errors with lme4

Tue, Nov 22, 2011 1:12 AM

Dear Alessio,

A few remarks.

- R-sig-mixed models is a better list for this kind of questions
- use the glmer() function if you want logistic or poisson regression
- the error you are getting is an indication that the model is too complex for the data
- watch for colinearity in the covariates

Best regards,

Thierry

-----Oorspronkelijk bericht-----
Van: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
Namens Alessio Unisi
Verzonden: maandag 21 november 2011 18:20
Aan: r-help at r-project.org
Onderwerp: [R] errors with lme4
Urgentie: Hoog

Dear list,
i'm a new R user, so I apologize if the topic is already being addressed by some
other user.

I'm trying to determine if the reproductive success of a species of bird is related
to a list of covariates.

These are the covariates:
?    elev: elevation of nest (meters)
?    seadist: distance from the sea (meters)
?    meanterranova: records of temperature
?    minpengS1: records of temperature
?    wchillpengS1: records of temperature
?    minpengS2: records of temperature
?    wchillpengS2: records of temperature
?    nnd: nearest neighbour distance
?    npd: nearest penguin distance
?    eggs: numbers of eggs
?    lay: laying date (julian calendar)
?    hatch: hatching date (julian calendar)
I have some NAs in the data.

I want to test the model with all the variable then i want to remove some, but
the ideal model:
GLM.1 <-lmer(fledgesucc ~ +lay +hatch +elev +seadist +nnd +npd
+meanterranova +minpengS1 +minpengS2 +wchillpengS1 +wchillpengS2
+(1|territory), family=binomial(logit), data=fledge)

doesn't work because of these errors:
'Warning message: In mer_finalize(ans) : gr cannot be computed at initial par
(65)'.
"matrix is not symmetric [1,2]"

If i delete one or more of the T records (i.e. minpengS2 +wchillpengS2) the
model works...below and example:

 GLM.16 <-lmer(fledgesucc ~ lay +hatch +elev +seadist +nnd +npd
+meanterranova +minpengS1 +(1|territory), family=binomial(logit),
data=fledge)

 > summary(GLM.16)

Generalized linear mixed model fit by the Laplace approximation
Formula: fledgesucc ~ lay + hatch + elev + seadist + nnd + npd +
meanterranova +      minpengS1 + (1 | territory)
   Data: fledge
 AIC   BIC logLik deviance
 174 204.2    -77      154
Random effects:
 Groups    Name        Variance Std.Dev.
 territory (Intercept) 0.54308  0.73694
Number of obs: 152, groups: territory, 96

Fixed effects:
               Estimate Std. Error z value Pr(>|z|)
(Intercept)   14.136846  14.510089   0.974    0.330
lay           -0.007642   0.267913  -0.028    0.977
hatch         -0.025947   0.267318  -0.097    0.923
elev           0.007481   0.027765   0.270    0.788
seadist       -0.004277   0.004550  -0.940    0.347
nnd           -0.035535   0.026504  -1.341    0.180
npd            0.003788   0.005521   0.686    0.493
meanterranova  1.242570   1.426158   0.871    0.384
minpengS1     -0.399852   0.418722  -0.955    0.340

Correlation of Fixed Effects:
            (Intr) lay    hatch  elev   seadst nnd    npd    mntrrn
lay          0.411
hatch       -0.515 -0.993
elev        -0.015  0.141 -0.135
seadist     -0.003 -0.023  0.019 -0.440
nnd         -0.061  0.066 -0.059 -0.020  0.231
npd          0.033 -0.108  0.100  0.298 -0.498 -0.338
meanterranv  0.459 -0.118  0.075 -0.061  0.014 -0.048  0.130
minpengS1   -0.540  0.015  0.035  0.032  0.000  0.039 -0.086 -0.970


I've attached an example of my dataset only 15 rows just to see the
dataset. Let me know if you need more informations.

Thanks in advance for your help and advices!
regards

--
Alessio Franceschi
Phd student
Dipartimento di Scienze Ambientali "G. Sarfatti"
Universit? di Siena
Via P.A. Mattioli, 8  - 53100 Siena (Italy)
Cell. +393384431806
email: franceschi6 at unisi.it; alfranceschi at alice.it

errors with lme4

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