[R-sig-ME] random effect latent variable model,

[Alex Fine]

Following up on what Jim wrote, I actually think it'd be easier to help if
you just describe the model in terms independent of a programming
language.  For example, "a linear mixed effects regression with a random
intercept for X and a by-X random slope for Z" or something.  That way
people who use R but do not use SAS (like me) can still help you.

On Wed, Jan 4, 2017 at 2:02 PM, Ren-Huai Huang <huangrenhuai at gmail.com>
wrote:

> Thank you Jim.
>
> I will look at PROC GLIMMIX in SAS. I choose PROC NLMIXED is because using
> general(ll)  in the model statement can specifiesa general log likelihood
> function.
>
> Attached below is the objective function I have tried in R . What I am not
> sure is to join the log likelihood with the random effect as I did in the
> last four lines. Let me know if I am wrong
> see https://github.com/huangrh/relvm for more details.
>
> Thanks again!
>
> Regards,
>
> Renhuai
>
> # -------------------------------------------------------------------
> # negative log likelihood: How to join Likelihood with random effect
> nll <- function(par, data = data_tbl, w = weight_tbl) { # Reconstruction of
> the parameters from the vector mu <- par[grep("mu",names(par))] # fl <-
> par[grep("fl", names(par))] # factor loading lv <-
> par[grep("lv",names(par))] # factor variable err <- par[grep("err",
> names(par))] # # # Convert to matrix d <- dim(data); nr <- d[1]; nc <- d[2]
> data<- as.matrix(data); w <- as.matrix(w) mu <- matrix(mu, nrow = nr, ncol
> = nc, byrow = TRUE) fl <- matrix(fl, nrow = nr, ncol = nc, byrow = TRUE) lv
> <- matrix(lv, nrow = nr, ncol = nc, byrow = FALSE) err <- matrix(err,nrow =
> nr, ncol = nc, byrow = TRUE) # negtive log likelyhood means <- mu + fl * lv
> out <- sum(( w * (dnorm(data, mean=means, sd = err, log=TRUE))),na.rm=TRUE)
> out <- out + sum(dnorm(lv[,1], mean = 0, sd = 1, log=TRUE), na.rm=TRUE)
> (-out) }
> #-----------------------------------------------------------
> -------------------------
> # optimize the nll function set.seed(100)
> #
> # the data set is available in the Github # <
> https://github.com/huangrh/relvm/blob/master/inst/extdata/dat244.csv>
> file
> <- system.file("/extdata/dat4557.csv", package="relvm") dat <-
> read.csv(file, na.strings=".") # subset score_tbl <- subset(dat,
> select=c(paste0("x",1:7))) weight_tbl<- subset(dat,
> select=c(paste0("w",1:7)))
> # Init parameters d <- dim(score_tbl); init <- unlist(list(mu = rep(0,
> d[2]), fl = rep(1, d[2]), # factor loading lv = rep(1, d[1]), # latent
> variable err = rep(1, d[2]))) # fit the function fit <- optim(par = init,
> fn = relvm:::nll, gr = NULL, method = "BFGS", control= list(maxit=5200),
> data = score_tbl, w = weight_tbl)
>
>
>
> On Wed, Jan 4, 2017 at 11:06 AM, Baldwin, Jim -FS <jbaldwin at fs.fed.us>
> wrote:
>
> > I  think you'd get a better response if you gave a complete (but maybe
> > minimal) working example.  Also, I suspect you'd be better off if your
> > example used PROC GLIMMIX in SAS rather than PROC NLMIXED.  And maybe
> > showing what you've tried so far would also create more interest to help.
> >
> > Jim
> >
> >
> > -----Original Message-----
> > From: R-sig-mixed-models [mailto:r-sig-mixed-models-
> bounces at r-project.org]
> > On Behalf Of Ren-Huai Huang
> > Sent: Tuesday, January 03, 2017 8:57 PM
> > To: r-sig-mixed-models at r-project.org
> > Subject: Re: [R-sig-ME] random effect latent variable model,
> >
> > Happy new year,
> >
> > My question is how to replicate the following sas code in R, Julia or
> > other open source languages? Thanks in advance!
> >
> > proc nlmixed data=input tech=dbldog qpoints=30 noad;
> >         ...
> >     model   id ~ general(loglik);
> >     random  latent_var ~ normal(0, 1) subject= id;
> >        ...
> > run;
> >
> > Regards,
> >
> > Renhuai
> >
> >
> > On Fri, Dec 30, 2016 at 4:24 PM, Ren-Huai Huang <huangrenhuai at gmail.com>
> > wrote:
> >
> > > Re: Random Effect Latent Variable Model
> > >
> > >
> > >
> > > Dear mixed model members,
> > >
> > >
> > > I am trying to do a weighted latent variable modeling with random
> > > effects in R, which is similar to sas nlmixed general log likelihood
> > > over a random effect (sas code attached here
> > > <https://huangrh.github.io/relvm/vignettes/sascode.html>). The purpose
> > > is to calculate a latent variable from 7 observed variables x1, x2, …
> > > , x7 with weightings w1, w2, … , w7 to the corresponding likelihood of
> > > the variables (the dataset is attached here
> > > <https://github.com/huangrh/relvm/blob/master/inst/extdata/dat244.csv
> >).
> > >
> > >
> > > Both equations of the weighted log likelihood and the random effect
> > > log likelihood are linked here
> > > <https://huangrh.github.io/relvm/vignettes/formula.html> (equations 2
> > > and 3). I was wondering if the subjective function should be the joint
> > > probability of both equations 2 and 3, as shown in equation 4. How to
> > > join the equation 2 with the random effect?
> > >
> > >
> > > I tried to optimize the function (link to the R code, lines 36-37
> > > <https://github.com/huangrh/relvm/blob/master/R/nll.R>) using optim in
> > > R (link to vignette
> > > <https://huangrh.github.io/relvm/vignettes/Random_Effect_
> > Latent_Variable_Model.html>).
> > > But the result is very different from the sas nlmixed mentioned above
> > > <https://huangrh.github.io/relvm/vignettes/sascode.html>.
> > >
> > >
> > > Any suggestions are very welcome to help me to do this right in R.
> > > Thank you very much in advance.
> > >
> > >
> > >
> > > Sincerely,
> > >
> > > Ren-Huai Huang
> > >
> >
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> >
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-- 
Alex Fine
Ph. (336) 302-3251
web:  abfine.github.io/

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