[R-sig-ME] LogLikelihood

[bbonit at tin.it]

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
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