[R-sig-ME] level 1 variance-covariance structure
Sebastián Daza
sebastian.daza at gmail.com
Tue Apr 12 15:42:34 CEST 2011
Thank you for your reply Thierry...
Increasing the number of iterations doesn't work:
m3a <- lme(attit ~ 1 + age13 , data=data, random= ~ age13 | id,
correlation = corAR1(, form = ~ ind | id), control=list(maxIter=1000,
msMaxIter=1000, niterEM=1000))
Error in lme.formula(attit ~ 1 + age13, data = data, random = ~age13 | :
nlminb problem, convergence error code = 1
message = function evaluation limit reached without convergence (9)
I have attached my database. I don't know if it is a problem of my model
or a limitation of lme function.
The best!
Sebastian.
On 4/12/2011 6:25 AM, ONKELINX, Thierry wrote:
> Dear Sebastian,
>
> You don't need to create dummy variables your selve.
>
> You can write m2a<- lme(attit ~ 1 + age13 , data=data, random= ~ 0 + ind1+ ind2+ ind3+ ind4+ ind5 | id, method="REML") as
>
> m2a<- lme(attit ~ 1 + age13 , data=data, random= ~ 0 + factor(ind) | id, method="REML")
>
> Or if ind is an indicator for age13:
>
> m2a<- lme(attit ~ 1 + age13 , data=data, random= ~ 0 + factor(age13) | id, method="REML")
>
> Have a look at lmeControl() to increase the number of iterations.
>
> Best regards,
>
> Thierry
>
> ----------------------------------------------------------------------------
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek
> team Biometrie& Kwaliteitszorg
> Gaverstraat 4
> 9500 Geraardsbergen
> Belgium
>
> Research Institute for Nature and Forest
> team Biometrics& Quality Assurance
> Gaverstraat 4
> 9500 Geraardsbergen
> Belgium
>
> tel. + 32 54/436 185
> Thierry.Onkelinx at inbo.be
> www.inbo.be
>
> To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of.
> ~ Sir Ronald Aylmer Fisher
>
> The plural of anecdote is not data.
> ~ Roger Brinner
>
> The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.
> ~ John Tukey
>
>
>> -----Oorspronkelijk bericht-----
>> Van: r-sig-mixed-models-bounces at r-project.org
>> [mailto:r-sig-mixed-models-bounces at r-project.org] Namens
>> Sebastián Daza
>> Verzonden: maandag 11 april 2011 18:44
>> Aan: R-SIG-Mixed-Models at r-project.org
>> Onderwerp: [R-sig-ME] level 1 variance-covariance structure
>>
>> Hi everyone,
>> I am trying to reproduce some results models from HLM (HMLM)
>> to contrast different specifications of level 1
>> variance-covariance, but I get convergence errors. I would
>> like to know if there are any problems with my model specification...
>>
>>
>> # database structure
>>
>> head(data[,c(1,2,6, 9:13,17)])
>> id attit age13 ind1 ind2 ind3 ind4 ind5 ind
>> 1 3 0.11 -2 1 0 0 0 0 1
>> 2 3 0.20 -1 0 1 0 0 0 2
>> 3 3 0.00 0 0 0 1 0 0 3
>> 4 3 0.00 1 0 0 0 1 0 4
>> 5 3 0.11 2 0 0 0 0 1 5
>> 6 8 0.29 -2 1 0 0 0 0 1
>>
>> # attit is a deviant measure and ind variables indicate
>> different waves # following some examples of snijders and
>> bosker's book, I get the unrestricted model:
>>
>> > m2a<- lme(attit ~ 1 + age13 , data=data, random= ~ 0 +
>> ind1+ ind2+
>> ind3+ ind4+ ind5 | id, method="REML")
>>
>> > summary(m2a)
>> Linear mixed-effects model fit by REML
>> Data: data
>> AIC BIC logLik
>> -326.2096 -236.5348 181.1048
>>
>> Random effects:
>> Formula: ~0 + ind1 + ind2 + ind3 + ind4 + ind5 | id
>> Structure: General positive-definite, Log-Cholesky parametrization
>> StdDev Corr
>> ind1 0.17219431 ind1 ind2 ind3 ind4
>> ind2 0.19789253 0.493
>> ind3 0.25942942 0.425 0.544
>> ind4 0.28354459 0.442 0.442 0.723
>> ind5 0.29097082 0.498 0.474 0.639 0.808
>> Residual 0.07457025
>>
>> Fixed effects: attit ~ 1 + age13
>> Value Std.Error DF t-value p-value
>> (Intercept) 0.3210558 0.012832840 839 25.01829 0
>> age13 0.0593529 0.004716984 839 12.58282 0
>> Correlation:
>> (Intr)
>> age13 0.504
>>
>> Standardized Within-Group Residuals:
>> Min Q1 Med Q3 Max
>> -1.46371871 -0.27170442 -0.04080686 0.26239553 1.69883910
>>
>> Number of Observations: 1079
>> Number of Groups: 239
>>
>> # variance-covariance matrix
>>
>> > extract.lme.cov2(m2a,data)$V[[6]]
>> 25 26 27 28 29
>> 25 0.03521160 0.01681647 0.01899029 0.02159300 0.02494013
>> 26 0.01681647 0.04472218 0.02793174 0.02481343 0.02727012
>> 27 0.01899029 0.02793174 0.07286434 0.05318967 0.04823107
>> 28 0.02159300 0.02481343 0.05318967 0.08595826 0.06667139
>> 29 0.02494013 0.02727012 0.04823107 0.06667139 0.09022474
>>
>> # I get the same results than unrestricted model in HLM
>>
>> # When I try to get the same unrestricted model using "corStruc"
>> commands in lme, I get a convergence problem. Am I
>> reproducing the model m2a?
>>
>> > m2b<- lme(attit ~ 1 + age13 , data=data, random= ~ age13
>> | id, correlation = corSymm(, form = ~ ind | id)) Error in
>> lme.formula(attit ~ 1 + age13, data = data, random = ~age13 | :
>> nlminb problem, convergence error code = 1
>> message = iteration limit reached without convergence (9)
>>
>> # When I try to get an autoregressive model, I get again a
>> convergence problem.
>>
>> > m3a<- lme(attit ~ 1 + age13 , data=data, random= ~ age13
>> | id, correlation = corAR1(, form = ~ ind | id)) Error in
>> lme.formula(attit ~ 1 + age13, data = data, random = ~age13 | :
>> nlminb problem, convergence error code = 1
>> message = iteration limit reached without convergence (9)
>>
>> Does anyone know how I can solve this?
>> Thank you in advance.
>>
>> --
>> Sebastián Daza
>> sebastian.daza at gmail.com
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
--
Sebastián Daza
sebastian.daza at gmail.com
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