Arie ten Cate
2017-Nov-02 06:51 UTC
[Rd] Bug in model.matrix.default for higher-order interaction encoding when specific model terms are missing
Hello Tyler,
Thank you for searching for, and finding, the basic description of the
behavior of R in this matter.
I think your example is in agreement with the book.
But let me first note the following. You write: "F_j refers to a
factor (variable) in a model and not a categorical factor". However:
"a factor is a vector object used to specify a discrete
classification" (start of chapter 4 of "An Introduction to R".)
You
might also see the description of the R function factor().
You note that the book says about a factor F_j:
"... F_j is coded by contrasts if T_{i(j)} has appeared in the
formula and by dummy variables if it has not"
You find:
"However, the example I gave demonstrated that this dummy variable
encoding only occurs for the model where the missing term is the
numeric-numeric interaction, ~(X1+X2+X3)^3-X1:X2."
We have here T_i = X1:X2:X3. Also: F_j = X3 (the only factor). Then
T_{i(j)} = X1:X2, which is dropped from the model. Hence the X3 in T_i
must be encoded by dummy variables, as indeed it is.
Arie
On Tue, Oct 31, 2017 at 4:01 PM, Tyler <tylermw at gmail.com>
wrote:> Hi Arie,
>
> Thank you for your further research into the issue.
>
> Regarding Stata: On the other hand, JMP gives model matrices that use the
> main effects contrasts in computing the higher order interactions, without
> the dummy variable encoding. I verified this both by analyzing the linear
> model given in my first example and noting that JMP has one more degree of
> freedom than R for the same model, as well as looking at the generated
model
> matrices. It's easy to find a design where JMP will allow us fit our
model
> with goodness-of-fit estimates and R will not due to the extra degree(s) of
> freedom required. Let's keep the conversation limited to R.
>
> I want to refocus back onto my original bug report, which was not for a
> missing main effects term, but rather for a missing lower-order interaction
> term. The behavior of model.matrix.default() for a missing main effects
term
> is a nice example to demonstrate how model.matrix encodes with dummy
> variables instead of contrasts, but doesn't demonstrate the
inconsistent
> behavior my bug report highlighted.
>
> I went looking for documentation on this behavior, and the issue stems not
> from model.matrix.default(), but rather the terms() function in
interpreting
> the formula. This "clever" replacement of contrasts by dummy
variables to
> maintain marginality (presuming that's the reason) is not described
anywhere
> in the documentation for either the model.matrix() or the terms() function.
> In order to find a description for the behavior, I had to look in the
> underlying C code, buried above the "TermCode" function of the
"model.c"
> file, which says:
>
> "TermCode decides on the encoding of a model term. Returns 1 if
variable
> ``whichBit'' in ``thisTerm'' is to be encoded by contrasts
and 2 if it is to
> be encoded by dummy variables. This is decided using the heuristic
> described in Statistical Models in S, page 38."
>
> I do not have a copy of this book, and I suspect most R users do not as
> well. Thankfully, however, some of the pages describing this behavior were
> available as part of Amazon's "Look Inside" feature--but if
not for that, I
> would have no idea what heuristic R was using. Since those pages could made
> unavailable by Amazon at any time, at the very least we have an problem
with
> a lack of documentation.
>
> However, I still believe there is a bug when comparing R's
implementation to
> the heuristic described in the book. From Statistical Models in S, page
> 38-39:
>
> "Suppose F_j is any factor included in term T_i. Let T_{i(j)} denote
the
> margin of T_i for factor F_j--that is, the term obtained by dropping F_j
> from T_i. We say that T_{i(j)} has appeared in the formula if there is some
> term T_i' for i' < i such that T_i' contains all the factors
appearing in
> T_{i(j)}. The usual case is that T_{i(j)} itself is one of the preceding
> terms. Then F_j is coded by contrasts if T_{i(j)} has appeared in the
> formula and by dummy variables if it has not"
>
> Here, F_j refers to a factor (variable) in a model and not a categorical
> factor, as specified later in that section (page 40): "Numeric
variables
> appear in the computations as themselves, uncoded. Therefore, the rule does
> not do anything special for them, and it remains valid, in a trivial sense,
> whenever any of the F_j is numeric rather than categorical."
