[R-sig-ME] Does corSymm() require balanced data?
Ben Bolker
bbo|ker @end|ng |rom gm@||@com
Mon Mar 15 18:05:12 CET 2021
On 3/15/21 10:56 AM, Tip But wrote:
> 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)?
Balance is probably less important than the total number with 4
observations. If you had 100 subjects with 3 times and 20 subjects with
4 times you'd probably be fine.
>
> ***"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?
I believe that form = ~1|id uses the order of the observations in the
data set as the time index, and the grouping variable from the random
effect as the grouping variable, so these should indeed be equivalent (I
think the documentation should state this, but I haven't checked)
If you **really** want an answer you can tell R to return it anyway:
use control=lmeControl(returnObject=TRUE), but I wouldn't trust it.
It's hard to find another mixed-model package in R that can handle
this case (unstructured correlation, homogeneous variance).
>
> 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
>>
>>
>> ///////////////////////////////////////////////////////////////////////////////////////////
>> To call in the statistician after the experiment is done may be no more
>> 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
>> The combination of some data and an aching desire for an answer does not
>> 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]]
>>>
>>> _______________________________________________
>>> R-sig-mixed-models using r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>
>
> [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-mixed-models using r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
More information about the R-sig-mixed-models
mailing list