lme
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)
lme
, representing
a fitted 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.
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. 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", J. Wiley & Sons.
fm1 <- lme(weight ~ Time * Diet, BodyWeight, ~ Time | Rat) Variogram(fm1, form = ~ Time | Rat, nint = 10, robust = TRUE)