>
> Going back to my original example with three variables: X1 (numeric), X2
> (numeric), X3 (categorical). This heuristic prescribes encoding X1:X2:X3
> with contrasts as long as X1:X2, X1:X3, and X2:X3 exist in the formula.
When
> any of the preceding terms do not exist, this heuristic tells us to use
> dummy variables to encode the interaction (e.g. "F_j [the interaction
term]
> is coded ... by dummy variables if it [any of the marginal terms obtained
by
> dropping a single factor in the interaction] has not [appeared in the
> formula]"). However, the example I gave demonstrated that this dummy
> variable encoding only occurs for the model where the missing term is the
> numeric-numeric interaction, "~(X1+X2+X3)^3-X1:X2". Otherwise,
the
> interaction term X1:X2:X3 is encoded by contrasts, not dummy variables.
This
> is inconsistent with the description of the intended behavior given in the
> book.
>
> Best regards,
> Tyler
>
>
> On Fri, Oct 27, 2017 at 2:18 PM, Arie ten Cate <arietencate at
gmail.com>
> wrote:
>>
>> Hello Tyler,
>>
>> I want to bring to your attention the following document: "What
>> happens if you omit the main effect in a regression model with an
>> interaction?"
>>
(https://stats.idre.ucla.edu/stata/faq/what-happens-if-you-omit-the-main-effect-in-a-regression-model-with-an-interaction).
>> This gives a useful review of the problem. Your example is Case 2: a
>> continuous and a categorical regressor.
>>
>> The numerical examples are coded in Stata, and they give the same
>> result as in R. Hence, if this is a bug in R then it is also a bug in
>> Stata. That seems very unlikely.
>>
>> Here is a simulation in R of the above mentioned Case 2 in Stata:
>>
>> df <-
expand.grid(socst=c(-1:1),grp=c("1","2","3","4"))
>> print("Full model")
>> print(model.matrix(~(socst+grp)^2 ,data=df))
>> print("Example 2.1: drop socst")
>> print(model.matrix(~(socst+grp)^2 -socst ,data=df))
>> print("Example 2.2: drop grp")
>> print(model.matrix(~(socst+grp)^2 -grp ,data=df))
>>
>> This gives indeed the following regressors:
>>
>> "Full model"
>> (Intercept) socst grp2 grp3 grp4 socst:grp2 socst:grp3 socst:grp4
>> "Example 2.1: drop socst"
>> (Intercept) grp2 grp3 grp4 socst:grp1 socst:grp2 socst:grp3 socst:grp4
>> "Example 2.2: drop grp"
>> (Intercept) socst socst:grp2 socst:grp3 socst:grp4
>>
>> There is a little bit of R documentation about this, based on the
>> concept of marginality, which typically forbids a model having an
>> interaction but not the corresponding main effects. (You might see the
>> references in https://en.wikipedia.org/wiki/Principle_of_marginality )
>> See "An Introduction to R", by Venables and Smith and the
R Core
>> Team. At the bottom of page 52 (PDF: 57) it says: "Although the
>> details are complicated, model formulae in R will normally generate
>> the models that an expert statistician would expect, provided that
>> marginality is preserved. Fitting, for [a contrary] example, a model
>> with an interaction but not the corresponding main effects will in
>> general lead to surprising results ....".
>> The Reference Manual states that the R functions dropterm() and
>> addterm() resp. drop or add only terms such that marginality is
>> preserved.
>>
>> Finally, about your singular matrix t(mm)%*%mm. This is in fact
>> Example 2.1 in Case 2 discussed above. As discussed there, in Stata
>> and in R the drop of the continuous variable has no effect on the
>> degrees of freedom here: it is just a reparameterisation of the full
>> model, protecting you against losing marginality... Hence the
>> model.matrix 'mm' is still square and nonsingular after the
drop of
>> X1, unless of course when a row is removed from the matrix
'design'
>> when before creating 'mm'.
>>
>> Arie
>>
>> On Sun, Oct 15, 2017 at 7:05 PM, Tyler <tylermw at gmail.com>
wrote:
>> > You could possibly try to explain away the behavior for a missing
main
>> > effects term, since without the main effects term we don't
have main
>> > effect
>> > columns in the model matrix used to compute the interaction
columns (At
>> > best this is undocumented behavior--I still think it's a bug,
as we know
>> > how we would encode the categorical factors if they were in fact
>> > present.
