On page 12 of Bates' book on mixed effect model, he describes the model as follows:

Bates' mixed effect model

Near the end of the screenshot, he mentions the

relative covariance factor $\Lambda_{\theta}$, depending on the variance-component parameter, $\theta$

without explaining what exactly is the relationship. Say we are given $\theta$, how would we derive $\Lambda_{\theta}$ from it?

On a related note, this is one of many instance in which I find Bates' exposition to be a bit lacking in details. Is there a better text that actually goes through the optimization process of parameter estimation and the proof for the distribution of test statistic?

  • 1
    $\begingroup$ I think $\theta$ just means what kind of variance-component you will assume, such as AR(1) or UN, etc. $\endgroup$
    – Deep North
    Commented Jun 11, 2015 at 6:50
  • $\begingroup$ @DeepNorth I have been reading the text more closely, and at some point the author talks about optimizing the likelihood with regards to $\theta$. So I think $\theta$ must be an actual parameter. (page 108, sec 5.4.2) $\endgroup$
    – Heisenberg
    Commented Jun 13, 2015 at 5:50
  • $\begingroup$ Did you manage to figure this out?, I'm having the same difficulty understanding the relationship between the covariance matrix and theta. $\endgroup$
    – user91529
    Commented Oct 8, 2015 at 5:32
  • $\begingroup$ Have you abandoned the question? So far, two answers have been provided, without a single comment on them. Please consider giving constructive feedback on the answers, such that, if they are not providing a (satisfactory) solution, at least a discussion can develop narrowing down the problem and leading to its solution. Not reacting to the answers of your question discourages further answers. $\endgroup$
    – skip
    Commented Jan 18, 2016 at 12:08

2 Answers 2


The variance-component parameter vector $\theta$ is estimated iteratively to minimise the model deviance $\widetilde{d}$ according to eq. 1.10 (p. 14).

The relative covariance factor, $\Lambda_\theta$, is a $q \times q$ matrix (dimensions are explained in the excerpt you posted). For a model with a simple scalar random-effects term, (p. 15, Fig. 1.3) it is calculated as a multiple of $\theta$ and identity matrix of dimensions $q \times q$:

$$\Lambda_\theta = \theta \times {I_q}$$


This is the general way to calculate $\Lambda_\theta$, and it is modified according to the number of random-effects and their covariance structure. For a model with two uncorrelated random-effects terms in a crossed design, as on pp. 32-34, it is block diagonal with two blocks each of which is a multiple of $\theta$ and identity (p. 34, Fig. 2.4):


Same with two nested random-effects terms (p. 43, Fig. 2.10, not shown here).

For a longitudinal (repeated-measures) model with a random intercept and a random slope which are allowed to correlate $\Lambda_\theta$ consists of triangular blocks representing both random-effects and their correlation (p. 62, Fig. 3.2):

enter image description here

Modelling the same dataset with two uncorrelated random-effects terms (p. 65, Fig. 3.3) returns $\Lambda_\theta$ of the same structure as shown previously, in Fig. 2.4:

enter image description here

Additional notes:

$\theta_i = \frac{\sigma_i}{\sigma}$ Where $\sigma_i$ refers to the square root of the i-th random-effect variance, and $\sigma$ refers to the square root of the residual variance (compare with pp. 32-34).

The book version from June 25, 2010 refers to a version of lme4 which has been modified. One of the consequences is that in the current version 1.1.-10. the random-effects model object-class merMod has a different structure and $\Lambda_\theta$ is accessed in a different way, using the method getME:

image(getME(fm01ML, "Lambda"))

It's hierarchical reasoning. There are a bunch of parameters in your linear model, the components of b. In a pure fixed effects model you would just get estimates of these and that would be that. Instead, you imagine that the values in b themselves are drawn from a multivariate normal distribution with a covariance matrix that is parameterized by theta. Here is a simple example. Suppose we look at animal counts at five different time periods at 10 different locations. We would get a linear model (I'm using R talk here) that would look like count ~ time + factor(location), so that you would have (in this case) a common slope for all of the regression (one at each location) but a different intercept at each location. We could just punt and call it a fixed effect model and estimate all of the intercepts. However, we want might not care about the particular locations if they were 10 locations selected from a large number of possible locations. So we put a covariance model on the intercepts. For instance, we declare the intercepts to be multivariate normal and independent with common variance sigma2. Then sigma2 is the "theta" parameter, because it characterizes the population of intercepts at each location (which are thus random effects).


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