There's a Formula package on CRAN
<https://cran.r-project.org/web/packages/Formula/index.html> that's
designed for this use case.
lme4 and nlme don't use it, but implement their own formula
manipulation machinery. (The cleanest version of this machinery is
actually in glmmTMB at
https://github.com/glmmTMB/glmmTMB/blob/master/glmmTMB/R/reformulas.R .)
I would probably recommend Duncan's or Richard's approach, but if you
want to keep your original syntax then the Formula package is probably
the way to go.
On 10/7/21 5:58 PM, Duncan Murdoch wrote:
I don't work with models like this, but I would find it more natural to
express the multiple formulas in a list:
list(d ~ p + x + y, s ~ p + w + y, p ~ z + y)
I'd really have no idea how either of the proposals below should be parsed.
Of course, if people working with models like this are used to working
with notation like yours, that would be a strong argument to use your
notation.
Duncan Murdoch
On 07/10/2021 5:51 p.m., Richard M. Heiberger wrote:
I am responding to a subset of what you asked. There are packages
which use multiple formulas
in their argument sequence.
What you have as a single formula with | as a separator
q | p | subject | time | rho ~ p + x + y | p + w + y | z + y
I think would be better as a comma-separated list of formulas
q , p , subject , time , rho ~ p + x + y , p + w + y , z + y
because in R notation | is usually an operator, not a separator.
lattice uses formulas and the | is used as a conditioning operator.
nlme and lme4 can have multiple formulas in the same calling sequence.
lme4 is newer. from its ?lme4-package
‘lme4’ covers approximately the same ground as the earlier ‘nlme’
package.
lme4 should probably be the modelyou are looking for for the package
design.
On Oct 07, 2021, at 17:20, pikappa.de...@gmail.com wrote:
Dear R-package-devel subscribers,
My question concerns a package design issue relating to the usage of
formulas.
I am interested in describing via formulas systems of the form:
d = p + x + y
s = p + w + y
p = z + y
q = min(d,s).
The context in which I am working is that of market models with,
primarily,
panel data. In the above system, one may think of the first equation as
demand, the second as supply, and the third as an equation
(co-)determining
prices. The fourth equation is implicitly used by the estimation
method, and
it does not need to be specified when programming the R formula. If
you need
more information bout the system, you may check the package diseq.
Currently, I am using constructors to build market model objects. In a
constructor call, I pass [i] the right-hand sides of the first three
equations as strings, [ii] an argument indicating whether the
equations of
the system have correlated shocks, [iii] the identifiers of the used
dataset
(one for the subjects of the panel and one for time), and [iv] the
quantity
(q) and price (p) variables. These four arguments contain all the
necessary
information for constructing a model.
I would now like to re-implement model construction using formulas,
which
would be a more regular practice for most R users. I am currently
considering passing all the above information with a single formula
of the
form:
q | p | subject | time | rho ~ p + x + y | p + w + y | z + y
where subject and time are the identifiers, and rho indicates whether
correlated or independent shocks should be used.
I am unaware of other packages that use formulas in this way (for
instance,
passing the identifiers in the formula), and I wonder if this would go
against any good practices. Would it be better to exclude some of the
necessary elements for constructing the model? This might make the
resuting
formulas more similar to those of models with multiple responses or
multiple
parts. I am not sure, though, how one would use such model formulas
without
all the relevant information. Is there any suggested design
alternative that
I could check?
I would appreciate any suggestions and discussion!
Kind regards,
Pantelis
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