[R-sig-ME] Exponent random effect in nlmer
Thierry Onkelinx
thierry.onkelinx at inbo.be
Tue Oct 11 12:06:07 CEST 2016
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
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ensure that a reasonable answer can be extracted from a given body of data.
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2016-10-11 11:29 GMT+02:00 Cole, Tim <tim.cole op ucl.ac.uk>:
> I have a model of the form
> m1 <- lmer(y ~ f + (1 | id) )
> where y is a continuous variable centred on zero, f is a unordered factor
> with coefficients b such 0 < b < 1, and there is a signficant random
> subject intercept.
>
> The random intercept can lead to predicted values outside the valid range
> (0, 1). For this reason I'd like to reformulate the model as
> m2 <- nlmer(y ~ (f - 1) ^ exp(1 | id) ) (using a invalid but I hope
> obvious notation), where the random effect is now a power centred on 1.
> This would constrain the fitted values to be within c(0, 1).
>
> My question is: can this be done in nlmer, and if so how? Please can
> someone point me in the right direction?
>
> Thanks,
> Tim Cole
> ---
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