glme
fit.
The semi-variogram values are calculated for pairs of residuals
within the same group.
If
collapse
is different from
none
,
the individual semi-variogram values are collapsed using either
a robust estimator (
robust=TRUE
)
defined in Cressie (1993),
or the average of the values within the same distance interval.
The semi-variogram is useful for modeling the error term correlation structure.
Variogram(object, distance, form, resType, data, na.action, maxDist, length.out, collapse, nint, breaks, robust, metric)
glme
, representing
a fitted generalized linear mixed-effects model.
form
,
data
, and
metric
,
unless
object
includes
a
corSpatial
element,
in which case the associated covariate
(obtained with the
getCovariate
method) is used.
|
operator in
form
).
Default is
~1
, implying that the observation
order within the groups is used to obtain the distances.
"response"
, the "raw" residuals
(observed - fitted) are used;
else, if
"pearson"
,
the standardized residuals
(raw residuals divided by the corresponding standard errors)
are used; else, if
"normalized"
,
the normalized residuals (standardized residuals pre-multiplied by the
inverse square-root factor of the estimated error correlation matrix)
are used; else, if
"deviance"
,
the deviance residuals are used.
Partial matching of arguments is used,
so only the first character needs to be provided.
Defaults to
"pearson"
.
form
.
By default, the same data used to fit
object
is used.
NA
s.
The default action (
na.fail
) causes
an error message to be printed and the function to terminate,
if there are any incomplete observations.
object
includes
a
corSpatial
element,
its semi-variogram values are calculated
and this argument is used as the
length.out
argument to the corresponding
Variogram
method.
Defaults to
50
.
"quantiles"
,
the semi-variogram values are split according to quantiles of the
distance distribution,
with equal number of observations per group,
with possibly varying distance interval lengths.
Else, if
"fixed"
, the semi-variogram values
are divided according to distance intervals of equal lengths,
with possibly different number of observations per interval.
Else, if
"none"
, no collapsing is used
and the individual semi-variogram values are returned.
Defaults to
"quantiles"
.
20
.
TRUE
the robust estimator is used.
Defaults to
FALSE
.
collapse
is ignored.
"euclidean"
for the root sum-of-squares of distances;
"maximum"
for the maximum difference;
and
"manhattan"
for the sum of the absolute differences.
Partial matching of arguments is used,
so only the first three characters need to be provided.
Defaults to
"euclidean"
.
variog
and
dist
representing, respectively,
the semi-variogram values and the corresponding distances.
If the semi-variogram values are collapsed, an extra column,
n.pairs
,
with the number of residual pairs used in each semi-variogram calculation,
is included in the returned data frame.
If
object
includes
a
corSpatial
element,
a data frame with its corresponding semi-variogram
is included in the returned value,
as an attribute
"modelVariog"
.
The returned value inherits from class
Variogram
.
Cressie, N. A. C. (1993). Statistics for Spatial Data. Wiley, New York.
fm1 <- glme(resp ~ trt, Clinic, ~ 1 | clinic, family = binomial) Variogram(fm1, nint = 10, maxDist = 30, robust = TRUE)