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.
NAs.
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)