Error in initializePtr() : updateMu: Size mismatch - R-SIG-mixed-models

Fri, May 1, 2020 12:00 PM #

Hallo,

I want to run a linear mixed model featuring 5 response variables and only
one predictor variable. I also have two random effects in the model. I am
using this code:

lm1 <- lmer(cbind(transpopbees, transbrood, transhoney, transpollen,
transeggs) ~ Varroa+(1|Site)+(1|Colony),data=pollencolony)

However I get this error message:

Error in initializePtr() : updateMu: Size mismatch

What does this mean and what am I doing wrong?

Thanks

Voeten, C.C.

Fri, May 1, 2020 12:25 PM #

Hi Pamela,

lmer/glmer do not support models with multiple dependent variables via the cbind() syntax. An alternative approach is to convert your data to long format and run the model in the following way:

value ~ 0+variable/Varroa + (0+variable|Site) + (0+variable|Colony)

with 'variable' the column containing "transpopbees", "transbrood", ..., and 'value' the column containing their values.
Alternatively, function gam() from package mgcv can fit your model. You would then use something like:

gam(list(transpopbees ~ Varroa + s(Site,bs='re') + s(Colony,bs='re'), transbrood ~ Varroa + s(Site,bs='re') + s(Colony,bs='re'), .....),family=mvn(5),data=pollencolony)

Hope this helps,
Cesko

-----Original Message-----
From: R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org> On Behalf Of Pamela Ochungo
Sent: Friday, May 1, 2020 9:00 PM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Error in initializePtr() : updateMu: Size mismatch

Hallo,

I want to run a linear mixed model featuring 5 response variables and only one predictor variable. I also have two random effects in the model. I am using this code:

lm1 <- lmer(cbind(transpopbees, transbrood, transhoney, transpollen,
transeggs) ~ Varroa+(1|Site)+(1|Colony),data=pollencolony)

However I get this error message:

Error in initializePtr() : updateMu: Size mismatch

What does this mean and what am I doing wrong?

Thanks


_______________________________________________
R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

Pamela Ochungo

Fri, May 1, 2020 1:15 PM #

Hi Cesko,

Thanks for your reply! I have tried the second option, function gam() from
mgcv. However I get an unexpected result as below:

Family: Multivariate normal
Link function:

Formula:
transpopbees ~ Varroa + s(Site, bs = "re") + s(Colony,
    bs = "re")
transbrood ~ Varroa + s(Site, bs = "re") + s(Colony, bs = "re")
transhoney ~ Varroa + s(Site, bs = "re") + s(Colony, bs = "re")
transpollen ~ Varroa + s(Site, bs = "re") + s(Colony, bs = "re")
transeggs ~ Varroa + s(Site, bs = "re") + s(Colony, bs = "re")

Estimated degrees of freedom:
0.0001 0.7631 0.0000 0.0003 1.7061 0.0003 1.7192
0.0002 0.0000 0.0003  total = 29.19

REML score: 547.659

How do I interpret this? I was rather hoping to get a result showing model
coefficients and p-values for each dependent variable.


Question: Is it acceptable to carry out LMM (lmer) for each of the 5
dependent variables separately against Varroa?

Cheers

Pamela

On Fri, May 1, 2020 at 10:25 PM Voeten, C.C. <c.c.voeten at hum.leidenuniv.nl>
wrote:

_______________________________________________
R-sig-mixed-models at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

Voeten, C.C.

Sat, May 2, 2020 2:48 AM #

Hi Pamela,

Re gam(): it looks like you merely didn't call summary() on your fitted model object?

Re separate models: whether or not this is appropriate depends entirely on your data and what you want to do with them. If you want to compare the different dependent variables to one another, or if you want to assume that there is correlation between them, then you need a multivariate model. But if the different dependent variables represent wholly separate measures and they are uncorrelated (or your substantive question permits you to leave such correlation out of the analysis), then I see no problem with running separate models instead.