>> > It's either specified in contrasts.arg or using the default
set in
>> > options). However, when all the main effects are present, why
would the
>> > three-factor interaction column not simply be the product of the
main
>> > effect columns? In my example: we know X1, we know X2, and we know
X3.
>> > Why
>> > does the encoding of X1:X2:X3 depend on whether we specified a
>> > two-factor
>> > interaction, AND only changes for specific missing interactions?
>> >
>> > In addition, I can use a two-term example similar to yours to show
how
>> > this
>> > behavior results in a singular covariance matrix when, given the
desired
>> > factor encoding, it should not be singular.
>> >
>> > We start with a full factorial design for a two-level continuous
factor
>> > and
>> > a three-level categorical factor, and remove a single row. This
design
>> > matrix does not leave enough degrees of freedom to determine
>> > goodness-of-fit, but should allow us to obtain parameter
estimates.
>> >
>> >> design =
expand.grid(X1=c(1,-1),X2=c("A","B","C"))
>> >> design = design[-1,]
>> >> design
>> > X1 X2
>> > 2 -1 A
>> > 3 1 B
>> > 4 -1 B
>> > 5 1 C
>> > 6 -1 C
>> >
>> > Here, we first calculate the model matrix for the full model, and
then
>> > manually remove the X1 column from the model matrix. This gives us
the
>> > model matrix one would expect if X1 were removed from the model.
We then
>> > successfully calculate the covariance matrix.
>> >
>> >> mm = model.matrix(~(X1+X2)^2,data=design)
>> >> mm
>> > (Intercept) X1 X2B X2C X1:X2B X1:X2C
>> > 2 1 -1 0 0 0 0
>> > 3 1 1 1 0 1 0
>> > 4 1 -1 1 0 -1 0
>> > 5 1 1 0 1 0 1
>> > 6 1 -1 0 1 0 -1
>> >
>> >> mm = mm[,-2]
>> >> solve(t(mm) %*% mm)
>> > (Intercept) X2B X2C X1:X2B X1:X2C
>> > (Intercept) 1 -1.0 -1.0 0.0 0.0
>> > X2B -1 1.5 1.0 0.0 0.0
>> > X2C -1 1.0 1.5 0.0 0.0
>> > X1:X2B 0 0.0 0.0 0.5 0.0
>> > X1:X2C 0 0.0 0.0 0.0 0.5
>> >
>> > Here, we see the actual behavior for model.matrix. The undesired
>> > re-coding
>> > of the model matrix interaction term makes the information matrix
>> > singular.
>> >
>> >> mm = model.matrix(~(X1+X2)^2-X1,data=design)
>> >> mm
>> > (Intercept) X2B X2C X1:X2A X1:X2B X1:X2C
>> > 2 1 0 0 -1 0 0
>> > 3 1 1 0 0 1 0
>> > 4 1 1 0 0 -1 0
>> > 5 1 0 1 0 0 1
>> > 6 1 0 1 0 0 -1
>> >
>> >> solve(t(mm) %*% mm)
>> > Error in solve.default(t(mm) %*% mm) : system is computationally
>> > singular:
>> > reciprocal condition number = 5.55112e-18
>> >
>> > I still believe this is a bug.
>> >
>> > Best regards,
>> > Tyler Morgan-Wall
>> >
>> > On Sun, Oct 15, 2017 at 1:49 AM, Arie ten Cate <arietencate at
gmail.com>
>> > wrote:
>> >
>> >> I think it is not a bug. It is a general property of
interactions.
>> >> This property is best observed if all variables are factors
>> >> (qualitative).
>> >>
>> >> For example, you have three variables (factors). You ask for
as many
>> >> interactions as possible, except an interaction term between
two
>> >> particular variables. When this interaction is not a constant,
it is
>> >> different for different values of the remaining variable. More
>> >> precisely: for all values of that variable. In other words:
you have a
>> >> three-way interaction, with all values of that variable.
