[R-sig-ME] Does corSymm() require balanced data?

Tip But |@w|@wt @end|ng |rom gm@||@com
Mon Mar 15 15:56:03 CET 2021


Dear Thierry,

Thank you so much for your insightful comments. May I follow-up on them
below in-line:


***"You have too few subjects with 4 observations. Either drop those fourth
observations."

>>>> Does the above mean that for an unstructured residual correlation
matrix, the unique number of measurements (e.g., 3 times, 4 times etc.)
must have relatively equal sizes (e.g., 9 subjects with 3 times, 7 subjects
with 4 times)?

***"Or use a different correlation structure. E.g. an AR1:

fit_alt <- lme(opp ~ time * ccog, random = ~1 | id,
  correlation = corAR1(form = ~ time), data = dat)
"

>>>> In your above R code, is it necessary to use `corAR1(form = ~ time)`?
It seems `corAR1(form = ~1 | id)` gives the same result?

On Mon, Mar 15, 2021 at 2:37 AM Thierry Onkelinx <thierry.onkelinx using inbo.be>
wrote:

> Dear Joe,
>
> You have too few subjects with 4 observations. Either drop those fourth
> observations. Or use a different correlation structure. E.g. an AR1
>
> fit <- lme(
>   opp ~ time * ccog, random = ~1 | id,
>   correlation = corSymm(), data = dat, subset = time < 3
> )
>
> fit_alt <- lme(
>   opp ~ time * ccog, random = ~1 | id,
>   correlation = corAR1(form = ~ time), data = dat
> )
> Best regards,
>
>
> ir. Thierry Onkelinx
> Statisticus / Statistician
>
> Vlaamse Overheid / Government of Flanders
> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
> FOREST
> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> thierry.onkelinx using inbo.be
> Havenlaan 88 bus 73, 1000 Brussel
> www.inbo.be
>
>
> ///////////////////////////////////////////////////////////////////////////////////////////
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> 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
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> ensure that a reasonable answer can be extracted from a given body of data.
> ~ John Tukey
>
> ///////////////////////////////////////////////////////////////////////////////////////////
>
> <https://www.inbo.be>
>
>
> Op ma 15 mrt. 2021 om 03:27 schreef Tip But <fswfswt using gmail.com>:
>
>> Dear Members,
>>
>> In my longitudinal data below, the first couple of subjects were measured
>> 4
>> times but the rest of the subjects were measured 3 times (see data below).
>>
>> We intend to use an unstructured residual correlation matrix in
>> `nlme::lme()`. But our model fails to converge.
>>
>> Question: Given our data is unbalanced with respect to our grouping
>> variable (i.e., `id`), can we use ` corSymm()`? And if we do, what would
>> be
>> the dimensions of the resultant unstructured residual correlation matrix
>> for our data; a 3x3 or a 4x4 matrix?
>>
>> Thank you for your expertise,
>> Joe
>>
>> # Data and R Code
>> dat <- read.csv("https://raw.githubusercontent.com/hkil/m/master/un.csv")
>>
>> library(nlme)
>>
>> fit <- lme(opp~time*ccog, random = ~1|id, correlation=corSymm(form = ~ 1 |
>> id),
>>            data=dat)
>>
>> Error:
>>   nlminb problem, convergence error code = 1
>>   message = false convergence (8)
>>
>>         [[alternative HTML version deleted]]
>>
>> _______________________________________________
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>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>

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