On Fri, 7 Oct 2011, bonda wrote:
Well, I am still confused... shouldn't the "made-by-hands"-process (correctly
constructed, of course) and the gefp()-process be similar?
Correctly constructed yes. And if unsure, just read the source code (as
previously suggested): it's open!
For the case of efp()-process it has been worked, at least... Besides,
I'd like to know: 1) if I have the gefp()-process (scaled to unit
interval [0;1]), can I get the unscaled one for the interval [0;n]?
Make your data a "ts" series with the desired index or use the order.by
argument of gefp().
2) is it possible to define a type of the score process like this was in
efp()? I could find that neither in ?gefp, nor in CSDA-paper-2006.
The 2005 Econ Rev paper on ?gefp explains how various types of processes
are special cases of the gefp framework by setting suitable functionals.
The example was bad and incorrect, the another one:
data("BostonHomicide")
gprocess <- gefp(homicides ~ ahomicides25, family = poisson, data =
BostonHomicide);
my.model <- glm(homicides ~ ahomicides25, family = poisson, data =
BostonHomicide);
J <- vcov(my.model);
This is (a) not what the paper says, (b) not on the right scale.
J.sqrinv <- solve(sqrtm(J))/n;
my.psi <- estfun(my.model);
my.process <- apply(as.matrix(my.psi), 2, cumsum)%*%t(J.sqrinv)/sqrt(n);
It's really not that difficult. All equations below pertain to the CSDA
paper:
## fit model
fm <- glm(homicides ~ ahomicides25, family = poisson,
data = BostonHomicide)
n <- nobs(fm)
## Equation 2
W <- apply(estfun(fm), 2, cumsum) / sqrt(n)
## Equation 3
J <- crossprod(estfun(fm)) / n
## Equation 4
efp1 <- W %*% solve(root.matrix(J))
## via gefp()
efp2 <- gefp(fm, fit = NULL)$process
## using information matrix instead of OPG
efp3 <- W %*% root.matrix(vcov(fm) * n)
## visualize everything
plot(merge(efp2[-1,], efp1, efp3),
screen = rep(1:2, 3), col = rep(1:3, each = 2))
The green lines pertain to the model with the alternative estimator of the
covariances and is hence somewhat different.
Z
Regards,
Julia
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