allDifferences.data.frame {asremlPlus}  R Documentation 
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.
Description
Uses supplied predictions and standard errors of pairwise differences,
or the variance matrix of predictions to form, in an
alldiffs.object
, for those components not already present,
(i) a table of all pairwise differences of the predictions,
(ii) the pvalue of each pairwise difference, and
(iii) the minimum, mean and maximum LSD values.
Predictions that are aliased (or nonestimable) are removed from the
predictions
component of the alldiffs.object
and
standard errors of differences involving them are removed from the sed
component.
If necessary, the order of the columns of the variables in the predictions
component are changed to be the initial columns of the predictions.frame
and to match their order in the classify
. Also, the rows of predictions
component are ordered so that they are in standard order for the variables in the
classify
. That is, the values of the last variable change with every row,
those of the secondlast variable only change after all the values of the last
variable have been traversed; in general, the values of a variable are the same for
all the combinations of the values to the variables to its right in the
classify
. The sortFactor
or sortOrder
arguments can be used
to order of the values for the classify
variables, which is achieved using
sort.alldiffs
.
Each pvalue is computed as the probability of a tstatistic as large as or larger
than the absolute value of the observed difference divided by its standard error. The
pvalues are stored in the p.differences
component. The degrees of freedom of
the tdistribution is the degrees of freedom stored in the tdf
attribute of
the alldiffs.object
. This tdistibrution is also used in calculating
the LSD statistics stored in the alldiffs.object
.
Usage
## S3 method for class 'data.frame'
allDifferences(predictions, classify, vcov = NULL,
differences = NULL, p.differences = NULL, sed = NULL,
LSD = NULL, meanLSD.type = "overall", LSDby = NULL,
backtransforms = NULL,
response = NULL, response.title = NULL,
term = NULL, tdf = NULL,
x.num = NULL, x.fac = NULL,
level.length = NA,
pairwise = TRUE, alpha = 0.05,
transform.power = 1, offset = 0, scale = 1,
inestimable.rm = TRUE,
sortFactor = NULL, sortWithinVals = NULL,
sortOrder = NULL, decreasing = FALSE, ...)
Arguments
predictions 
A predictions.frame , or a data.frame , beginning
with the variables classifying the predictions and also containing columns
named predicted.value , standard.error and est.status ;
each row contains a single predicted value. It may also contain columns
for the lower and upper limits of error intervals for the predictions.
Note that the names standard.error and
est.status have been changed to std.error and status
in the pvals component produced by asremlR4 ; if the new names
are in the data.frame supplied to predictions , they will be
returned to the previous names.

classify 
A character string giving the variables that define the margins
of the multiway table that has been predicted. Multiway tables are
specified by forming an interaction type term from the
classifying variables, that is, separating the variable names
with the : operator.

vcov 
A matrix containing the variance matrix of the predictions; it is used in
computing the variance of linear transformations of the predictions.

differences 
A matrix containing all pairwise differences between
the predictions; it should have the same number of rows and columns as there are
rows in predictions .

p.differences 
A matrix containing pvalues for all pairwise differences
between the predictions; each pvalue is computed as the probability of a tstatistic
as large as or larger than the observed difference divided by its standard error.
The degrees of freedom of the t distribution for computing it are computed as
the denominator degrees of freedom of the F value for the fixed term, if available;
otherwise, the degrees of freedom stored in the attribute tdf are used;
the matrix should be of the same size as that for differences .

sed 
A matrix containing the standard errors of all pairwise differences
between the predictions; they are used in computing the pvalues.

LSD 
A data.frame containing the mean, minimum and maximum LSD for determining
the significance of pairwise differences, the mean LSD being calculated using
the square root of the mean of the variances of pairwise differences.
If factor.combination was specified for meanLSD.type when the
LSDs were being calculated, then LSD contains an LSD for each
factor.combination of the factors specified by LSDby .
Each LSD is calculated from the square root of the mean of the variances for all
pairwise differences for each factor combination, unless there is only one predicted
value for each factor.combination , when it is based on the standard error of
the prediction multiplied by the square root of two.
If LSD is not NULL then the overall mean LSD will be added as
an attribute named meanLSD of the alldiffs.object , as will
the values of meanLSD.type and LSDby . The LSD for a single prediction
assumes that any predictions to be compared are independent; this is not the case if
residual errors are correlated.

meanLSD.type 
A character string determining whether the mean LSD stored is
(i) the overall mean, based on the square root of the mean of the variances of
all pairwise variances, (ii) the mean for each factor.combination of the
factors specified by LSDby , which is based on the square root of
the mean of the variances for all pairwise differences for each factor combination, unless
there is only one prediction for a factor.combination , when notional LSDs are
calculated that are based on the standard error of the prediction multiplied by the square
root of two, or
(iii) the per.prediction mean, based, for each prediction,
on the square root of the mean of the variances for all pairwise differences involving
that prediction. If LSD is not NULL then meanLSD.type will be added
as an attribute of the alldiffs.object .

LSDby 
A character (vector) of variables names, being the names of the
factors or numerics in the classify for each
combination of which a mean LSD, minLSD and max LSD is stored in the LSD
component of the alldiffs.object when meanLSD.type is
factor.combinatons .

backtransforms 
A data.frame containing the backtransformed values of the predicted
values that is consistent with the predictions component, except
that the column named predicted.value is replaced by one called
backtransformed.predictions . Any error.interval values will also
be the backtransformed values. Each row contains a single predicted value.

response 
A character specifying the response variable for the
predictions. It is stored as an attribute to the alldiffs.object .

response.title 
A character specifying the title for the response variable
for the predictions. It is stored as an attribute to the alldiffs.object .

term 
A character string giving the variables that define the term
that was fitted using asreml and that corresponds
to classify . It is often the same as classify .
It is stored as an attribute to the alldiffs.object .

tdf 
an integer specifying the degrees of freedom of the standard error. It is used as
the degrees of freedom for the tdistribution on which pvalues and confidence
intervals are based.
It is stored as an attribute to the alldiffs.object .

x.num 
A character string giving the name of the numeric covariate that
corresponds to x.fac , is potentially included in terms in the
fitted model and which corresponds to the xaxis variable. It should
have the same number of unique values as the number of levels in
x.fac .

x.fac 
A character string giving the name of the factor that corresponds to
x.num , is potentially included in terms in the fitted model and
which corresponds to the xaxis 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 . However, the levels
can be nonnumeric in nature, provided that x.num is also set.

level.length 
The maximum number of characters from the the levels of
factors to use in the row and column labels of the tables of
pairwise differences and their pvalues and standard errors.

pairwise 
A logical indicating whether all pairwise differences of the
predictions and their standard errors and pvalues are to be
computed and stored. If FALSE , the components differences
and p.differences will be NULL in the returned
alldiffs.object .

alpha 
The significance level for an LSD to compare a pair of predictions.

transform.power 
A numeric specifying the power of a transformation, if
one has been applied to the response variable. Unless it is equal
to 1, the default, backtransforms of the predictions will be
obtained and presented in tables or graphs as appropriate.
The backtransformation raises the predictions to the power equal
to the reciprocal of transform.power , unless it equals 0 in
which case the exponential of the predictions is taken.

offset 
A numeric that has been added to each value of the
response after any scaling and before applying any power transformation.

scale 
A numeric by which each value of the response has been multiplied
before adding any offset and applying any power transformation.

inestimable.rm 
A logical indicating whether rows for predictions that
are not estimable are to be removed from the components of the
alldiffs.object .

sortFactor 
A character containing the name of the
factor that indexes the set of predicted values that determines
the sorting of each component of the the alldiffs.object by
sort.alldiffs . If NULL then sorting is not carried
out. If there is more than one variable
in the classify term then sortFactor is sorted for the
predicted values within each combination of the values of the sortWithin
variables: the classify variables, excluding the
sortFactor . There should be only one predicted value for
each unique value of sortFactor within each set defined by a
combination of the values of the sortWithin variables.

