[R-sig-ME] LogLikelihood

[Andrzej Galecki]

More precisely D should be positive definite and have 2 by  2 blocks on the
diagonal.

AG



On Sun, Jan 25, 2015 at 12:51 PM, Andrzej Galecki <agalecki at umich.edu>
wrote:

> Hello Gianluca,
>
> There are two random effects (q=2).
>
> Matrix D should be 2 by 2, not 6 by 6.
>
> Did not check the rest of your code, but this is an obvious mistake/error.
>
> Best wishes
>
> Andrzej Galecki
>
>
> On Sun, Jan 25, 2015 at 12:27 PM, bbonit at tin.it <bbonit at tin.it> wrote:
>
>>
>>
>> Dear list, my name is Gianluca Bonitta
>> I'm trying to build up the Loglikelihood of the following model.
>> For check it I had used logLik(mod0,REML=F) like "gold standard"
>> Like You see there is a difference   # diff   logLik(mod0,REML=F) - mylog
>> = 0.6339805
>> Can somebody help to resolve my mistake ?
>> Maybe professor Bolker or professor Bates that are the "fathers" of lme4
>> pack
>> thank You in advance
>> Best
>> Gianluca
>>
>>
>> ########################################################################################
>> library(lme4)
>> data(sleepstudy)
>> dat <- sleepstudy[ (sleepstudy$Days %in% 0:4) & (sleepstudy$Subject
>> %in% 331:333) ,]
>> colnames(dat) <- c("y", "x", "group")
>> mod0 <- lmer( y ~ 1 + x  +( x | group ), data = dat,REML="F")
>>
>> ########################################################################################
>>
>>   q <- 2                                          # number of random
>> effects
>>   n <- nrow(dat)                              # number of individuals
>>   m <- length(unique(dat$group))      # number of groups
>>   Y <- dat$y                                    # response vector
>>   R <- diag(1,nrow(dat))*summary(mod0)$sigma^2    # covariance matrix of
>> residuals
>>   beta <- as.numeric(fixef(mod0))                 # fixed effects vector
>> (p x 1)
>>   a<-rep(c(597.1903,60.05023),m)                  # variance rand effects
>>   ranef(mod0)$group
>>   b <-c(17.94432, -3.753130,-33.31148, 10.294328,15.36716, -6.541198) #
>> random effect estimated
>>   D <-matrix(-0.97,6,6)                           # random effect
>> estimated correlation
>>   diag(D) <-a
>>   X <- cbind(rep(1,n), dat$x)                     # model matrix of fixed
>> effects (n x p)
>>   Z.sparse<- getME(mod0,"Z")                   # model matrix of random
>> effect (sparse format)
>>   Z <- as.matrix(Z.sparse)
>>   V <-Z%*% D %*% t(Z) + R                   # (total) covariance matrix
>> of Y
>>   # check: values in Y can be perfectly matched using lmer's information
>>   Y.test <- X %*% beta + Z %*% b + resid(mod0)
>>   cbind(Y, Y.test)
>>   mu = X %*% beta + Z %*% b
>>
>> ###############################################################################################
>>    ll = -n/2*log(2*pi) - sum(log(diag(chol(V)))) -  .5 * t(Y- mu) %*%
>> chol2inv(chol(V)) %*% (Y-mu);
>>    logLik(mod0,REML=F)
>>    ll
>> ####################################à
>> # diff   'log Lik.' 0.6339805 (df=6)
>>
>>    logLik(mod0,REML=F) -ll
>>         [[alternative HTML version deleted]]
>>
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>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>
>

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