Hello all, I have the following two questions: 1) how can I get the values of boundaries for fluctuation process gefp(), for functionals maxBB, meanL2BB, etc? 2) how can I get fragments of gefp()-process, e.g., if I have n=200 observations, i=1,2,...,200, and need gefp()[50:100], i.e. from i=50 till i=100? Thank you in advance, Julia -- View this message in context: http://r.789695.n4.nabble.com/gefp-boundaries-tp3872529p3872529.html Sent from the R help mailing list archive at Nabble.com.
On Tue, 4 Oct 2011, bonda wrote:> Hello all, > > I have the following two questions: 1) how can I get the values of > boundaries for fluctuation process gefp(), for functionals maxBB, meanL2BB, > etc?Both, maxBB and meanL2BB, are objects of class "efpFunctional" that contain several functions including $computeCritval and $boundary that compute the critical value c and base boundary b(t), respectively. The full boundary is the c * b(t). In case of maxBB and meanL2BB however, the boundary is constant anyway, i.e., b(t) = 1, so you essentially need only the critical value. ## compute and plot gefp library("strucchange") data("durab") scus <- gefp(y ~ lag, data = durab) plot(scus, functional = meanL2BB) ## add the critical value (= boundary here) in another color abline(h = meanL2BB$computeCritval(0.05, nproc = 2), col = 4)> 2) how can I get fragments of gefp()-process, e.g., if I have n=200 > observations, i=1,2,...,200, and need gefp()[50:100], i.e. from i=50 till > i=100?gefp_object$process has the cumulative score process as a "zoo" object which preserves the original time scale of the data (if any), e.g. proc <- scus$process window(proc, start = 2000, end = 2001) proc[11:20] Note, however, that the process has n+1 observations because an additional zero is added as the first element (to facilitate visualizations etc.). See ?gefp, ?efpFunctional for more information and in particular Zeileis A. (2006), Implementing a Class of Structural Change Tests: An Econometric Computing Approach. _Computational Statistics & Data Analysis_, *50*, 2987-3008. doi:10.1016/j.csda.2005.07.001. hth, Z> Thank you in advance, > Julia > > -- > View this message in context: http://r.789695.n4.nabble.com/gefp-boundaries-tp3872529p3872529.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >
Thank you very much for the answer. If I take Poisson model and follow
"Generalized M-fluctuation tests for parameter instability", A.
Zeileis and
K. Hornik, Statistica Neerlandica (2007) Vol. 61, N. 4, p. 500-501 (section
4.3):
data("Boston")
n <- 506;
my.X <- as.matrix(cbind(1, Boston["crim"],
Boston["age"]));
my.model <- glm(tax ~ crim + age, family = poisson, data = Boston);
my.psi <- estfun(my.model);
my.mu <- fitted(my.model);
J <- sum(my.mu*my.X%*%t(my.X))/n;
my.process <- apply(as.matrix(my.psi), 2, cumsum)/sqrt(J*n);
gprocess <- gefp(tax ~ crim + age, family = poisson, data=Boston);
then my.process and gprocess$process have to be the same?
Best regards,
Julia
--
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On Thu, 6 Oct 2011, bonda wrote:> Thank you very much for the answer. If I take Poisson model and follow > "Generalized M-fluctuation tests for parameter instability", A. Zeileis and > K. Hornik, Statistica Neerlandica (2007) Vol. 61, N. 4, p. 500-501 (section > 4.3): > > data("Boston") > n <- 506;I guess that this is data("Boston", package = "MASS")?> my.X <- as.matrix(cbind(1, Boston["crim"], Boston["age"])); > my.model <- glm(tax ~ crim + age, family = poisson, data = Boston);Applying a Poisson model for count data to a tax rate seems to be very awkward.> my.psi <- estfun(my.model); > my.mu <- fitted(my.model); > J <- sum(my.mu*my.X%*%t(my.X))/n;This should be the covariance matrix, not a scalar.> my.process <- apply(as.matrix(my.psi), 2, cumsum)/sqrt(J*n); > > gprocess <- gefp(tax ~ crim + age, family = poisson, data=Boston); > > then my.process and gprocess$process have to be the same?No. Even if you would have computed the J that you would have been interested in (and computed its root correctly), then gefp() still uses a different covariance matrix estimator by default. See ?gefp and the 2006 CSDA paper for more details. Looking at the actual code may also help.> Best regards, > Julia > > -- > View this message in context: http://r.789695.n4.nabble.com/gefp-boundaries-tp3872529p3878886.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >