms < choose$sig.terms #Remove all Date terms left in the fixed model terms < "(Date/(Sources * (Type + Species)))" current.asrt < changeTerms(current.asrt, dropFixed = terms) #if there are significant date terms, reparameterize to xDays + spl(xDays) + Date if (length(sig.date.terms) != 0) { #add lin + spl + devn for each to fixed and random models trend.date.terms < sapply(sig.date.terms, FUN=function(term){sub("Date","xDay",term)}, simplify=TRUE) trend.date.terms < paste(trend.date.terms, collapse=" + ") current.asrt < changeTerms(current.asrt, addFixed=trend.date.terms) trend.date.terms < sapply(sig.date.terms, FUN=function(term){sub("Date","spl(xDay)",term)}, simplify=TRUE) trend.date.terms < c(trend.date.terms, sig.date.terms) trend.date.terms < paste(trend.date.terms, collapse=" + ") current.asrt < changeTerms(current.asrt, addRandom = trend.date.terms) current.asrt < rmboundary(current.asrt) } #Now test terms for sig date terms spl.terms < sapply(terms.treat, FUN=function(term){paste("spl(xDay):",term,sep="")}, simplify=TRUE) spl.terms < c("spl(xDay)",spl.terms) lin.terms < sapply(terms.treat, FUN=function(term){paste(term,":xDay",sep="")}, simplify=TRUE) lin.terms < c("xDay",lin.terms) systematic.terms < c(terms.treat, lin.terms, spl.terms, date.terms) systematic.terms < unname(systematic.terms) treat.marginality < matrix(c(1,0,0,0,0,0, 1,1,0,0,0,0, 1,0,1,0,0,0, 1,0,1,1,0,0, 1,1,1,1,1,0, 1,1,1,1,1,1), nrow=6) systematic.marginality < kronecker(matrix(c(1,0,0,0, 1,1,0,0, 1,1,1,0, 1,1,1,1), nrow=4), treat.marginality) systematic.marginality < systematic.marginality[1, 1] rownames(systematic.marginality) < systematic.terms colnames(systematic.marginality) < systematic.terms choose < chooseModel(current.asrt, systematic.marginality, denDF="algebraic", pos=TRUE) current.asrt < choose$asrtests.obj #Check if any deviations are significant and, for those that are, go back to #fixed dates current.asrt < reparamSigDevn(current.asrt, choose$sig.terms, trend.num = "xDay", devn.fac = "Date", denDF = "algebraic") ## End(Not run)