[R-sig-ME] Implications of modeling residuals in multilevel models
Simon Harmel
@|m@h@rme| @end|ng |rom gm@||@com
Sun Dec 17 04:30:32 CET 2023
Hello All,
I have a highly skewed dataset. But, my MODEL of choice below shows
drastically improved, normally distributed residuals (and predicted values)
compared to other models whose residuals are not modeled.
Three quick questions:
1- Is MODEL below assuming that my data come from a population that looks
normal once "X1_categorical" and "X2_numeric" are taken into account
as modeled in MODEL?
2- Do these normally distributed predictions work better for a subject
randomly drawn from a similarly skewed population with a
known "X1_categorical" and "X2_numeric"?
3- I think the distribution of residuals mirrors that of the data. If so,
is it correct to say MODEL below is actually **trimming** my highly skewed
data as if it was distributed as:
```
hist(resid(MODEL, type = "normalized"))
```
MODEL <- nlme::lme(y ~ X1_categorical + X2_numeric ...,
random = ~1| subject,
data = data,
correlation = corSymm(~1|subject),
weights = varComb(varIdent(form = ~ 1 | X1_categorical ),
varPower(form = ~ X2_numeric )))
Thanks,
Simon
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