Best,
Cesko

From: Pamela Ochungo <pamochungo at gmail.com> 
Sent: Friday, May 1, 2020 10:15 PM
To: Voeten, C.C. <c.c.voeten at hum.leidenuniv.nl>
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] Error in initializePtr() : updateMu: Size mismatch

Hi Cesko,

Thanks for your?reply! I have tried the second option, function gam() from mgcv. However I get an unexpected result as below:

Family: Multivariate normal 
Link function: 

Formula:
transpopbees ~ Varroa + s(Site, bs = "re") + s(Colony, 
? ? bs = "re")
transbrood ~ Varroa + s(Site, bs = "re") + s(Colony, bs = "re")
transhoney ~ Varroa + s(Site, bs = "re") + s(Colony, bs = "re")
transpollen ~ Varroa + s(Site, bs = "re") + s(Colony, bs = "re")
transeggs ~ Varroa + s(Site, bs = "re") + s(Colony, bs = "re")

Estimated degrees of freedom:
0.0001 0.7631 0.0000 0.0003 1.7061 0.0003 1.7192 
0.0002 0.0000 0.0003 ?total = 29.19 

REML score: 547.659

How do I interpret this? I was rather hoping to get a result showing model coefficients and p-values for each dependent variable.


Question: Is it acceptable to carry out LMM (lmer) for each of the 5 dependent variables separately?against Varroa?

Cheers

Pamela

On Fri, May 1, 2020 at 10:25 PM Voeten, C.C. <mailto:c.c.voeten at hum.leidenuniv.nl> wrote:

Hi Pamela,

lmer/glmer do not support models with multiple dependent variables via the cbind() syntax. An alternative approach is to convert your data to long format and run the model in the following way:

value ~ 0+variable/Varroa + (0+variable|Site) + (0+variable|Colony)

with 'variable' the column containing "transpopbees", "transbrood", ..., and 'value' the column containing their values.
Alternatively, function gam() from package mgcv can fit your model. You would then use something like:

gam(list(transpopbees ~ Varroa + s(Site,bs='re') + s(Colony,bs='re'), transbrood ~ Varroa + s(Site,bs='re') + s(Colony,bs='re'), .....),family=mvn(5),data=pollencolony)

Hope this helps,
Cesko

-----Original Message-----
From: R-sig-mixed-models <mailto:r-sig-mixed-models-bounces at r-project.org> On Behalf Of Pamela Ochungo
Sent: Friday, May 1, 2020 9:00 PM
To: mailto:r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Error in initializePtr() : updateMu: Size mismatch

Hallo,

I want to run a linear mixed model featuring 5 response variables and only one predictor variable. I also have two random effects in the model. I am using this code:

lm1 <- lmer(cbind(transpopbees, transbrood, transhoney, transpollen,
transeggs) ~ Varroa+(1|Site)+(1|Colony),data=pollencolony)

However I get this error message:

Error in initializePtr() : updateMu: Size mismatch

What does this mean and what am I doing wrong?

Thanks

? ? ? ? [[alternative HTML version deleted]]

_______________________________________________
mailto:R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

Pamela Ochungo

Sat, May 2, 2020 3:11 AM #

Hi Cesko,

Thanks! I see my mistake now! I have called the summary and it works
perfectly well!

How would you visualize the results? Graphing residuals?

Cheers and thanks once again!

Pamela

On Sat, May 2, 2020 at 12:48 PM Voeten, C.C. <c.c.voeten at hum.leidenuniv.nl>
wrote:

Hi Pamela,

Re gam(): it looks like you merely didn't call summary() on your fitted
model object?

Re separate models: whether or not this is appropriate depends entirely on
your data and what you want to do with them. If you want to compare the
different dependent variables to one another, or if you want to assume that
there is correlation between them, then you need a multivariate model. But
if the different dependent variables represent wholly separate measures and
they are uncorrelated (or your substantive question permits you to leave
such correlation out of the analysis), then I see no problem with running
separate models instead.

Best,
Cesko

From: Pamela Ochungo <pamochungo at gmail.com>
Sent: Friday, May 1, 2020 10:15 PM
To: Voeten, C.C. <c.c.voeten at hum.leidenuniv.nl>
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] Error in initializePtr() : updateMu: Size mismatch

Hi Cesko,

Thanks for your reply! I have tried the second option, function gam() from
mgcv. However I get an unexpected result as below:

Family: Multivariate normal
Link function:

Formula:
transpopbees ~ Varroa + s(Site, bs = "re") + s(Colony,
    bs = "re")
transbrood ~ Varroa + s(Site, bs = "re") + s(Colony, bs = "re")
transhoney ~ Varroa + s(Site, bs = "re") + s(Colony, bs = "re")
transpollen ~ Varroa + s(Site, bs = "re") + s(Colony, bs = "re")
transeggs ~ Varroa + s(Site, bs = "re") + s(Colony, bs = "re")

Estimated degrees of freedom:
0.0001 0.7631 0.0000 0.0003 1.7061 0.0003 1.7192
0.0002 0.0000 0.0003  total = 29.19

REML score: 547.659

How do I interpret this? I was rather hoping to get a result showing model
coefficients and p-values for each dependent variable.


Question: Is it acceptable to carry out LMM (lmer) for each of the 5
dependent variables separately against Varroa?

Cheers

Pamela

On Fri, May 1, 2020 at 10:25 PM Voeten, C.C. <mailto:
c.c.voeten at hum.leidenuniv.nl> wrote:
Hi Pamela,

lmer/glmer do not support models with multiple dependent variables via the
cbind() syntax. An alternative approach is to convert your data to long
format and run the model in the following way:

value ~ 0+variable/Varroa + (0+variable|Site) + (0+variable|Colony)

with 'variable' the column containing "transpopbees", "transbrood", ...,
and 'value' the column containing their values.
Alternatively, function gam() from package mgcv can fit your model. You
would then use something like:

gam(list(transpopbees ~ Varroa + s(Site,bs='re') + s(Colony,bs='re'),
transbrood ~ Varroa + s(Site,bs='re') + s(Colony,bs='re'),
.....),family=mvn(5),data=pollencolony)

Hope this helps,
Cesko

-----Original Message-----
From: R-sig-mixed-models <mailto:r-sig-mixed-models-bounces at r-project.org>
On Behalf Of Pamela Ochungo
Sent: Friday, May 1, 2020 9:00 PM
To: mailto:r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Error in initializePtr() : updateMu: Size mismatch

Hallo,

I want to run a linear mixed model featuring 5 response variables and only
one predictor variable. I also have two random effects in the model. I am
using this code:

lm1 <- lmer(cbind(transpopbees, transbrood, transhoney, transpollen,
transeggs) ~ Varroa+(1|Site)+(1|Colony),data=pollencolony)

However I get this error message:

Error in initializePtr() : updateMu: Size mismatch

What does this mean and what am I doing wrong?

Thanks

        [[alternative HTML version deleted]]

_______________________________________________
mailto:R-sig-mixed-models at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

Voeten, C.C.

Sat, May 2, 2020 3:15 AM #

Hi Pamela,

I assume, since you mention graphing residuals, that you want to visualize for purposes of model checking? Then I recommend mgcv?s gam.check() function. (You can ignore the part where it checks the value of k, the basis dimension, as that is not relevant for your model.)

Best,
Cesko

From: Pamela Ochungo <pamochungo at gmail.com>
Sent: Saturday, May 2, 2020 12:11 PM
To: Voeten, C.C. <c.c.voeten at hum.leidenuniv.nl>
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] Error in initializePtr() : updateMu: Size mismatch

Hi Cesko,

Thanks! I see my mistake now! I have called the summary and it works perfectly well!

How would you visualize the results? Graphing residuals?

Cheers and thanks once again!

