[R-pkg-devel] Formula modeling

On Oct 07, 2021, at 17:20, pikappa.devel at 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


	[[alternative HTML version deleted]]

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.devel at 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


	[[alternative HTML version deleted]]

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.devel at 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


????[[alternative HTML version deleted]]

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.devel at 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


	[[alternative HTML version deleted]]

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.devel at 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


	[[alternative HTML version deleted]]

Hi,

The different environments can potentially be an issue in the future. I was not aware of the vector construction notation, and I think this is what I was mainly looking for.

I could provide two initialization methods. One will use the ugly vector notation that one could use to bind the whole model with a particular environment. The second can be more user-friendly and use the comma-separated list of formulas. Essentially, the second will prepare the vector formula and call the first initialization method.

The (|) operator comment makes sense, and I would also want to avoid this to the extent that it is feasible.  So, I am currently thinking something along the line:

c(d, s, p | subject | time) ~ c(p + x + y, p + w + y, z + y)

From a perspective of a person that does not use formulas outside of 

This is very similar to how the function ?lme4::lmer uses the bar to separate expressions for design matrices from grouping factors. Actually, the subject and time variables are needed for subsetting prices for various operations required for the model matrix.

Thanks for the suggestions; they are very helpful!

Best,
Pantelis

-----Original Message-----
From: Duncan Murdoch <murdoch.duncan at gmail.com>
Sent: Friday, October 8, 2021 2:04 AM
To: Richard M. Heiberger <rmh at temple.edu>; pikappa.devel at gmail.com
Cc: r-package-devel at r-project.org
Subject: Re: [R-pkg-devel] [External] Formula modeling

On 07/10/2021 5:58 p.m., 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.

There's a disadvantage to this proposal.  I'd assume that "p" means the same in all 3 formulas, but with the notation I give, it could refer to
3 unrelated variables, because each of the formulas would have its own environment, and they could all be different.  I guess you could make it a requirement that they all use the same environment, but that's likely going to be confusing to users, who won't know what it means.

Another possibility that wouldn't have this problem (but in my opinion is kind of ugly) is to use R vector construction notation:

  c(d, s, p) ~ c(p + x + y, p + w + y, z + y)

Duncan Murdoch

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.devel at 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


	[[alternative HTML version deleted]]