[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
Joseph Coveney <jcoveney@bigplanet.com> |

To |
Statalist <statalist@hsphsun2.harvard.edu> |

Subject |
Re: st: cross-classified random effects |

Date |
Wed, 28 Apr 2004 20:53:53 +0900 |

Last month Phoenix Do posted to the list a question about whether -gllamm- or another Stata command can handle cross-classified random effects. Laurent Glance replied that he did not think that -gllamm- (which is set up primarily for hierarchical models) could handle cross-classified random effects models. I believe that -gllamm- *can* estimate crossed-random effects models. Harvey Goldstein describes how to set up the crossed-random effects model for a hierarchical software package such as HLM (or MLwiN, or -gllamm-) in Chapter 8 of his _Multilevel Statistical Models_ Second Edition. (London: Edward Arnold, 1995), esp. pp. 116-17 and pp. 123-24. (This book is the third in the series, Kendall's Library of Statistics, and is co-published in the U.S.A. by Halsted Press, part of John Wiley & Sons.) Harvey Goldstein's approach is to choose one of the crossed random effects as the second level, and to take the other crossed effect and create dummy (indicator) variables of it as random effects at a third level, and to -constraint- the variance covariance matrix of this second random effect so that the diagonal elements are equal and off-diagonal elements are zero. I've illustrated the approach in the do-file below using the dataset from P. E. Shrout & J. L. Fleiss, Intraclass Correlations: Uses in Assessing Rater Reliability. _Psychological Bulletin_ 86:420-28, 1979. Because there are multiple random effects at the third level, you'll need to use the -eqs- option to specify equations for them. And because you need to use -eqs-, you'll need to specify the equation for the random effect at the second level of the hierarchy, as well. To do that, use the method described in -gllamm-'s user's manual: create a variable equal to one so that an intercept-only constant can be defined for the equation for the random effect at the second level. Run -gllamm, noest- to get the names of the elements of the variance-covariance matrix, and set up -constraints- to make for the matrix structure of the random effects at the third level of the hierarchy, which are really a dummy-variable rendition of the second of the two crossed random effects. -gllamm- takes care of the variance-covariance matrix at the second level, and you'll see the structure of the block-diagonal Cholesky decomposition matrix with that random effect represented as a single element and with zero covariance between it and elements for the random effect at the third level (the crossed random effect). The latter are set up with zero covariance, and this is seen in the Cholesky decomposition matrix, as well. Use -matrix list e(chol)- after convergence to see all of this. Now, with the Shrout & Fleiss small dataset of four raters, -gllamm- will need to estimate five random effects. -gllamm- takes its time estimating numerous random effects, and if you have dozens of neighborhoods, it will still be worthwhile to use an alternative software package, such as one of those that Laurent mentioned. (Or hold off until Stata releases their offering for mixed models, as Bobby Gutierrez mentioned on Monday.) In addition, if you're working with normal (linear) models and are interested in the three variance components in their own right, such as to estimate a judges-as-random-effect intraclass correlation coefficient for an unbalanced dataset, then -gllamm-'s maximum likelihood method will give more biased estimates than will restricted maximum likelihood (REML) despite that there's only a single fixed effect (the intercept, the two populations' joint mean). So, for a project that I just finished to estimate ICC(2,1) for an unbalanced dataset of physicians evaluating patients, I needed to resort to S-Plus, anyway. (It's a little awkward to fit a simple unblocked crossed-random effects model in R/S-Plus, too, by the way. See https://stat.ethz.ch/pipermail/r-help/2003-October/039775.html , but also http://maths.newcastle.edu.au/~rking/R/help/03b/6412.html .) Joseph Coveney ---------------------------------------------------------------------------- clear set more off input byte target byte judge byte