R help - Feb 2010 - lmer - error asMethod(object) : matrix is not symmetric

Dear R-help,

I  am having problems using package lme4.

I am trying to analyse the effect of a continuous variable (Dist_NV)
on a count data response variable (SR_SUN) using Poisson error
distribution. However, when I run the model:

summary(lmer((SR_SUN)~Dist_NV + (1|factor(Farm_code)) ,
family=poisson, REML=FALSE))

1 error message and 1 warning message show up:

in asMethod(object) : matrix is not symmetric [1,2]
In addition: Warning message:
In mer_finalize(ans) : singular convergence (7)

A model including  Dist_NV together with other variables runs with no problems.
What am I doing wrong?

Thank you,

Luisa

Dear R users,

I  am having problems using package lme4.

I am trying to analyse the effect of a continuous variable (Dist_NV)
on a count data response variable (SR_SUN) using Poisson error
distribution. However, when I run the model:

summary(lmer((SR_SUN)~Dist_NV + (1|factor(Farm_code)) ,
family=poisson, REML=FALSE))

1 error message and 1 warning message show up:

in asMethod(object) : matrix is not symmetric [1,2]
In addition: Warning message:
In mer_finalize(ans) : singular convergence (7)

A model including  Dist_NV together with other variables runs with no problems.
What am I doing wrong?

Thank you,

Luisa


-- 
Luisa Carvalheiro, PhD
Southern African Biodiversity Institute, Kirstenbosch Research Center, Claremont
& University of Pretoria
Postal address - SAWC Pbag X3015 Hoedspruit 1380, South Africa
telephone - +27 (0) 790250944
Carvalheiro at sanbi.org
lgcarvalheiro at gmail.com

This is similar to another question on the list today.

On Tue, Feb 16, 2010 at 4:39 AM, Luisa Carvalheiro
<lgcarvalheiro at gmail.com> wrote:
> Dear R users,
>
> I ?am having problems using package lme4.
>
> I am trying to analyse the effect of a continuous variable (Dist_NV)
> on a count data response variable (SR_SUN) using Poisson error
> distribution. However, when I run the model:
>
> summary(lmer((SR_SUN)~Dist_NV + (1|factor(Farm_code)) ,
> family=poisson, REML=FALSE))
>
> 1 error message and 1 warning message show up:
>
> in asMethod(object) : matrix is not symmetric [1,2]
> In addition: Warning message:
> In mer_finalize(ans) : singular convergence (7)
So the first thing to do is to include the optional argument verbose TRUE in the
call to lmer.  (Also, REML = FALSE is ignored for
Generalized Linear Mixed Models and can be omitted. although there is
no harm in including it.)

You need to know where the optimizer is taking the parameter values
before you can decide why.

P.S. Questions like this will probably be more readily answered on the
R-SIG-Mixed-Models mailing list.
> A model including ?Dist_NV together with other variables runs with no
problems.
> What am I doing wrong?
>
> Thank you,
>
> Luisa
>
>
> --
> Luisa Carvalheiro, PhD
> Southern African Biodiversity Institute, Kirstenbosch Research Center,
Claremont
> & University of Pretoria
> Postal address - SAWC Pbag X3015 Hoedspruit 1380, South Africa
> telephone - +27 (0) 790250944
> Carvalheiro at sanbi.org
> lgcarvalheiro at gmail.com
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

Dear Douglas,

Thank you for your reply.
Just some extra info on the dataset: In my case Number of obs is 33,
and number of groups of factor(Farm_code) is 12.
This is the information on iterations I get:

summary(lmer(round(SR_SUN)~Dist_NV + (1|factor(Farm_code)) ,
family=poisson, verbose =TRUE))
  0:     60.054531:  1.06363  2.14672 -0.000683051
  1:     60.054531:  1.06363  2.14672 -0.000683051
Error in asMethod(object) : matrix is not symmetric [1,2]
In addition: Warning message:
In mer_finalize(ans) : singular convergence (7)


