Description Usage Arguments Details Value Author(s) References See Also Examples
Methods to extract and pre-decorrelate the (negative)
marginal maximum likelihood observation scores and compute the
standardized cumulative score processes of a fitted
olmm
object.
1 2 3 4 5 6 7 8 9 10 11 12 13 | estfun.olmm(x, predecor = FALSE, control = predecor_control(),
nuisance = NULL, ...)
predecor_control(impute = TRUE, seed = NULL,
symmetric = TRUE, center = FALSE,
reltol = 1e-6,
maxit = 250L, minsize = 1L,
include = c("observed", "all"),
verbose = FALSE, silent = FALSE)
gefp.olmm(object, scores = NULL, order.by = NULL, subset = NULL,
predecor = TRUE, parm = NULL, center = TRUE, drop = TRUE,
silent = FALSE, ...)
|
x, object |
a fitted |
predecor |
logical scalar. Indicates whether the within-subject correlation of the estimating equations should be removed by a linear transformation. See details. |
control |
a list of control parameter as produced by
|
nuisance |
integer vector. Defines the coefficients which are regarded as nuisance and therefore omitted from the transformation. |
impute |
logical scalar. Whether missing values should be replaced using imputation. |
seed |
an integer scalar. Specifies the random number used for
the |
symmetric |
logical scalar. Whether the transformation matrix should be symmetric. |
minsize |
integer scalar. The minimum number of observations for which entries in the transformation should be computed. Higher values will lead to lower accuracy but stabilize the computation. |
reltol |
convergence tolerance used to compute the transformation matrix. |
maxit |
the maximum number of iterations used to compute the transformation matrix. |
silent |
logical scalar. Should the report of warnings be suppressed? |
include |
logical scalar. Whether the transformation matrix
should be computed based on the scores corresponding to observations
(option |
verbose |
logical scalar. Produces messages. |
scores |
a function or a matrix. Function to extract the
estimating equations from |
order.by |
a numeric or factor vector. The explanatory variable
to be used to order the entries in the estimating equations. If set
to |
subset |
logical vector. For extracts the subset of the estimating equations to be used. |
parm |
integer, logical or a character vector. Extracts the columns of the estimating equations. |
center |
logical scalar. |
drop |
logical. Whether singularities should be handled automatically (otherwise singularities yield an error). |
... |
arguments passed to other
functions. |
Complements the estfun
method of the package sandwich
and the gefp
method of the package strucchange for
olmm
objects. estfun.olmm
allows to
pre-decorrelate the intra-individual correlation of observation
scores, see the argument predecor
. The value returned by
gefp.olmm
may be used for testing coefficient constancy
regarding an explanatory variable order.by
by the
sctest
function of package strucchange, see the
examples below.
If predecor = TRUE
in estfun.olmm
, a linear
within-subject transformation is applied that removes (approximately)
the intra-subject correlation from the scores. Backgrounds are
provided by Buergin and Ritschard (2014a).
Given a score matrix produced by estfun.olmm
, the
empirical fluctuation process can be computed by
gefp.olmm
. See Zeileis and Hornik
(2007). gefp.olmm
provides with subset
and
parm
arguments specifically designed for nodewise tests in the
tvcm
algorithm. Using subset
extracts the
partial fluctuation process of the selected subset. Further,
center = TRUE
makes sure that the partial fluctuation process
(starts and) ends with zero.
predecor_control
returns a list of control parameters
for computing the pre-decorrelation transformation
matrix. estfun.olmm
returns a matrix
with the estimating equations and gefp.olmm
a list of
class class "gefp"
.
Reto Buergin
Zeileis A., Hornik K. (2007), Generalized M-Fluctuation Tests for Parameter Instability, Statistica Neerlandica, 61(4), 488–508.
Buergin R. and Ritschard G. (2015), Tree-Based Varying Coefficient Regression for Longitudinal Ordinal Responses. Computational Statistics & Data Analysis, 86, 65–80.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ## ------------------------------------------------------------------- #
## Dummy example :
##
## Testing coefficient constancy on 'z4' of the 'vcrpart_1' data.
## ------------------------------------------------------------------- #
data(vcrpart_1)
## extract a unbalanced subset to show to the full functionality of estfun
vcrpart_1 <- vcrpart_1[-seq(1, 100, 4),]
subset <- vcrpart_1$wave != 1L ## obs. to keep for fluctuation tests
table(table(vcrpart_1$id))
## fit the model
model <- olmm(y ~ treat + re(1|id), data = vcrpart_1)
## extract and pre-decorrelate the scores
scores <- estfun.olmm(model, predecor = TRUE,
control = predecor_control(verbose = TRUE))
attr(scores, "T") # transformation matrix
## compute the empirical fluctuation process
fp <- gefp.olmm(model, scores, order.by = vcrpart_1$z4)
## process a fluctuation test
library(strucchange)
sctest(fp, functional = catL2BB(fp))
|
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