Description Usage Arguments Details Value Author(s) References See Also Examples
Methods to extract and predecorrelate 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 = 1e6,
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 withinsubject 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
predecorrelate the intraindividual 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
withinsubject transformation is applied that removes (approximately)
the intrasubject 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 predecorrelation 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 MFluctuation Tests for Parameter Instability, Statistica Neerlandica, 61(4), 488–508.
Buergin R. and Ritschard G. (2015), TreeBased 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(1id), data = vcrpart_1)
## extract and predecorrelate 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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