When I run a similar model (exp variable Dist_hives) the number of
iterations is 11:

 summary(lmer(round(SR_SUN)~Dist_hives + (1|factor(Farm_code)) ,
family=poisson, verbose =TRUE))
  0:     61.745238: 0.984732  1.63769 0.000126484
  1:     61.648229: 0.984731  1.63769 -2.08637e-05
  2:     61.498777: 0.984598  1.63769 4.11867e-05
  3:     47.960908: 0.381062  1.63585 6.77029e-05
  4:     46.223789: 0.250732  1.66727 8.31854e-05
  5:     46.222223: 0.250732  1.66727 6.97790e-05
  6:     46.216710: 0.250730  1.66727 7.60560e-05
  7:     46.168835: 0.230386  1.64883 9.16430e-05
  8:     46.165955: 0.228062  1.65658 8.70694e-05
  9:     46.165883: 0.228815  1.65737 8.63400e-05
 10:     46.165883: 0.228772  1.65734 8.63698e-05
 11:     46.165883: 0.228772  1.65734 8.63701e-05



I am very confused with the fact that it runs with Dist_hives and not
with Dist_NV. Both variables are distance values, the first having no
obvious relation with the response variable and the second (Dist_NV)
seems to have a negative effect on SR_SUN.

Does this information helps identifying the problem with my data/analysis?

Thank you,

Luisa




On Tue, Feb 16, 2010 at 5:35 PM, Douglas Bates <bates at stat.wisc.edu>
wrote:
> This is similar to another question on the list today.
>
> On Tue, Feb 16, 2010 at 4:39 AM, Luisa Carvalheiro
> <lgcarvalheiro at gmail.com> wrote:
>> Dear R users,
>>
>> I ?am having problems using package lme4.
>>
>> I am trying to analyse the effect of a continuous variable (Dist_NV)
>> on a count data response variable (SR_SUN) using Poisson error
>> distribution. However, when I run the model:
>>
>> summary(lmer((SR_SUN)~Dist_NV + (1|factor(Farm_code)) ,
>> family=poisson, REML=FALSE))
>>
>> 1 error message and 1 warning message show up:
>>
>> in asMethod(object) : matrix is not symmetric [1,2]
>> In addition: Warning message:
>> In mer_finalize(ans) : singular convergence (7)
>
> So the first thing to do is to include the optional argument verbose >
TRUE in the call to lmer. ?(Also, REML = FALSE is ignored for
> Generalized Linear Mixed Models and can be omitted. although there is
> no harm in including it.)
>
> You need to know where the optimizer is taking the parameter values
> before you can decide why.
>
> P.S. Questions like this will probably be more readily answered on the
> R-SIG-Mixed-Models mailing list.
>
>> A model including ?Dist_NV together with other variables runs with no
problems.
>> What am I doing wrong?
>>
>> Thank you,
>>
>> Luisa
>>
>>
>> --
>> Luisa Carvalheiro, PhD
>> Southern African Biodiversity Institute, Kirstenbosch Research Center,
Claremont
>> & University of Pretoria
>> Postal address - SAWC Pbag X3015 Hoedspruit 1380, South Africa
>> telephone - +27 (0) 790250944
>> Carvalheiro at sanbi.org
>> lgcarvalheiro at gmail.com
>>
>> ______________________________________________
>> R-help at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>


-- 
Luisa Carvalheiro, PhD
Southern African Biodiversity Institute, Kirstenbosch Research Center, Claremont
& University of Pretoria
Postal address - SAWC Pbag X3015 Hoedspruit 1380, South Africa
telephone - +27 (0) 790250944
Carvalheiro at sanbi.org
lgcarvalheiro at gmail.com