>> >>
>> >> An even smaller example is the following script with only two
>> >> variables, each being a factor:
>> >>
>> >> df <- expand.grid(X1=c("p","q"),
X2=c("A","B","C"))
>> >> print(model.matrix(~(X1+X2)^2 ,data=df))
>> >> print(model.matrix(~(X1+X2)^2 -X1,data=df))
>> >> print(model.matrix(~(X1+X2)^2 -X2,data=df))
>> >>
>> >> The result is:
>> >>
>> >> (Intercept) X1q X2B X2C X1q:X2B X1q:X2C
>> >> 1 1 0 0 0 0 0
>> >> 2 1 1 0 0 0 0
>> >> 3 1 0 1 0 0 0
>> >> 4 1 1 1 0 1 0
>> >> 5 1 0 0 1 0 0
>> >> 6 1 1 0 1 0 1
>> >>
>> >> (Intercept) X2B X2C X1q:X2A X1q:X2B X1q:X2C
>> >> 1 1 0 0 0 0 0
>> >> 2 1 0 0 1 0 0
>> >> 3 1 1 0 0 0 0
>> >> 4 1 1 0 0 1 0
>> >> 5 1 0 1 0 0 0
>> >> 6 1 0 1 0 0 1
>> >>
>> >> (Intercept) X1q X1p:X2B X1q:X2B X1p:X2C X1q:X2C
>> >> 1 1 0 0 0 0 0
>> >> 2 1 1 0 0 0 0
>> >> 3 1 0 1 0 0 0
>> >> 4 1 1 0 1 0 0
>> >> 5 1 0 0 0 1 0
>> >> 6 1 1 0 0 0 1
>> >>
>> >> Thus, in the second result, we have no main effect of X1.
Instead, the
>> >> effect of X1 depends on the value of X2; either A or B or C.
In fact,
>> >> this is a two-way interaction, including all three values of
X2. In
>> >> the third result, we have no main effect of X2, The effect of
X2
>> >> depends on the value of X1; either p or q.
>> >>
>> >> A complicating element with your example seems to be that your
X1 and
>> >> X2 are not factors.
>> >>
>> >> Arie
>> >>
>> >> On Thu, Oct 12, 2017 at 7:12 PM, Tyler <tylermw at
gmail.com> wrote:
>> >> > Hi,
>> >> >
>> >> > I recently ran into an inconsistency in the way
model.matrix.default
>> >> > handles factor encoding for higher level interactions
with
>> >> > categorical
>> >> > variables when the full hierarchy of effects is not
present.
>> >> > Depending on
>> >> > which lower level interactions are specified, the factor
encoding
>> >> > changes
>> >> > for a higher level interaction. Consider the following
minimal
>> >> reproducible
>> >> > example:
>> >> >
>> >> > --------------
>> >> >
>> >> >> runmatrix =
expand.grid(X1=c(1,-1),X2=c(1,-1),X3=c("A","B","C"))>
>> >> model.matrix(~(X1+X2+X3)^3,data=runmatrix) (Intercept) X1 X2
X3B X3C
>> >> X1:X2 X1:X3B X1:X3C X2:X3B X2:X3C X1:X2:X3B X1:X2:X3C
>> >> > 1 1 1 1 0 0 1 0 0 0
0
>> >> > 0 0
>> >> > 2 1 -1 1 0 0 -1 0 0 0
0
>> >> > 0 0
>> >> > 3 1 1 -1 0 0 -1 0 0 0
0
>> >> > 0 0
>> >> > 4 1 -1 -1 0 0 1 0 0 0
0
>> >> > 0 0
>> >> > 5 1 1 1 1 0 1 1 0 1
0
>> >> > 1 0
>> >> > 6 1 -1 1 1 0 -1 -1 0 1
0
>> >> > -1 0
>> >> > 7 1 1 -1 1 0 -1 1 0 -1
0
>> >> > -1 0
>> >> > 8 1 -1 -1 1 0 1 -1 0 -1
0
>> >> > 1 0
>> >> > 9 1 1 1 0 1 1 0 1 0
1
>> >> > 0 1
>> >> > 10 1 -1 1 0 1 -1 0 -1 0
1
>> >> > 0 -1
>> >> > 11 1 1 -1 0 1 -1 0 1 0
-1
>> >> > 0 -1
>> >> > 12 1 -1 -1 0 1 1 0 -1 0
-1
>> >> > 0 1
>> >> > attr(,"assign")
>> >> > [1] 0 1 2 3 3 4 5 5 6 6 7 7
>> >> > attr(,"contrasts")
>> >> > attr(,"contrasts")$X3
>> >> > [1] "contr.treatment"
>> >> >
>> >> > --------------
>> >> >
>> >> > Specifying the full hierarchy gives us what we expect:
the
>> >> > interaction
>> >> > columns are simply calculated from the product of the
main effect
>> >> columns.
>> >> > The interaction term X1:X2:X3 gives us two columns in the
model
>> >> > matrix,
>> >> > X1:X2:X3B and X1:X2:X3C, matching the products of the
main effects.
>> >> >
>> >> > If we remove either the X2:X3 interaction or the X1:X3
interaction,
>> >> > we
>> >> get
>> >> > what we would expect for the X1:X2:X3 interaction, but
when we remove
>> >> > the
>> >> > X1:X2 interaction the encoding for X1:X2:X3 changes
completely:
>> >> >
>> >> > --------------
>> >> >
>> >> >> model.matrix(~(X1+X2+X3)^3-X1:X3,data=runmatrix)
(Intercept) X1 X2
>> >> X3B X3C X1:X2 X2:X3B X2:X3C X1:X2:X3B X1:X2:X3C
>> >> > 1 1 1 1 0 0 1 0 0
0 0
>> >> > 2 1 -1 1 0 0 -1 0 0
0 0
>> >> > 3 1 1 -1 0 0 -1 0 0
0 0
>> >> > 4 1 -1 -1 0 0 1 0 0
0 0
>> >> > 5 1 1 1 1 0 1 1 0
1 0
>> >> > 6 1 -1 1 1 0 -1 1 0
-1 0
>> >> > 7 1 1 -1 1 0 -1 -1 0
-1 0
>> >> > 8 1 -1 -1 1 0 1 -1 0
1 0
>> >> > 9 1 1 1 0 1 1 0 1
0 1
>> >> > 10 1 -1 1 0 1 -1 0 1
0 -1
>> >> > 11 1 1 -1 0 1 -1 0 -1
0 -1
>> >> > 12 1 -1 -1 0 1 1 0 -1
0 1
>> >> > attr(,"assign")
>> >> > [1] 0 1 2 3 3 4 5 5 6 6
>> >> > attr(,"contrasts")
>> >> > attr(,"contrasts")$X3
>> >> > [1] "contr.treatment"
>> >> >
>> >> >
>> >> >
>> >> >> model.matrix(~(X1+X2+X3)^3-X2:X3,data=runmatrix)
(Intercept) X1 X2
>> >> X3B X3C X1:X2 X1:X3B X1:X3C X1:X2:X3B X1:X2:X3C
>> >> > 1 1 1 1 0 0 1 0 0
0 0
>> >> > 2 1 -1 1 0 0 -1 0 0
0 0
>> >> > 3 1 1 -1 0 0 -1 0 0
0 0
>> >> > 4 1 -1 -1 0 0 1 0 0
0 0
>> >> > 5 1 1 1 1 0 1 1 0
1 0
>> >> > 6 1 -1 1 1 0 -1 -1 0
-1 0
>> >> > 7 1 1 -1 1 0 -1 1 0
-1 0
>> >> > 8 1 -1 -1 1 0 1 -1 0
1 0
>> >> > 9 1 1 1 0 1 1 0 1
0 1
>> >> > 10 1 -1 1 0 1 -1 0 -1
0 -1
>> >> > 11 1 1 -1 0 1 -1 0 1
0 -1
>> >> > 12 1 -1 -1 0 1 1 0 -1
0 1
>> >> > attr(,"assign")
>> >> > [1] 0 1 2 3 3 4 5 5 6 6
>> >> > attr(,"contrasts")
>> >> > attr(,"contrasts")$X3
>> >> > [1] "contr.treatment"
>> >> >
>> >> >
>> >> >> model.matrix(~(X1+X2+X3)^3-X1:X2,data=runmatrix)
(Intercept) X1 X2
>> >> X3B X3C X1:X3B X1:X3C X2:X3B X2:X3C X1:X2:X3A X1:X2:X3B
X1:X2:X3C
>> >> > 1 1 1 1 0 0 0 0 0 0
1
>> >> > 0 0
>> >> > 2 1 -1 1 0 0 0 0 0 0
-1
>> >> > 0 0
>> >> > 3 1 1 -1 0 0 0 0 0 0
-1
>> >> > 0 0
>> >> > 4 1 -1 -1 0 0 0 0 0 0
1
>> >> > 0 0
>> >> > 5 1 1 1 1 0 1 0 1 0
0
>> >> > 1 0
>> >> > 6 1 -1 1 1 0 -1 0 1 0
0
>> >> > -1 0
>> >> > 7 1 1 -1 1 0 1 0 -1 0
0
>> >> > -1 0
>> >> > 8 1 -1 -1 1 0 -1 0 -1 0
0
>> >> > 1 0
>> >> > 9 1 1 1 0 1 0 1 0 1
0
>> >> > 0 1
>> >> > 10 1 -1 1 0 1 0 -1 0 1
0
>> >> > 0 -1
>> >> > 11 1 1 -1 0 1 0 1 0 -1
0
>> >> > 0 -1
>> >> > 12 1 -1 -1 0 1 0 -1 0 -1
0
>> >> > 0 1
>> >> > attr(,"assign")
>> >> > [1] 0 1 2 3 3 4 4 5 5 6 6 6
>> >> > attr(,"contrasts")
>> >> > attr(,"contrasts")$X3
>> >> > [1] "contr.treatment"
>> >> >
>> >> > --------------
>> >> >
>> >> > Here, we now see the encoding for the interaction
X1:X2:X3 is now the
>> >> > interaction of X1 and X2 with a new encoding for X3 where
each factor
>> >> level
>> >> > is represented by its own column. I would expect, given
the two
>> >> > column
>> >> > dummy variable encoding for X3, that the X1:X2:X3 column
would also
>> >> > be
>> >> two
>> >> > columns regardless of what two-factor interactions we
also specified,
>> >> > but
>> >> > in this case it switches to three. If other two factor
interactions
>> >> > are
>> >> > missing in addition to X1:X2, this issue still occurs.
This also
>> >> > happens
>> >> > regardless of the contrast specified in contrasts.arg for
X3. I don't
>> >> > see
>> >> > any reasoning for this behavior given in the
documentation, so I
>> >> > suspect
>> >> it
>> >> > is a bug.
>> >> >
>> >> > Best regards,
>> >> > Tyler Morgan-Wall
>> >> >
>> >> > [[alternative HTML version deleted]]
>> >> >
>> >> > ______________________________________________
>> >> > R-devel at r-project.org mailing list
>> >> > https://stat.ethz.ch/mailman/listinfo/r-devel