Pamela

On Sat, May 2, 2020 at 12:48 PM Voeten, C.C. <c.c.voeten at hum.leidenuniv.nl<mailto:c.c.voeten at hum.leidenuniv.nl>> wrote:

Hi Pamela,

Re gam(): it looks like you merely didn't call summary() on your fitted model object?

Re separate models: whether or not this is appropriate depends entirely on your data and what you want to do with them. If you want to compare the different dependent variables to one another, or if you want to assume that there is correlation between them, then you need a multivariate model. But if the different dependent variables represent wholly separate measures and they are uncorrelated (or your substantive question permits you to leave such correlation out of the analysis), then I see no problem with running separate models instead.

Best,
Cesko

From: Pamela Ochungo <pamochungo at gmail.com<mailto:pamochungo at gmail.com>>
Sent: Friday, May 1, 2020 10:15 PM
To: Voeten, C.C. <c.c.voeten at hum.leidenuniv.nl<mailto:c.c.voeten at hum.leidenuniv.nl>>
Cc: r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org>
Subject: Re: [R-sig-ME] Error in initializePtr() : updateMu: Size mismatch

Hi Cesko,

Thanks for your reply! I have tried the second option, function gam() from mgcv. However I get an unexpected result as below:

Family: Multivariate normal
Link function:

Formula:
transpopbees ~ Varroa + s(Site, bs = "re") + s(Colony,
    bs = "re")
transbrood ~ Varroa + s(Site, bs = "re") + s(Colony, bs = "re")
transhoney ~ Varroa + s(Site, bs = "re") + s(Colony, bs = "re")
transpollen ~ Varroa + s(Site, bs = "re") + s(Colony, bs = "re")
transeggs ~ Varroa + s(Site, bs = "re") + s(Colony, bs = "re")

Estimated degrees of freedom:
0.0001 0.7631 0.0000 0.0003 1.7061 0.0003 1.7192
0.0002 0.0000 0.0003  total = 29.19

REML score: 547.659

How do I interpret this? I was rather hoping to get a result showing model coefficients and p-values for each dependent variable.


Question: Is it acceptable to carry out LMM (lmer) for each of the 5 dependent variables separately against Varroa?

Cheers

Pamela

On Fri, May 1, 2020 at 10:25 PM Voeten, C.C. <mailto:c.c.voeten at hum.leidenuniv.nl<mailto:c.c.voeten at hum.leidenuniv.nl>> wrote:

Hi Pamela,

lmer/glmer do not support models with multiple dependent variables via the cbind() syntax. An alternative approach is to convert your data to long format and run the model in the following way:

value ~ 0+variable/Varroa + (0+variable|Site) + (0+variable|Colony)

with 'variable' the column containing "transpopbees", "transbrood", ..., and 'value' the column containing their values.
Alternatively, function gam() from package mgcv can fit your model. You would then use something like:

gam(list(transpopbees ~ Varroa + s(Site,bs='re') + s(Colony,bs='re'), transbrood ~ Varroa + s(Site,bs='re') + s(Colony,bs='re'), .....),family=mvn(5),data=pollencolony)

Hope this helps,
Cesko

-----Original Message-----
From: R-sig-mixed-models <mailto:r-sig-mixed-models-bounces at r-project.org<mailto:r-sig-mixed-models-bounces at r-project.org>> On Behalf Of Pamela Ochungo
Sent: Friday, May 1, 2020 9:00 PM
To: mailto:r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org>
Subject: [R-sig-ME] Error in initializePtr() : updateMu: Size mismatch

Hallo,

I want to run a linear mixed model featuring 5 response variables and only one predictor variable. I also have two random effects in the model. I am using this code:

lm1 <- lmer(cbind(transpopbees, transbrood, transhoney, transpollen,
transeggs) ~ Varroa+(1|Site)+(1|Colony),data=pollencolony)

However I get this error message:

Error in initializePtr() : updateMu: Size mismatch

What does this mean and what am I doing wrong?

Thanks


_______________________________________________
mailto:R-sig-mixed-models at r-project.org<mailto:R-sig-mixed-models at r-project.org> mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models