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> stopifnot(require(lme4))
Loading required package: lme4
Loading required package: Matrix
> 
> (testLevel <- lme4:::testLevel())
[1] 1
> source(system.file("testdata", "lme-tst-funs.R", package="lme4", mustWork=TRUE))# -> unn()
> 
> 
> ## "MEMSS" is just 'Suggest' -- must still work, when it's missing:
> if (suppressWarnings(!require(MEMSS, quietly=TRUE)) ||
+     (data(ergoStool, package="MEMSS") != "ergoStool")) {
+ 
+     cat("'ergoStool' data from package 'MEMSS' is not available --> skipping test\n")
+ } else {
+ 
+ fm1   <-  lmer (effort ~ Type + (1|Subject), data = ergoStool)
+ ##sp no longer supported since ~ 2012-3:
+ ##sp fm1.s  <- lmer (effort ~ Type + (1|Subject), data = ergoStool, sparseX=TRUE)
+ ## was segfaulting with sparseX (a while upto 2010-04-06)
+ 
+ fe1   <- fixef(fm1)
+ ##sp fe1.s <- fixef(fm1.s)
+ 
+ print(s1.d <- summary(fm1))
+ ##sp print(s1.s <- summary(fm1.s))
+ Tse1.d <- c(0.57601226, rep(0.51868384, 3))
+ stopifnot(exprs = {
+     ##sp all.equal(fe1, fe1.s, tolerance= 1e-12)
+     all.equal(Tse1.d, unname(se1.d <- coef(s1.d)[,"Std. Error"]),
+               tolerance = 1e-6) # std.err.: no too much accuracy
+     is(V.d <- vcov(fm1), "symmetricMatrix")
+     ##sp all.equal(se1.d, coef(s1.s)[,"Std. Error"])#, tol = 1e-10
+     ##sp all.equal(  V.d, vcov(fm1.s))#, tol = 1e-9
+     all.equal(Matrix::diag(V.d), unn(se1.d)^2, tolerance= 1e-12)
+ })
+ 
+ }## if( ergoStool is available from pkg MEMSS )

Attaching package: 'MEMSS'

The following objects are masked from 'package:datasets':

    CO2, Orange, Theoph

Linear mixed model fit by REML ['lmerMod']
Formula: effort ~ Type + (1 | Subject)
   Data: ergoStool

REML criterion at convergence: 121.1

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.80200 -0.64317  0.05783  0.70100  1.63142 

Random effects:
 Groups   Name        Variance Std.Dev.
 Subject  (Intercept) 1.775    1.332   
 Residual             1.211    1.100   
Number of obs: 36, groups:  Subject, 9

Fixed effects:
            Estimate Std. Error t value
(Intercept)   8.5556     0.5760  14.853
TypeT2        3.8889     0.5187   7.498
TypeT3        2.2222     0.5187   4.284
TypeT4        0.6667     0.5187   1.285

Correlation of Fixed Effects:
       (Intr) TypeT2 TypeT3
TypeT2 -0.450              
TypeT3 -0.450  0.500       
TypeT4 -0.450  0.500  0.500
> 
> ### -------------------------- a "large" example -------------------------
> str(InstEval)
'data.frame':	73421 obs. of  7 variables:
 $ s      : Factor w/ 2972 levels "1","2","3","4",..: 1 1 1 1 2 2 3 3 3 3 ...
 $ d      : Factor w/ 1128 levels "1","6","7","8",..: 525 560 832 1068 62 406 3 6 19 75 ...
 $ studage: Ord.factor w/ 4 levels "2"<"4"<"6"<"8": 1 1 1 1 1 1 1 1 1 1 ...
 $ lectage: Ord.factor w/ 6 levels "1"<"2"<"3"<"4"<..: 2 1 2 2 1 1 1 1 1 1 ...
 $ service: Factor w/ 2 levels "0","1": 1 2 1 2 1 1 2 1 1 1 ...
 $ dept   : Factor w/ 14 levels "15","5","10",..: 14 5 14 12 2 2 13 3 3 3 ...
 $ y      : int  5 2 5 3 2 4 4 5 5 4 ...
> 
> if (FALSE) { # sparse X is not currently implemented, so forget about this:
+ 
+ system.time(## works with 'sparseX'; d has 1128 levels
+ fm7 <- lmer(y ~ d + service + studage + lectage + (1|s),
+              data = InstEval, sparseX=TRUE, verbose=1L, REML=FALSE)
+ )
+ system.time(sfm7 <- summary(fm7))
+ fm7 # takes a while as it computes summary() again !
+ 
+ range(t.fm7 <- coef(sfm7)[,"t value"])## -10.94173  10.61535 for REML, -11.03438  10.70103 for ML
+ 
+ m.t.7 <- mean(abs(t.fm7), trim = .01)
+ #stopifnot(all.equal(m.t.7, 1.55326395545110, tolerance = 1.e-9)) ##REML value
+ stopifnot(all.equal(m.t.7, 1.56642013605506, tolerance = 1.e-6)) ## ML
+ 
+ hist.t <- cut(t.fm7, floor(min(t.fm7)) : ceiling(max(t.fm7)))
+ cbind(table(hist.t))
+ 
+ }# fixed effect 'd' -- with 'sparseX' only --------------------------------
> 
> if(testLevel <= 1) { cat('Time elapsed: ', proc.time(),'\n'); q("no") }
Time elapsed:  1.76 0.26 1.96 NA NA 
> proc.time()
   user  system elapsed 
   1.76    0.26    1.96