term using
classify and the supplied asreml object and stores
them in an alldiffs object. If x.num is
supplied, the predictions will be obtained for the values supplied
in x.pred.values and, if supplied, x.plot.values will
replace them in the alldiffs object that is returned.
If x.fac, but not x.num, is specified, predictions
will involve it and, if supplied, x.plot.values will replace
the levels of x.fac in the alldiffs object
that is returned. In order to get the correct predictions you may
need to supply additional arguments to predict through ...
e.g. present. Any aliased predictions will be removed, as
will any standard error of pairwise differences involving them. Also calculated are the approximate degrees of freedom of the
standard errors of the predictions. If the deominator degrees of
freedom for term are available in wald.tab, they are
used. Otherwise the residual degrees of freedom or the maximum of
the denominator degrees in wald.tab, excluding the
Intercept, are used. Which is used depends on the setting of
dDF.na. These degrees of freedom are used for the
t-distribution on which p-values and confidence intervals are
based. It is stored as an attribute to the alldiffs object.
The degrees of freedom are also used in valculating the minimum,
mean and maximum LSD fro comparing pairs of predictions, which are
also stored in the alldiffs object.
If pairwise = TRUE, all pairwise differences between the
predictions, their standard errors, p-values and LSD
statistics are computed using predictiondiffs.asreml.
This adds them to the alldiffs object as additional
list components named differences, sed,
p.differences and LSD.
If a transformation has been applied (any one of
transform.power is not one, scale is not one and
offset is nonzero), the back-transforms of the predicted
values and their lower and upper confidence intervals are added
to a data.frame that is consistent with an object of class
asremlPredict, such as is stored in the pvals
component of the prediction component of the value produced
by predict.asreml. This data.frame is added to the
alldiffs object as a list component called
backtransforms.
The printing of the components produced is controlled by the
tables argument.
predictparallel.asreml(classify, term = NULL, asreml.obj = NULL, titles = NULL, x.num = NULL, x.fac = NULL, x.pred.values = NULL, x.plot.values = NULL, error.intervals = "Confidence", avsed.tolerance = 0.25, pairwise = TRUE, tables = "all" , levels.length = NA, transform.power = 1, offset = 0, scale = 1, inestimable.rm = TRUE, wald.tab = NULL, alpha = 0.05, dDF.na = "residual", dDF.values = NULL, trace = FALSE, ...): operator.asreml and that corresponds
to classify. It only needs to be specified when
it is different to classify.asreml object for a fitted model.list, each component of which is named for an object
name and contains a character string giving a title to use
in output (e.g. tables and graphs) for the object. Here they will
be used for table headings.x.fac, (ii)is potentially included in
terms in the fitted model, and (iii) which corresponds to the
x-axis variable. It should have the same number of unique values
as the number of levels in x.fac.x.num, (ii) is potentially included in
terms in the fitted model, and (iii) which corresponds to the
x-axis variable. It should have the same number of levels as the
number of unique values in x.num. The levels of
x.fac must be in the order in which they are to be plotted
- if they are dates, then they should be in the form
yyyymmdd which can be achieved using as.Date.x.num for which predicted values are
required.x.num are to be
plotted or x.fac is to be plotted because there is no
x.num term corresponding to the same term with x.fac."none", "StandardError",
"Confidence" and "halfLeastSignificant". The default
is for confidence limits to be used. The
"halfLeastSignificant" option results in half the
mean Least Significant Difference (LSD) being added and subtracted
to the predictions, the mean LSD being calculated using the average
of the standard errors of all pairwise differences (SEDs) between
the predictions. However, if the range of the SEDs divided by the
average of the SEDs exceeds avsed.tolerance, calculations
and plotting will revert to confidence intervals. Also, half LSDs
cannot be used for backtransformed values and so confidence
intervals will be used instead.predictions and their standard errors and p-values are to be
computed and stored. If tables is equal to
"differences" or "all" or error.intervals is
equal to "halfLeastSignificant", they will be stored
irrespective of the value of pairwise.character vector containing a combination of
none,
predictions, backtransforms, differences,
p.differences, sed, LSD and all.
These nominate which components of the alldiffs
object to print.transform.power, unless it equals 0 in
which case the exponential of the predictions is taken.logical indicating whether rows for predictions
that are not estimable are to be removed from the components of
thealldiffs object.data.frame containing the pseudo-anova table for the
fixed terms produced by a call to wald.asreml. The main
use of it here is in determinining the degrees of freedom of the standard errors
of the predictions. denominator degrees of freedom when
p-values or confidence intervals are to be calculated.NA. Consistent with when no denDF are available, the
default is "residual" and so the residual degrees of
freedom from asreml.obj$nedf are used.
If dDF.na = "none", no subtitute denominator degrees of
freedom are employed; if dDF.na = "maximum", the maximum
of those denDF that are available, excluding that for the
Intercept, is used; if all denDF are NA, asreml.obj$nedf is used. If
dDF.na = "supplied", a vector of values for the
denominator degrees of freedom is to be supplied in dDF.values.
Any other setting is ignored and a warning message produced.
Generally, substituting these degrees of freedom is
anticonservative in that it is likely that the degrees of freedom
used will be too large.vector of values to be used when
dDF.na = "supplied". Its values will be used when
denDF in a test for a fixed effect is NA.
This vector must be the same length as the number of
fixed terms, including (Intercept) whose value could be
NA.predict.asreml.alldiffs object with predictions and their standard
errors and, depending on the settings of the arguments, all pairwise
differences between predictions, their standard errors and p-values
and LSD statistics. If power.transform is not one, it will
contain a data.frame with the backtransformed predictions. If
error.intervals is not "none", then the
predictions component and, if present, the
backtransforms component will contain columns for the lower
and upper values of the limits for the interval. The names of these
columns will consist of three parts separated by full stops:
1) the first part will be lower or upper;
2) the second part will be one of Confidence,
StandardError or halfLeastSignificant;
3) the third component will be limits.The name of the response, the term, the classify
and tdf, as well as the degrees of freedom
of the standard error, will be set as attributes to the object.alldiffs, print.alldiffs, predictiondiffs.asreml,
pred.present.asreml, as.Date,
predictionplot.asreml, pred.present.asreml, predict.asreml