Extracts the REML log likelihood for two asreml objects
and forms the observed REML ratio statistic. It assumes that the second
asreml object is the result of fitting a model that is a reduced
version of the model for the first object and is considered to the null model.
Using the mean and V, nboot bootstrap samples of simulated
response values are generated in parallel; that is, ncores cores are used
and each is used to generate and analyse a sample. The full and reduced models
are fitted to the data and if either analysis fails to converge another sample
is generated and analysed using the current core, with a maximum of
max.retries attempts to obtain a sample that converges for both analysis.
Thus the maximum number of data sets that will be generated is
nboot * max.retries. If a bootstrap sample converges for both analyses,
the REML ratio test statistic is formed for it. The p-value is then calculated
as \((k + 1) / (b + 1)\) where \(k\) is the number
of simulated ratio test statistics greater than the observed test statistic and
\(s\) is the number of bootstrap samples that were returned.
The function checks that the models do not differ in either their fixed
or sparse models. It also check the difference in the number of variance
parameters between the two fits to the models, taking into account the
bound.exclusions.
# S3 method for asreml
bootREMLRT(h0.asreml.obj, h1.asreml.obj,
nboot = 100, max.retries = 5, seed = NULL,
means=NULL, V = NULL, extra.matrix = NULL, ignore.terms = NULL,
fixed.spline.terms = NULL,
bound.exclusions = c("F","B","S","C"),
tolerance = 1E-10, update = TRUE, trace = FALSE,
ncores = 2, ...)A list with the following components:
REMLRT: the observed REML ratio statistic.
p: the bootstrap p-value for the observed test statistic.
DF: the calculated difference in DF for the variance parameters in the two models.
totalunconverged: the total number of unconverged analyses over the simulations.
REMLRT.sim: a numeric containing the values of the ratio statistics
for the simulated data. It has an attribute called na.action that
can be retrieved using attr(REMLRT.sim, which = "na.action"); it
contains a list of the simulation numbers that were abandoned because
max.retries failed to converge for both models.
nunconverged: the number of unconverged analyses for each bootstrap sample, the
maximum being max.retries.
asreml object containing the fit under the model for the
null hypothesis.
asreml object containing the fit under the model for the
alternative hypothesis.
The number of bootstrap samples to be generated.
The maximum number of attempts to generate a sample whose analyses converge for both models.
A single value, interpreted as an integer, that specifies the
starting value of the random number generator. The "L'Ecuyer-CMRG" random
generator is used and nextRNGStream is used to seed each core from the
original seed.
The vector of means to be used in generating simulated bootstrap
samples. If it is NULL, the fitted values based on object
are used. It must be the same length as the response variable for
object.
The fitted variance matrix, i.e. having the pattern and values that
conform to the model fit stored in the supplied object. If it is
NULL, estimateV.asreml is used to estimate the
variance matrix for the observations from the variance parameter estimates
from the reduced.asreml.obj.
A matrix of order equal to the number of observations that is to
be added to the variance matrix, the latter based
on the information in asreml.obj. It is assumed that the sigma-parameterized
values of the variance parameter estimates, such as is given in the varcomp
component of summary.asreml, have been used in calculating
extra.matrix; the values in the vparameters component of
G.param and R.param may be either gamma- or sigma-parameterized.
The argument extra.matrix can be used in conjunction with
ignore.terms as a workaround to include components of the variance matrix
for variance functions that have not been implemented in estimateV.
A character giving terms from either the random or
residual models that are to be ignored in that their contributions to
the variance is not to be included in the estimated matrix. The term names are those
given in the vparameters component of the asreml object or the
varcomp component produced by summary.asreml, but only up to the
first exclamation mark (!). This can be used
in conjunction with estimateV.asreml as a workaround to include components
of the variance matrix for variance functions that have not been implemented
in estimateV.
A character vector giving one or more spline terms in the
random model that are regarded as fixed and so are to be ignored
because they are not regarded as contributing to the variance. The term
names are those given in the vparameters component of the
asreml object or the varcomp component produced by
summary.asreml, but only up to the first exclamation mark
(!).
A character specifying one or more bound codes that
will result in a variance parameter in the random model being
excluded from contributing to the variance. If set to NULL
then none will be excluded.
The value such that eigenvalues less than it are considered to be zero.
If TRUE then the arguments R.param
and G.param are set to those in the asreml object supplied
in object so that the values from the original model are used as
starting values. If FALSE then asreml calls are
evaluated, the only changes from the previous call being that
(i) the model is fitted to simulated data and (ii) modifications
specified via ... are mode, except that changes cannot be made to
any of the models.
If TRUE then partial iteration details are displayed when ASReml-R
functions are invoked; if FALSE then no output is displayed.
A numeric specifying the number of cores to use in doing the
simulations. In choosing a value for ncores, it is necessary to
take into account other processes that are using parallel processing at
the same time.
Other arguments that are passed down to the function asreml. Changes to the models are not allowed. Other changes are dangerous and generally should be avoided.
Chris Brien
REMLRT.asreml, infoCriteria.asreml, newfit.asreml,
testranfix.asrtests
if (FALSE) {
bootREMLRT(ICV.max, ICV.red, ncores = parallel::detectCores())
}Run the code above in your browser using DataLab