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`.

Description

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.

Usage

# S3 method for asrtests
reparamSigDevn(asrtests.obj,terms = NULL, 
               trend.num = NULL, devn.fac = NULL, 
               allow.unconverged = TRUE, allow.fixedcorrelation = TRUE, 
               checkboundaryonly = FALSE, 
               denDF = "numeric", IClikelihood = "none", 
               trace = FALSE, update = TRUE, 
               set.terms = NULL, ignore.suffices = TRUE,
               bounds = "P", initial.values = NA,...)

Value

An asrtests.object containing the components (i) asreml.obj, (ii) wald.tab, and (iii) test.summary.

Arguments

asrtests.obj: an asrtests.object containing the components (i) asreml.obj, (ii) wald.tab, and (iii) test.summary.
terms: A character string vector giving the random terms that are to be reparameterized.
trend.num: A character string giving the name of the numeric covariate that corresponds to devn.fac and is potentially included in terms in the fitted model.
devn.fac: A character string giving the name of the factor that corresponds to trend.num and is included in terms in the fitted model. The name must match those in the vparameters component of the asreml.obj component in the asrtests.obj.
allow.unconverged: A logical indicating whether to accept a new model even when it does not converge. Initially all changes are made with allow.unconverged set to TRUE. If allow.unconverged has been set to FALSE in the call and the final fit does not converge, an attempt is made to achieve convergence by removing any boundary terms. If this is unsuccessful, the supplied asrtests.obj is returned.
allow.fixedcorrelation: A logical indicating whether to accept a new model even when it contains correlations in the model whose values have been designated as fixed, bound or singular. If FALSE and the new model contains correlations whose values have not been able to be estimated, the supplied asrtests.obj is returned. The fit in the asreml.obj component of the supplied asrtests.obj will also be tested and a warning issued if both fixed correlations are found in it and allow.fixedcorrelation is FALSE.
checkboundaryonly: If TRUE then boundary and singular terms are not removed by rmboundary.asrtests; a warning is issued instead.
denDF: Specifies the method to use in computing approximate denominator degrees of freedom when wald.asreml is called. Can be none to suppress the computations, numeric for numerical methods, algebraic for algebraic methods or default, the default, to autommatically choose numeric or algebraic computations depending on problem size. The denominator degrees of freedom are calculated according to Kenward and Roger (1997) for fixed terms in the dense part of the model.
IClikelihood: A character that controls both the occurrence and the type of likelihood for information criterion in the test.summary of the new asrtests.object. If none, none are included. Otherwise, if REML, then the AIC and BIC based on the Restricted Maximum Likelihood are included; if full, then the AIC and BIC based on the full likelihood, evaluated using REML estimates, are included. (See also infoCriteria.asreml.)
trace: If TRUE then partial iteration details are displayed when ASReml-R functions are invoked; if FALSE then no output is displayed.
update: If TRUE, and set.terms is NULL, then newfit.asreml is called to fit the model to be tested, using the values of the variance parameters stored in the asreml.object, that is stored in asrtests.obj, as starting values. If FALSE or set.terms is not NULL, then newfit.asreml will not use the stored variance parameter values as starting values when fitting the new model, the only modifications being (i) the models are updated and (ii) those specified via ....
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 asreml-assigned names of the variance terms (i.e. the information to the right of an "!", other than "R!") is to be ignored in matching elements of terms. If TRUE for an element of terms, the suffices are stripped from the asreml-assigned names. If FALSE for an element of terms, the element must exactly match an asreml-assigned name for a variance term. This vector must be of length one or the same length as terms. If it is of length one then the same action is applied to the asreml-assigned suffices for all the terms in terms.
bounds: A character vector specifying the bounds to be applied to the terms specified in set.terms. This vector must be of length one or the same length as set.terms. If it is of length one then the same constraint is applied to all the terms in set.terms. If any of the bounds are equal to NA then they are left unchanged for those terms.
initial.values: A character vector specifying the initial values for the terms specified in terms. This vector must be of length one or the same length as terms. If it is of length one then the same initial value is applied to all the terms in terms. If any of the initial.values are equal to NA then they are left unchanged for those terms.
...: further arguments passed to asreml via changeTerms.asrtests and as.asrtests.

Author

Chris Brien

References

Kenward, M. G., & Roger, J. H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics, 53, 983-997.

Examples

Run this code

if (FALSE) {
  data(WaterRunoff.dat)
  asreml.options(keep.order = TRUE) #required for asreml-R4 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")
}

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