Hi Shubha,
genoud does not return the initial fit value. But you could easily
obtain it by passing your starting values to your function directly.
Alternatively, one can have genoud print out the entire initial
population (or the entire population as is evolves), and one can then
decide to
Hi Paul,
Solution.tolerance is the right way to increase precision. In your
example, extra precision *is* being obtained, but it is just not
displayed because the number of digits which get printed is controlled
by the options(digits) variable. But the requested solution
precision is in the
Hi Paul,
I see. You want to increase the population size (pop.size)
option---of lesser importance are the max.generations,
wait.generations and P9 options. For more details, see
http://sekhon.berkeley.edu/papers/rgenoudJSS.pdf.
For example, if I run
a - genoud(myfunc, nvars=2,
The issue is that you are using a derivative based optimizer for a
problem for which it is well known that such optimizers will not
perform well. You should consider using a global optimizer. For
example, rgenoud combines a genetic search algorithm with a BFGS
optimizer and it works well for
Hi Gala,
The default p-value is the bootstrap p-value for the ks-test.
Bootstrapping is highly recommended because the bootstrapped
Kolmogorov-Smirnov test, unlike the standard test, provides correct
coverage even when there are point masses in the distributions being
compared. The bootstrap
As we noted earlier and as is clearly stated in the docs, multinomRob
is estimating an OVERDISPERSED multinomial model. And in your models
here the overdispersion parameter is not identified; you need more
observations. Walter pointed out using the print.level trick to get
the coefs for the
Hi Roger,
Yes, multinomRob can handle equality constraints of this type---see
the 'equality' option. But the function assumes that the outcomes are
multinomial counts and it estimates overdispersed multinomial logistic
models via MLE, a robust redescending-M estimator, and LQD which is
another
Mebane
Jasjeet Singh Sekhon writes:
Hi Roger,
Yes, multinomRob can handle equality constraints of this type---see
the 'equality' option. But the function assumes that the outcomes
are
multinomial counts and it estimates overdispersed
cannot compute correct p-values with ties in: ks.test(x, pgev,
fit$mle[1], fit$mle[2], fit$mle[3])
You may want to use the ks.boot function in the Matching package which
implements a bootstrap ks-test which provides consistent pvalues
(achieved significance levels) when there are ties.
Does anyone else find that using the Var.calc option (for
heteroscedasticity consistent std. errors) in Match() (from the
Matching library) slows down computation of the matching estimator by
a lot?
The Var.calc option to Match() slows down the function because an
additional loop through the
How do you go about deciding how many matches you will use? With my
data, my standard errors generally get smaller if I use more
matches.
Generally, select the max number of matches that result in good or
acceptable balance (hence bounding bias due to the observed
confounders). See the
Hi,
64bit CPUs, such as opterons, help significantly with large databases
or if you are running multiple processes. But there is a speed
penalty if you are not.
Some packages can make use of multiple processors, such as my rgenoud
(genetic optimization using derivatives) and Matching packages,
===
Thomas Lumley writes:
On Wed, 5 Apr 2006, Jasjeet Singh Sekhon wrote:
Hi,
64bit CPUs, such as opterons, help significantly with large databases
or if you are running multiple processes. But there is a speed
penalty if you are not.
This would
Given that information, I think a genetic algorithm
should probably do well with your problem.
You may want to try the rgenoud package (R-GENetic Optimization Using
Derivatives) which is on CRAN. For more information see:
http://sekhon.berkeley.edu/rgenoud/
It works well for these kinds of
Hi Scott,
It is difficult to debug your issue without more information. Would
it be possible to email me code of a simple example?
Cheers,
Jas.
===
Jasjeet S. Sekhon
Associate Professor
Matching version 0.48 is now available on CRAN.
Matching provides functions for estimating causal effects by
multivariate and propensity score matching. The package includes a
variety of univariate and multivariate tests to determine if balance
has been obtained by the matching procedure. These
Author: Walter R. Mebane, Jr. [EMAIL PROTECTED], Jasjeet Singh Sekhon [EMAIL
PROTECTED]
Maintainer: Jasjeet Singh Sekhon [EMAIL PROTECTED]
Description: overdispersed multinomial regression using robust (LQD and tanh)
estimation
Depends: R (= 1.7.0), rgenoud (= 1.22), MASS (= 7.1-8), mvtnorm (= 0.6-3
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