# [R-sig-ME] [R] understanding I() in lmer formula

Ben Bolker bbolker at gmail.com
Sat Jun 17 16:33:04 CEST 2017

```For the level of detail you're getting into, it would be a really good
idea to read the paper that accompanies the lme4 package:
vignette("lmer",package="lme4") .  This goes into a lot of detail
about the theory and data structures ...

On Fri, Jun 16, 2017 at 11:48 PM, Don Cohen <don-r-help at isis.cs3-inc.com> wrote:
>
> (Probably I should change the subject line and admit that this is no
> longer about I().)
>
> Emmanuel Curis writes:
>  > L = \prod_{i=1}^n \prod_{,j=1}^{n_i} F'(Yij=y_ij|Ai=ai,Bi=bi) G'(Ai=ai, Bi=Bi)
>  >
>  > where F' is the density of a Gaussian of mean ai + bi * days_{i,j} and
>  > variance sigma (the variance of epsilon, the residual) and G' the
>  > joint density of a binormal Gaussian vector of expectation (uA, uB)
>  > and of covariance matrix Sigma = ( (f, g ), ( g, h ) ) and you
>  > minimize -log L. i the subject index, j the measure index within the subject.
>  ...
>  > Once you wrote it, you can use dnorm to compute it, and use the logLik
>  > function to check the results.
>
> I'm trying to do this and things are not working out quite as I hoped.
> Starting with the result of lmer on my sample data:
>   > summary(xm)
>   Linear mixed model fit by maximum likelihood  ['lmerMod']
>   Formula: xout ~ xin + (xin | xid)
>      Data: xd
>
>        AIC      BIC   logLik deviance df.resid
>      -16.2    -15.7     14.1    -28.2        2
>
>   Scaled residuals:
>        Min       1Q   Median       3Q      Max
>   -0.89383 -0.29515  0.05005  0.33286  0.80360
>
>   Random effects:
>    Groups   Name        Variance  Std.Dev. Corr
>    xid      (Intercept) 2.814e-03 0.053051
>             xin         6.583e-03 0.081134 -0.03
>    Residual             4.917e-05 0.007012
>   Number of obs: 8, groups:  xid, 3
>
>   Fixed effects:
>               Estimate Std. Error t value
>   (Intercept)  0.06061    0.03172   1.911
>   xin          1.00397    0.04713  21.301
>
>   Correlation of Fixed Effects:
>       (Intr)
>   xin -0.058
>
>   > coef(xm)
>   \$xid
>     (Intercept)      xin
>   a  0.09870105 1.100632
>   b -0.01183068 1.008098
>   c  0.09496031 0.903172
>
> The good news is that I seem to be correctly computing the predicted
> values, e.g., 0.09870105 + 1.100632 * xin for a data point in
> group "a" gives a value which differs from its xout value by the same
> amount as the corresponding value of xm at resp\$wtres (which I surmise
> is a vector of the residues).
> So I expect that F' will be
>  dnorm(xm at resp\$wtres ...)
> The mean should be zero, by design ...
>   > mean(xm at resp\$wtres)
>    -2.775558e-17
> as expected, essentially zero
>
> The sd to pass to dnorm should be what?  Is that the .007012 from
> this line above?
>    Residual             4.917e-05 0.007012
> That's not very close to what I see in the actual residuals:
>   > var(xm at resp\$wtres)
>    1.566477e-05
>   > sd(xm at resp\$wtres)
>    0.003957874
> And I'd expect the value to be at least close to the actual SD of
> the residuals.  If the correct value is neither of the two above,
> please tell me what it is (or how to find it).
>
> Then for G, I'm even less certain.  I suspect I'm supposed to use
> dmvnorm(...)
> The first argument would be one of the lines from the coef output above,
> such as c(0.09870105, 1.100632), right?
> The second (mean) would be the estimates of the fixed effects,
>  c(0.06061,1.00397), right?
> And the third, is supposed to be the variance-covariance matrix.
> That would be your
>  f g
>  g h
> where f and h, are the two variances shown under:
>   Random effects:
>    Groups   Name        Variance  Std.Dev. Corr
>    xid      (Intercept) 2.814e-03 0.053051
>             xin         6.583e-03 0.081134 -0.03
>    Residual             4.917e-05 0.007012
> so f = 2.814e-03, h = 6.583e-03
> and g, I *think* would be the product of the two SD's and the Corr,
> or 0.053051 * 0.081134 * -0.03
> Is that all correct?
>
> I hope you can see some errors above, since this doesn't look like
> it's going to end up computing anything like the logLik of 14.1 !
>
> Also, I have the feeling that all of this output is rounded, and that
> I'd get better results (or at least results that agree with the other
> results computed in the model) if I could find these data in the model
> rather than copying them from the output.  I've found some of it, like
> the residues (at least that's what I think wtres is!), but other data
> above, such f,g,h I have not yet found.  If you can tell me how to find
> (or compute) them, that would be helpful.
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

```