sortWithinVals 
A list with a component named for each factor and
numeric that is a classify variable for the predictions,
excluding sortFactor . Each component should contain a single
value that is a value of the variable. The combination of this set
of values will be used to define a subset of the predicted values whose
order will define the order of sortFactor to be used for all
combinations of the sortWithinVals variables. If
sortWithinVals is NULL then the first value of each
sortWithin variable in predictions component is used
to define sortWithinVals . If there is only one variable in the
classify then sortWithinVals is ignored.

sortOrder 
A character vector whose length is the same as the number
of levels for sortFactor in the predictions component of the
alldiffs.object . It specifies the desired order of the
levels in the reordered components of the alldiffs.object .
The argument sortWithinVals is ignored.
The following creates a sortOrder vector levs for factor
f based on the values in x :
levs < levels(f)[order(x)] .

decreasing 
A logical passed to order that detemines whether
the order for sorting the components of the alldiffs.object
is for increasing or decreasing magnitude of the predicted values.

... 
provision for passsing arguments to functions called internally 
not used at present.

Value
An alldiffs.object
with components
predictions
, vcov
, differences
, p.differences
sed
, and LSD
.
The name of the response
, the response.title
,
the term
, the classify
, tdf
, sortFactor
and the sortOrder
will be set as attributes to the object.
Note that the classify
in an alldiffs.object
is based on the
variables indexing the predictions, which may differ from the
classify
used to obtain the original predictions (for example,
when the alldiffs.object
s stores a linear transformation of predictions.
Also, see predictPlus.asreml
for more information.
Author(s)
Chris Brien
See Also
asremlPluspackage
, as.alldiffs
, as.predictions.frame
,
sort.alldiffs
, subset.alldiffs
,
print.alldiffs
, renewClassify.alldiffs
,
redoErrorIntervals.alldiffs
,
recalcLSD.alldiffs
, plotPredictions.data.frame
,
predictPlus.asreml
,
predictPresent.asreml
Examples
data(Oats.dat)
## Use asreml to get predictions and associated statistics
## Not run:
m1.asr < asreml(Yield ~ Nitrogen*Variety,
random=~Blocks/Wplots,
data=Oats.dat)
current.asrt < as.asrtests(m1.asr)
Var.pred < asreml::predict.asreml(m1.asr, classify="Nitrogen:Variety",
sed=TRUE)
if (getASRemlVersionLoaded(nchar = 1) == "3")
Var.pred < Var.pred$predictions
Var.preds < Var.pred$pvals
Var.sed < Var.pred$sed
Var.vcov < NULL
wald.tab < current.asrt$wald.tab
den.df < wald.tab[match("Variety", rownames(wald.tab)), "denDF"]
## End(Not run)
## Use lmerTest and emmmeans to get predictions and associated statistics
if (requireNamespace("lmerTest", quietly = TRUE) &
requireNamespace("emmeans", quietly = TRUE))
{
m1.lmer < lmerTest::lmer(Yield ~ Nitrogen*Variety + (1Blocks/Wplots),
data=Oats.dat)
Var.emm < emmeans::emmeans(m1.lmer, specs = ~ Nitrogen:Variety)
Var.preds < summary(Var.emm)
den.df < min(Var.preds$df)
## Modify Var.preds to be compatible with a predictions.frame
Var.preds < as.predictions.frame(Var.preds, predictions = "emmean",
se = "SE", interval.type = "CI",
interval.names = c("lower.CL", "upper.CL"))
Var.vcov < vcov(Var.emm)
Var.sed < NULL
}
## Use the predictions obtained with either asreml or lmerTest
if (exists("Var.preds"))
{
## Order the Varieties in decreasing order for the predictions values in the
## first N level
Var.diffs < allDifferences(predictions = Var.preds,
classify = "Nitrogen:Variety",
sed = Var.sed, vcov = Var.vcov, tdf = den.df,
sortFactor = "Variety", decreasing = TRUE)
print.alldiffs(Var.diffs, which="differences")
## Change the order of the factors in the alldiffs object and reorder components
Var.reord.diffs < allDifferences(predictions = Var.preds,
classify = "Variety:Nitrogen",
sed = Var.sed, vcov = Var.vcov, tdf = den.df)
print.alldiffs(Var.reord.diffs, which="predictions")
}
[Package
asremlPlus version 4.232
Index]