score 1 1 9 1 2 2 1 3 5 1 4 8 2 1 6 2 2 1 2 3 3 2 4 2 3 1 8 3 2 4 3 3 6 3 4 8 4 1 7 4 2 1 4 3 2 4 4 6 5 1 10 5 2 5 5 3 6 5 4 9 6 1 6 6 2 2 6 3 4 6 4 7 end /* A constant is needed for the equation for the intercept of the random effect at the second level. */ generate byte targets = 1 eq k: targets /* Create dummy (indicator) variables for the crossed random effect that will be assigned to the third level of the hierarchical model, and assign each to an equation for intercept of a random effect. */ tabulate judge, generate(rater) eq r1: rater1 eq r2: rater2 eq r3: rater3 eq r4: rater4 /* Define constraints to hold the diagonal elements of the Cholesky decomposition matrix equal, so that a single variance will be estimated for this crossed random effect, which is represented by numerous dummy variables (numerous random effects) at the third level. */ constraint define 1 rater1 = rater2 constraint define 2 rater2 = rater3 constraint define 3 rater3 = rater4 /* As an aside, you can use the constraints below in lieu of -gllamm-'s -nocorrel- option; I've chosen the latter in this illustration. */ constraint define 4 [con2_2_1]_cons = 0 constraint define 5 [con2_3_1]_cons = 0 constraint define 6 [con2_3_2]_cons = 0 constraint define 7 [con2_4_1]_cons = 0 constraint define 8 [con2_4_2]_cons = 0 /* Since the crossed random effect that's assigned to the third level is represented by a bunch of dummy variables, you'll need to create a constant here, too, in order to define (identify) the third level's single random effect (the crossed random effect that you're assigning to the third level of the hierarchy). You could go ahead and use the same constant that you've created for the second level's equation. But the printout would be confusing, so create another constant with a more appropriate name for the random effect at this level. */ generate byte judges = 1 /* Estimate the model; I've chosen three integration points in the interests of time--it still takes a while. */ gllamm score, i(target judges) nrf(1, 4) eqs(k r1 r2 r3 r4) nocorrel /// constraints(1 2 3) nip(3) adapt trace allc /* If your'e willing to wait, uncomment the next two lines. matrix A = e(b) gllamm score, i(target judges) nrf(1, 4) eqs(k r1 r2 r3 r4) nocorrel /// constraints(1 2 3) adapt from(A) skip trace allc */ matrix list e(chol) exit ---------------------------------------------------------------------------- From: "Glance, Laurent" <Laurent_Glance@urmc.rochester.edu> Subject: st: RE: cross-classified random effects Date: Tue, 23 Mar 2004 07:05:19 -0500 I assume that your outcome variable is binary. Gllaam will not handle cross-classified data structures. MLWIN can handle cross-classified models, but the syntax for cross-classified models is not well documented. I have found PROC GLIMMIX to work well with cross-classified data structures. The best reference for PROC GLIMMIX is SAS System for Mixed Models by Ramon Littell - available through the SAS web site. Larry -----Original Message----- From: Do, Phoenix [mailto:phoenix@rand.org] Sent: Monday, March 22, 2004 6:42 PM To: statalist@hsphsun2.harvard.edu Subject: st: cross-classified random effects Hello, I am trying to model risk behaviors for IDUs. Since my data is hierarchical in nature, I want to apply a multilevel model using gllamm. My dataset consists of an unbalanced panel in which some people are observed only once while others are observed 2 to 10 times. We want to account for "neighborhood" characteristics so we have neighborhood level variables in our model. So level 1 would be the individual level time series data. Level 2 would be person level. Level 3 would be the neighborhood. However, these people move and are not necessarily in the same neighborhood throughout our study. I believe what I need to do is use a crossed random effects model. Can you do this in gllaam? And if so, how? I haven't been able to find any reference to this in the manual. Thank you for any help you can offer, Phoenix * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

- Prev by Date:
**st: fixed effects Tobit** - Next by Date:
**st: RE: Re: Computing mfx** - Previous by thread:
**st: fixed effects Tobit** - Next by thread:
**st: computing percentages** - Index(es):

© Copyright 1996–2017 StataCorp LLC | Terms of use | Privacy | Contact us | What's new | Site index |