level 1 variance-covariance structure
Hi Sebastian, two things to think about here ... first, the error reported below refers to the function evaluation limit. You need to increase that as well as the number if iterations. The relevant argument for control is msMaxEval. Second, I've had models converge after 5000 iterations. Best wishes Andrew
On Tue, Apr 12, 2011 at 08:42:34AM -0500, Sebasti?n Daza wrote:
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
Sebasti?n 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. -- Sebasti?n Daza sebastian.daza at gmail.com
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-- Sebasti?n Daza sebastian.daza at gmail.com
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