>> >>
>> >
>> > [[alternative HTML version deleted]]
>> >
>> > ______________________________________________
>> > R-devel at r-project.org mailing list
>> > https://stat.ethz.ch/mailman/listinfo/r-devel
>
>
Tyler
2017-Nov-02 15:11 UTC
[Rd] Bug in model.matrix.default for higher-order interaction encoding when specific model terms are missing
Hi Arie, The book out of which this behavior is based does not use factor (in this section) to refer to categorical factor. I will again point to this sentence, from page 40, in the same section and referring to the behavior under question, that shows F_j is not limited to categorical factors: "Numeric variables appear in the computations as themselves, uncoded. Therefore, the rule does not do anything special for them, and it remains valid, in a trivial sense, whenever any of the F_j is numeric rather than categorical." Note the "... whenever any of the F_j is numeric rather than categorical." Factor here is used in the more general sense of the word, not referring to the R type "factor." The behavior of R does not match the heuristic that it's citing. Best regards, Tyler On Thu, Nov 2, 2017 at 2:51 AM, Arie ten Cate <arietencate at gmail.com> wrote:> Hello Tyler, > > Thank you for searching for, and finding, the basic description of the > behavior of R in this matter. > > I think your example is in agreement with the book. > > But let me first note the following. You write: "F_j refers to a > factor (variable) in a model and not a categorical factor". However: > "a factor is a vector object used to specify a discrete > classification" (start of chapter 4 of "An Introduction to R".) You > might also see the description of the R function factor(). > > You note that the book says about a factor F_j: > "... F_j is coded by contrasts if T_{i(j)} has appeared in the > formula and by dummy variables if it has not" > > You find: > "However, the example I gave demonstrated that this dummy variable > encoding only occurs for the model where the missing term is the > numeric-numeric interaction, ~(X1+X2+X3)^3-X1:X2." > > We have here T_i = X1:X2:X3. Also: F_j = X3 (the only factor). Then > T_{i(j)} = X1:X2, which is dropped from the model. Hence the X3 in T_i > must be encoded by dummy variables, as indeed it is. > > Arie > > On Tue, Oct 31, 2017 at 4:01 PM, Tyler <tylermw at gmail.com> wrote: > > Hi Arie, > > > > Thank you for your further research into the issue. > > > > Regarding Stata: On the other hand, JMP gives model matrices that use the > > main effects contrasts in computing the higher order interactions, > without > > the dummy variable encoding. I verified this both by analyzing the linear > > model given in my first example and noting that JMP has one more degree > of > > freedom than R for the same model, as well as looking at the generated > model > > matrices. It's easy to find a design where JMP will allow us fit our > model > > with goodness-of-fit estimates and R will not due to the extra degree(s) > of > > freedom required. Let's keep the conversation limited to R. > > > > I want to refocus back onto my original bug report, which was not for a > > missing main effects term, but rather for a missing lower-order > interaction > > term. The behavior of model.matrix.default() for a missing main effects > term > > is a nice example to demonstrate how model.matrix encodes with dummy > > variables instead of contrasts, but doesn't demonstrate the inconsistent > > behavior my bug report highlighted. > > > > I went looking for documentation on this behavior, and the issue stems > not > > from model.matrix.default(), but rather the terms() function in > interpreting > > the formula. This "clever" replacement of contrasts by dummy variables to > > maintain marginality (presuming that's