Dear all,
I am writing an R code to fit a Bayesian mixed logit (BML) via MCMC / MH
algorithms following Train (2009, ch. 12).
Unfortunately, after many draws the covariance matrix of the correlated random
parameters tend to become a matrix with almost perfect correlation, so I think
there is
many
datapoints, rendering them practically useless again.
So in the end, it always boils down to interpretation.
Cheers
Joris
On Fri, Jun 18, 2010 at 10:29 PM, Carlo Fezzi c.fe...@uea.ac.uk wrote:
Thanks Joris,
I understand your point regarding the need for the two models to be
nested
-project.org [mailto:r-help-boun...@r-project.org]
On Behalf Of Joris Meys
Sent: Monday, June 21, 2010 12:09 PM
To: Carlo Fezzi
Cc: r-help@r-project.org
Subject: Re: [R] {Spam?} Re: mgcv, testing gamm vs lme,which degrees of
freedom?
Hi Carlo,
You should get the book of Simon Wood and read
: r-help-boun...@r-project.org
[mailto:r-help-boun...@r-project.org]
On Behalf Of Joris Meys
Sent: Monday, June 21, 2010 12:09 PM
To: Carlo Fezzi
Cc: r-help@r-project.org
Subject: Re: [R] {Spam?} Re: mgcv, testing gamm vs lme,which degrees
of
freedom?
Hi Carlo,
You should get
, Jun 16, 2010 at 9:33 PM, Carlo Fezzi c.fe...@uea.ac.uk wrote:
Dear all,
I am using the mgcv package by Simon Wood to estimate an additive
mixed
model in which I assume normal distribution for the residuals. I would
like to test this model vs a standard parametric mixed model
the other take this into account?
Sorry if this may sound dull, many thanks for your help,
Carlo
On Wednesday 16 June 2010 20:33, Carlo Fezzi wrote:
Dear all,
I am using the mgcv package by Simon Wood to estimate an additive
mixed
model in which I assume normal distribution for the residuals
...
Cheers
Joris
On Fri, Jun 18, 2010 at 6:27 PM, Carlo Fezzi c.fe...@uea.ac.uk wrote:
Dear Simon,
thanks a lot for your prompt reply.
Unfortunately I am still confused about which is the correct way to test
the two models... as you point out: why in my example the two models
have
the same
Dear all,
I am using the mgcv package by Simon Wood to estimate an additive mixed
model in which I assume normal distribution for the residuals. I would
like to test this model vs a standard parametric mixed model, such as the
ones which are possible to estimate with lme.
Since the smoothing
of a bigger
code and needs to run several times).
Any suggestion would be greatly appreciated.
Carlo
***
Carlo Fezzi
Senior Research Associate
Centre for Social and Economic Research
on the Global Environment (CSERGE),
School of Environmental Sciences
University
Ph. (410) 502-2619
email: rvarad...@jhmi.edu
- Original Message -
From: Carlo Fezzi c.fe...@uea.ac.uk
Date: Thursday, January 7, 2010 12:13 pm
Subject: [R] faster GLS code
To: r-help@r-project.org
Dear helpers,
I wrote a code which estimates a multi-equation model
))
OPTIMIZATION
a-constrOptim(outer.iteration = 500, control = list(maxit=1),
theta=(high-low)/(N+1)* N:1, f=negdet, ui = u.s, ci=c.s,
method=Nelder-Mead)
**
Carlo Fezzi
Senior Research Associate
Centre for Social Research on the Global
constrOptim does not allow this argument which, on
the other hand, is allowed in optim.
I would be extremely grateful if anybody could suggest a way I could use to
I obtain the values of the hessian matrix...
Many thanks,
Carlo
**
Carlo Fezzi
be really grateful if anybody could help me with this issue, I
attach my code below.
Many thanks,
Carlo
***
Carlo Fezzi
Centre for Social and Economic Research
on the Global Environment (CSERGE),
School of Environmental Sciences,
University of East Anglia
To: Carlo Fezzi
Cc: r-help@r-project.org
Subject: Re: [R] R computing speed
I would suggest that you use Rprof to get a profile of the code to see
where time is being spent. You did not provide commented, minimal,
self-contained, reproducible code, so it is hard to tell from just
looking
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