Greetings-
Using R 1.5.0 under linux and the latest MASS and nlme, I am trying to
develop a three-level (two levels of nesting) model with a dichotomous
oucome variable. The unconditional model is thus:
> doubt1.pql<-glmmPQL(fixed = r.info.doubt ~ 1, random = ~1 |
groupid/participantid,
+ family = binomial, data = fgdata.10statements.df)
iteration 1
iteration 2
iteration 3
iteration 4
iteration 5 > summary(doubt1.pql)
Linear mixed-effects model fit by maximum likelihood
Data: fgdata.10statements.df
AIC BIC logLik
35838.51 35864.14 -17915.26
Random effects:
Formula: ~1 | groupid
(Intercept)
StdDev: 0.9113356
Formula: ~1 | participantid %in% groupid
(Intercept) Residual
StdDev: 1.222692 0.8411629
Variance function:
Structure: fixed weights
Formula: ~invwt
Fixed effects: r.info.doubt ~ 1
Value Std.Error DF t-value p-value
(Intercept) -5.523606 0.3170107 4351 -17.42404 <.0001
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-0.54548030 -0.06825715 -0.05379631 -0.04567625 18.29733311
Number of Observations: 4480
Number of Groups:
groupid participantid %in% groupid
21 129
---
The most interesting question, though, is the effect of the dummy
variables is.prot, is.cath, is.union, is.sport, and is.busi. Each of these
is a dichotomous variable. I tried:
> doubt5.pql<-glmmPQL(random = ~ 1 | groupid/participantid,
+ fixed = r.info.doubt ~ is.prot + is.cath + is.union
+ is.sport + is.busi,
+ data = fgdata.10statements.df,
+ na.action=na.omit,
+ niter=50,
+ family = binomial)
(the version without niter=50 just failed to converge after 10
interations; adding niter=50 gives:
iteration 1
...
iteration 26
Error in solve.default(estimates[dimE[1] - (p:1), dimE[2] - (p:1), drop FALSE])
:
singular matrix `a' in solve
In addition: Warning message:
Algorithm did not converge in: (if (is.empty.model(mt)) glm.fit.null else
glm.fit)(x = X, y = Y,
---
Can someone help with interpreting this? What strategies might be fruitful
in gaining convergence? This particular outcome variable is very sparse
(28 positive values in an N of 4480); is that likely part of the problem?
Thanks.
----------------------------------------------------------------------
Andrew J Perrin - http://www.unc.edu/~aperrin
Assistant Professor of Sociology, U of North Carolina, Chapel Hill
clists at perrin.socsci.unc.edu * andrew_perrin (at) unc.edu
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