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Exponent random effect in nlmer

Dear Tim,

y centred on 0 and a valid range (0, 1) seems to be conflicting statements.

Here a some solutions depending on y

- y stems from a binomial process
     - use a binomial glmm.
- y is continuous and you are willing to transform y
    - 0 < y <  1
        - apply a logit transformation on y. lmer(plogis(y) ~ f + (1 | id) )
    - 0 <= y < 1
        - apply a log transformation on y. lmer(log(y) ~ f + (1 | id) )
    - 0 < y <= 1
        - apply a log transformation on 1 - y. lmer(log(1 - y) ~ f + (1 |
id) )
- y is continuous are not willing to transform y
   - use a beta regression with 0 and/or 1 inflation in case you have 0 or
1 in the data. Have a look at the gamlss package to fit this model.

Best regards,


ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

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

2016-10-11 11:29 GMT+02:00 Cole, Tim <tim.cole at ucl.ac.uk>: