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R-sig-mixed-models Digest, Vol 115, Issue 19

Sat, Jul 16, 2016 3:29 AM

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>
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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,




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