the reason) is not described > anywhere > > in the documentation for either the model.matrix() or the terms() > function. > > In order to find a description for the behavior, I had to look in the > > underlying C code, buried above the "TermCode" function of the "model.c" > > file, which says: > > > > "TermCode decides on the encoding of a model term. Returns 1 if variable > > ``whichBit'' in ``thisTerm'' is to be encoded by contrasts and 2 if it > is to > > be encoded by dummy variables. This is decided using the heuristic > > described in Statistical Models in S, page 38." > > > > I do not have a copy of this book, and I suspect most R users do not as > > well. Thankfully, however, some of the pages describing this behavior > were > > available as part of Amazon's "Look Inside" feature--but if not for > that, I > > would have no idea what heuristic R was using. Since those pages could > made > > unavailable by Amazon at any time, at the very least we have an problem > with > > a lack of documentation. > > > > However, I still believe there is a bug when comparing R's > implementation to > > the heuristic described in the book. From Statistical Models in S, page > > 38-39: > > > > "Suppose F_j is any factor included in term T_i. Let T_{i(j)} denote the > > margin of T_i for factor F_j--that is, the term obtained by dropping F_j > > from T_i. We say that T_{i(j)} has appeared in the formula if there is > some > > term T_i' for i' < i such that T_i' contains all the factors appearing in > > T_{i(j)}. The usual case is that T_{i(j)} itself is one of the preceding > > terms. Then F_j is coded by contrasts if T_{i(j)} has appeared in the > > formula and by dummy variables if it has not" > > > > Here, F_j refers to a factor (variable) in a model and not a categorical > > factor, as specified later in that section (page 40): "Numeric variables > > appear in the computations as themselves, uncoded. Therefore, the rule > does > > not do anything special for them, and it remains valid, in a trivial > sense, > > whenever any of the F_j is numeric rather than categorical." > > > > Going back to my original example with three variables: X1 (numeric), X2 > > (numeric), X3 (categorical). This heuristic prescribes encoding X1:X2:X3 > > with contrasts as long as X1:X2, X1:X3, and X2:X3 exist in the formula. > When > > any of the preceding terms do not exist, this heuristic tells us to use > > dummy variables to encode the interaction (e.g. "F_j [the interaction > term] > > is coded ... by dummy variables if it [any of the marginal terms > obtained by > > dropping a single factor in the interaction] has not [appeared in the > > formula]"). However, the example I gave demonstrated that this dummy > > variable encoding only occurs for the model where the missing term is the > > numeric-numeric interaction, "~(X1+X2+X3)^3-X1:X2". Otherwise, the > > interaction term X1:X2:X3 is encoded by contrasts, not dummy variables. > This > > is inconsistent with the description of the intended behavior given in > the > > book. > > > > Best regards, > > Tyler > > > > > > On Fri, Oct 27, 2017 at 2:18 PM, Arie ten Cate <arietencate at gmail.com> > > wrote: > >> > >> Hello Tyler, > >> > >> I want to bring to your attention the following document: "What > >> happens if you omit the main effect in a regression model with an > >> interaction?" > >> (https://stats.idre.ucla.edu/stata/faq/what-happens-if-you- > omit-the-main-effect-in-a-regression-model-with-an-interaction). > >> This gives a useful review of the problem. Your example is Case 2: a > >> continuous and a categorical regressor. > >> > >> The numerical examples are coded in Stata, and they give the same > >> result as in R. Hence, if this is a bug in R then it is also a bug in > >> Stata. That seems very