On Tue, Feb 16, 2010 at 10:54 AM, Luisa Carvalheiro
<lgcarvalheiro at gmail.com> wrote:
> Dear Douglas,
>
> Thank you for your reply.
> Just some extra info on the dataset: In my case Number of obs is 33,
> and number of groups of factor(Farm_code) is 12.
> This is the information on iterations I get:
>
> summary(lmer(round(SR_SUN)~Dist_NV + (1|factor(Farm_code)) ,
> family=poisson, verbose =TRUE))
> ?0: ? ? 60.054531: ?1.06363 ?2.14672 -0.000683051
> ?1: ? ? 60.054531: ?1.06363 ?2.14672 -0.000683051
> Error in asMethod(object) : matrix is not symmetric [1,2]
> In addition: Warning message:
> In mer_finalize(ans) : singular convergence (7)
> When I run a similar model (exp variable Dist_hives) the number of
> iterations is 11:
> ?summary(lmer(round(SR_SUN)~Dist_hives + (1|factor(Farm_code)) ,
> family=poisson, verbose =TRUE))
> ?0: ? ? 61.745238: 0.984732 ?1.63769 0.000126484
> ?1: ? ? 61.648229: 0.984731 ?1.63769 -2.08637e-05
> ?2: ? ? 61.498777: 0.984598 ?1.63769 4.11867e-05
> ?3: ? ? 47.960908: 0.381062 ?1.63585 6.77029e-05
> ?4: ? ? 46.223789: 0.250732 ?1.66727 8.31854e-05
> ?5: ? ? 46.222223: 0.250732 ?1.66727 6.97790e-05
> ?6: ? ? 46.216710: 0.250730 ?1.66727 7.60560e-05
> ?7: ? ? 46.168835: 0.230386 ?1.64883 9.16430e-05
> ?8: ? ? 46.165955: 0.228062 ?1.65658 8.70694e-05
> ?9: ? ? 46.165883: 0.228815 ?1.65737 8.63400e-05
> ?10: ? ? 46.165883: 0.228772 ?1.65734 8.63698e-05
> ?11: ? ? 46.165883: 0.228772 ?1.65734 8.63701e-05
> I am very confused with the fact that it runs with Dist_hives and not
> with Dist_NV. Both variables are distance values, the first having no
> obvious relation with the response variable and the second (Dist_NV)
> seems to have a negative effect on SR_SUN.
As you say, Dist_hives has very little relationship to the response
variable.  The two fixed-effects coefficients are the last two
parameters in the iteration output (the first parameter is the
standard deviation of the random effects).  So the slope with respect
to Dist_hives for the linear predictor is 0.0000863.  Either you have
very large magnitudes of Dist_hives or that variable does not have
much predictive power.

For the second (Dist_NV) variable, the optimization algorithm is not
able to make progress from the starting estimates.  This may be an
indication that the problem is badly scaled.  Are the values of
Dist_NV very large?  If so, you may want to change the unit (say from
meters to kilometers) so the values are much smaller.

It may also help to use a starting estimate for the standard deviation
of the random effects derived from the other model.  That is, include
start = 0.22 in the call to lmer.
> Does this information helps identifying the problem with my data/analysis?
>
> Thank you,
>
> Luisa
>
>
>
>
> On Tue, Feb 16, 2010 at 5:35 PM, Douglas Bates <bates at
stat.wisc.edu> wrote:
>> This is similar to another question on the list today.
>>
>> On Tue, Feb 16, 2010 at 4:39 AM, Luisa Carvalheiro
>> <lgcarvalheiro at gmail.com> wrote:
>>> Dear R users,
>>>
>>> I ?am having problems using package lme4.
>>>
>>> I am trying to analyse the effect of a continuous variable
(Dist_NV)
>>> on a count data response variable (SR_SUN) using Poisson error
>>> distribution. However, when I run the model:
>>>
>>> summary(lmer((SR_SUN)~Dist_NV + (1|factor(Farm_code)) ,
>>> family=poisson, REML=FALSE))
>>>
>>> 1 error message and 1 warning message show up:
>>>
>>> in asMethod(object) : matrix is not symmetric [1,2]
>>> In addition: Warning message:
>>> In mer_finalize(ans) : singular convergence (7)
>>
>> So the first thing to do is to include the optional argument verbose
>> TRUE in the call to lmer. ?(Also, REML = FALSE is ignored for
>> Generalized Linear Mixed Models and can be omitted. although there is
>> no harm in including it.)
>>
>> You need to know where the optimizer is taking the parameter values
>> before you can decide why.
>>
>> P.S. Questions like this will probably be more readily answered on the
>> R-SIG-Mixed-Models mailing list.
>>
>>> A model including ?Dist_NV together with other variables runs with
no problems.
>>> What am I doing wrong?
>>>
>>> Thank you,
>>>
>>> Luisa
>>>
>>>
>>> --
>>> Luisa Carvalheiro, PhD
>>> Southern African Biodiversity Institute, Kirstenbosch Research
Center, Claremont
>>> & University of Pretoria
>>> Postal address - SAWC Pbag X3015 Hoedspruit 1380, South Africa
>>> telephone - +27 (0) 790250944
>>> Carvalheiro at sanbi.org
>>> lgcarvalheiro at gmail.com
>>>
>>> ______________________________________________
>>> R-help at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>
>
>
>
> --
> Luisa Carvalheiro, PhD
> Southern African Biodiversity Institute, Kirstenbosch Research Center,
Claremont
> & University of Pretoria
> Postal address - SAWC Pbag X3015 Hoedspruit 1380, South Africa
> telephone - +27 (0) 790250944
> Carvalheiro at sanbi.org
> lgcarvalheiro at gmail.com
>