Thanks for the thorough explanation. To be clear, though, if "f" is a
PooledDataFactor, "f" is treated as a fixed effect in the Formula language,
whereas "(1|f)" is treated as a random effect? Similarly, "f&g" is the
cartesian product of the fixed effects for f and g? I see that (x|g) has
random effects on the slope of x. Is there a way to get "fixed" slopes on
x?
On Thursday, August 21, 2014 4:35:05 PM UTC-5, Douglas Bates wrote:
>
> On Thursday, August 21, 2014 4:20:46 PM UTC-5, Thomas Covert wrote:
>>
>> Is there a reference somewhere for the formula language specified in
>> DataFrames and used in MixedModels? In particular, I'm confused about how
>> fixed- and random-effects are separately specified. For example, suppose
>> I've got Y_{it} = X_{it}b + u_i + e_{it}. My understanding is that a fixed
>> effect spec for this is Y ~ X + (1|i). What is the random effects
>> specification? If I had a separate categorical variable Z, how would I
>> write Y_{it} = X_{it}b + {Fixed effects on categories of Z} + u_i + e_{it},
>> with random effects on i?
>
>
> The formula language used in MixedModels is similar to that used in the
> lme4 package for R. There are examples in the README.md file and in the
> demo and docs directories of the package.
>
> To specify random effects you need to have a factor (PooledDataVector in
> Julia parlance) which would correspond to the i subscript in your
> specification. Call this g and the response y. Then a simple random
> effects model is written as
>
> y ~ 1 + (1|g)
>
> A model with covariates, x (numeric) and f (categorical) is wiitten
>
> y ~ x + f + (1|g)
>
> A model with these covariates, random slopes and random intercepts is
> written
>
> y ~ x + f + (x|g)
>
> Perhaps it would be best to use the issue tracker in the MixedModels
> package if you have further questions of this type.
>
>>
>> Thanks.
>>
>> -Thom
>>
>
