Does corSymm() require balanced data?

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 at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

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Op ma 15 mrt. 2021 om 03:27 schreef Tip But <fswfswt at 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)

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