Re: [R] Multilevel Modelling

2014-03-28 Thread Bert Gunter 
No, I don't think that will make any difference.

1) Post this to the r-sig-mixed-models list rather than here, as you
are likely to get a much better answer there.

2) Did you realize that treatment is a linear term in the fixed
effects portion, not a factor? If you don't understand the question,
then you need to work through an R tutorial and learn R. If you do,
proceed.

3) I believe that the problem is that treatment has only 3 levels.
First of all, it is nonsense to estimate a variance component based on
only 3 values; second, 1 df is already used up for the intercept and
slope for Time in the fixed effect, leaving essentially 2 to estimate
the variance (this is not strictly correct, since the model is
nonlinear; but linear approximations are used in fitting). So while I
don't think you're singular, you're as close as you can come, and that
is why convergence isn't achieved. Note that if you replace the fixed
effect term by just an intercept, you converge. Not that the variance
estimate means much.

4) As I said, you'll get better answers on the mixed models list,
including correction of any errors in my explanation. But this all
does suggest that you don't understand mixed models and probably
should consult a local statistical resource for help.

Cheers,
Bert

Bert Gunter
Genentech Nonclinical Biostatistics
(650) 467-7374

Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom.
H. Gilbert Welch




On Fri, Mar 28, 2014 at 9:01 AM, Patrick Coulombe
patrick.coulo...@gmail.com wrote:
 Have you tried running it using lmer() in lme4 instead, see if that helps?

 Patrick

 2014-03-27 6:21 GMT-06:00 Laura Thomas skagandboneg...@hotmail.com:
 Hi All,

 I am using R for the purpose of multilevel modelling for the first time. I 
 am trying to examine individuals interpersonal changes in the dependent 
 variable over time and how this varies between groups.

 I am using the following code:
 treat.lme1-lme(DependentVariable~Treatment*I(Time-1), 
 random=~1+I(Time-1)|Treatment, data=MLMworkbook2,na.action=na.omit)

 but receive the error message:
  Error in lme.formula(DependentVariable ~ Treatment * I(Time - 1), random = 
 ~1 +  : nlminb problem, convergence error code = 1 message = iteration limit 
 reached without convergence (10)

 Any help with this would be much appreciated.

 Many thanks,

 Laura

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Re: [R] Multilevel Modelling

2014-03-28 Thread Patrick Coulombe 
Have you tried running it using lmer() in lme4 instead, see if that helps?

Patrick

2014-03-27 6:21 GMT-06:00 Laura Thomas skagandboneg...@hotmail.com:
 Hi All,

 I am using R for the purpose of multilevel modelling for the first time. I am 
 trying to examine individuals interpersonal changes in the dependent variable 
 over time and how this varies between groups.

 I am using the following code:
 treat.lme1-lme(DependentVariable~Treatment*I(Time-1), 
 random=~1+I(Time-1)|Treatment, data=MLMworkbook2,na.action=na.omit)

 but receive the error message:
  Error in lme.formula(DependentVariable ~ Treatment * I(Time - 1), random = 
 ~1 +  : nlminb problem, convergence error code = 1 message = iteration limit 
 reached without convergence (10)

 Any help with this would be much appreciated.

 Many thanks,

 Laura

 structure(list(Participant = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
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[R] Multilevel Modelling

2014-03-27 Thread Laura Thomas 
Hi All,

I am using R for the purpose of multilevel modelling for the first time. I am 
trying to examine individuals interpersonal changes in the dependent variable 
over time and how this varies between groups.

I am using the following code:
treat.lme1-lme(DependentVariable~Treatment*I(Time-1), 
random=~1+I(Time-1)|Treatment, data=MLMworkbook2,na.action=na.omit)

but receive the error message:
 Error in lme.formula(DependentVariable ~ Treatment * I(Time - 1), random = ~1 
+  : nlminb problem, convergence error code = 1 message = iteration limit 
reached without convergence (10)”

Any help with this would be much appreciated.

Many thanks,

Laura

structure(list(Participant = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 
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2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
1L, 1L, 1L,