[R-sig-ME] Specifying a (simple?) two level model
Hans Ekbrand
hans at sociologi.cjb.net
Thu Jun 30 08:49:39 CEST 2011
Hi this is my first post to the list. Am new to mixed models, but I
think I have managed to specify my rather simple modelling problem
correct. The problem I have is that the computation never seems to
finish (I waited for 10 hours before giving up).
I am trying to model how risks of poverty vary with labour market
position, while letting the effects of labour market position vary
over countries.
Here is a sample of the dataset, if you want to try it out
> print(load(url("http://code.cjb.net/temp/pov.temp.RData")))
[1] "poverty.risks"
> str(poverty.risks)
'data.frame': 161348 obs. of 3 variables:
$ poverty.third.year: logi FALSE FALSE FALSE FALSE TRUE FALSE ...
$ country : Factor w/ 22 levels "sweden","unitedkingdom",..: 1 1 1 1 1 1 1 1 1 1 ...
$ cluster : Factor w/ 22 levels "Unemployed - Unemployed",..: 16 20 16 16 18 20 16 1 16 2 ...
Labour market position is a factor that summaries a history of
labourmarket positions for three year, where "Unemploed - Unemployed"
means that the individual was unemployed at time0 and at time1.
Here my specification:
my.fit <- glmer(poverty.third.year ~ cluster + (1 + cluster | country), family = binomial("logit"), data = poverty.risks)
I saw, in Bates Chapter 2, that you could split the random terms in (1 | cluster) + (1 | country). Also, am not sure wether or not to include cluster as fixed term. If I split the random terms and skip cluster as a fixed term, then the computation takes only a few seconds.
my.fit <- glmer(poverty.third.year ~ 1 + (1 | cluster) + (1 | country), family = binomial("logit"), data = poverty.risks)
summary(fit)
Generalized linear mixed model fit by the Laplace approximation
Formula: poverty.third.year ~ 1 + (1 | cluster) + (1 | country)
Data: poverty.risks
AIC BIC logLik deviance
103922 103952 -51958 103916
Random effects:
Groups Name Variance Std.Dev.
cluster (Intercept) 0.54046 0.73516
country (Intercept) 0.17247 0.41530
Number of obs: 161348, groups: cluster, 22; country, 22
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.0760 0.1807 -11.49 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
My understanding, which I hope is wrong, is that this model does not
compute country specific poverty risks for each cluster.
If the first model is the wright one for me, then for how long would
it be reasonable to wait the computation to terminate?
--
Hans Ekbrand <hans at sociologi.cjb.net>
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