[R-sig-ME] R-sig-mixed-models Digest, Vol 115, Issue 19

[Mattia Manica]

Dear Teresa,

I hope this paper would be helpful Wenger et al 2012 "Assessing
transferability of ecological models: an underappreciated aspect of
statistical validation"
http://depts.washington.edu/oldenlab/wordpress/wp-content/uploads/2013/03/MethodsEE_2012.pdf

Regards

On Sat, Jul 16, 2016 at 12:00 PM, <r-sig-mixed-models-request at r-project.org>
wrote:

> Send R-sig-mixed-models mailing list submissions to
>         r-sig-mixed-models at r-project.org
>
> To subscribe or unsubscribe via the World Wide Web, visit
>         https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> or, via email, send a message with subject or body 'help' to
>         r-sig-mixed-models-request at r-project.org
>
> You can reach the person managing the list at
>         r-sig-mixed-models-owner at r-project.org
>
> When replying, please edit your Subject line so it is more specific
> than "Re: Contents of R-sig-mixed-models digest..."
>
>
> Today's Topics:
>
>    1. Re: K-fold cross validation of GLMMs (Ben Bolker)
>    2. cross validation for discrete time survival analysis
>       (shahla ebrahimi)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Fri, 15 Jul 2016 16:07:23 +0000 (UTC)
> From: Ben Bolker <bbolker at gmail.com>
> To: r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] K-fold cross validation of GLMMs
> Message-ID: <loom.20160715T164234-254 at post.gmane.org>
> Content-Type: text/plain; charset=us-ascii
>
> Teresa Oliveira <mteresaoliveira92 at ...> writes:
>
> >
> > Dear list members,
> >
> > I would like to know if you have knowledge about any R-package that
> allows
> > to perform k-fold cross validation of a GLMM (developed with
> > "lme4::glmer()")?
> >
> > If there is none, which other kind of cross validation do you think is
> > appropriate, and which packages are available?
> > Or which other way to validate GLMMs, rather than cross validation, do
> you
> > propose?
> >
> > Thank you very much in advance for your help!
> >
> > Best regards,
> > Teresa
>
>   I don't know of a package offhand.  (I haven't tried, but I
> *strongly* recommend the 'sos' package; the findFn() command does
> a full-text search of packages on CRAN ...)
>
>   This is going to be a little bit tricky because you really ought
> to cross-validate at the level of the grouping factor, not at the
> level of the individual observation (this is assuming you have
> only a single grouping factor, or at worst nested grouping factors).
> Given a specified loss/objective function, it shouldn't be too hard
> to put together a loop that would do this; it might even be possible
> to use the modular structure of merMod objects to avoid redoing some
> of the expensive computations each time round.
>   Perhaps someone has done some of this and could share code ... ?
>
>
>
> ------------------------------
>
> Message: 2
> Date: Sat, 16 Jul 2016 03:50:25 +0000 (UTC)
> From: shahla ebrahimi <shebrahimi_3622 at yahoo.com>
> To: "r-sig-mixed-models at r-project.org"
>         <r-sig-mixed-models at r-project.org>
> Subject: [R-sig-ME] cross validation for discrete time survival
>         analysis
> Message-ID:
>         <1469054561.84641.1468641025575.JavaMail.yahoo at mail.yahoo.com>
> Content-Type: text/plain; charset="UTF-8"
>
> Dear Mr./Ms.
>
> Greetings
> I would be very grateful if you could let me know how to do cross
> validation when estimating a discrete time survival analysis in R.
> library(readxl)require(lme4)
> setwd("D:/Nasdaq")df=read_excel("Book1.xlsx",sheet = 1)model <-
> glmer(EVENT ~ TIME + (1+TIME|ID)+x1+x2+x3+x4+x5, data=df, family=binomial)p
> <- as.numeric(predict(model, type="response")>0.5)acc=mean(p==df$EVENT)
>
> Thanks in advance.Best regards,
>
>
>
>
>         [[alternative HTML version deleted]]
>
>
>
> ------------------------------
>
> Subject: Digest Footer
>
> _______________________________________________
> R-sig-mixed-models mailing list
> R-sig-mixed-models at r-project.org
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
> ------------------------------
>
> End of R-sig-mixed-models Digest, Vol 115, Issue 19
> ***************************************************
>

	[[alternative HTML version deleted]]