random effect latent variable model,

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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