unlikely. > >> > >> Here is a simulation in R of the above mentioned Case 2 in Stata: > >> > >> df <- expand.grid(socst=c(-1:1),grp=c("1","2","3","4")) > >> print("Full model") > >> print(model.matrix(~(socst+grp)^2 ,data=df)) > >> print("Example 2.1: drop socst") > >> print(model.matrix(~(socst+grp)^2 -socst ,data=df)) > >> print("Example 2.2: drop grp") > >> print(model.matrix(~(socst+grp)^2 -grp ,data=df)) > >> > >> This gives indeed the following regressors: > >> > >> "Full model" > >> (Intercept) socst grp2 grp3 grp4 socst:grp2 socst:grp3 socst:grp4 > >> "Example 2.1: drop socst" > >> (Intercept) grp2 grp3 grp4 socst:grp1 socst:grp2 socst:grp3 socst:grp4 > >> "Example 2.2: drop grp" > >> (Intercept) socst socst:grp2 socst:grp3 socst:grp4 > >> > >> There is a little bit of R documentation about this, based on the > >> concept of marginality, which typically forbids a model having an > >> interaction but not the corresponding main effects. (You might see the > >> references in https://en.wikipedia.org/wiki/Principle_of_marginality ) > >> See "An Introduction to R", by Venables and Smith and the R Core > >> Team. At the bottom of page 52 (PDF: 57) it says: "Although the > >> details are complicated, model formulae in R will normally generate > >> the models that an expert statistician would expect, provided that > >> marginality is preserved. Fitting, for [a contrary] example, a model > >> with an interaction but not the corresponding main effects will in > >> general lead to surprising results ....". > >> The Reference Manual states that the R functions dropterm() and > >> addterm() resp. drop or add only terms such that marginality is > >> preserved. > >> > >> Finally, about your singular matrix t(mm)%*%mm. This is in fact > >> Example 2.1 in Case 2 discussed above. As discussed there, in Stata > >> and in R the drop of the continuous variable has no effect on the > >> degrees of freedom here: it is just a reparameterisation of the full > >> model, protecting you against losing marginality... Hence the > >> model.matrix 'mm' is still square and nonsingular after the drop of > >> X1, unless of course when a row is removed from the matrix 'design' > >> when before creating 'mm'. > >> > >> Arie > >> > >> On Sun, Oct 15, 2017 at 7:05 PM, Tyler <tylermw at gmail.com> wrote: > >> > You could possibly try to explain away the behavior for a missing main > >> > effects term, since without the main effects term we don't have main > >> > effect > >> > columns in the model matrix used to compute the interaction columns > (At > >> > best this is undocumented behavior--I still think it's a bug, as we > know > >> > how we would encode the categorical factors if they were in fact > >> > present. > >> > It's either specified in contrasts.arg or using the default set in > >> > options). However, when all the main effects are present, why would > the > >> > three-factor interaction column not simply be the product of the main > >> > effect columns? In my example: we know X1, we know X2, and we know X3. > >> > Why > >> > does the encoding of X1:X2:X3 depend on whether we specified a > >> > two-factor > >> > interaction, AND only changes for specific missing interactions? > >> > > >> > In addition, I can use a two-term example similar to yours to show how > >> > this > >> > behavior results in a singular covariance matrix when, given the > desired > >> > factor encoding, it should not be singular. > >> > > >> > We start with a full factorial design for a two-level continuous > factor > >> > and > >> > a three-level categorical factor, and remove a single row. This design > >> > matrix does not leave enough degrees of freedom to determine > >> > goodness-of-fit, but should allow us to obtain parameter estimates. > >> > > >> >> design = expand.grid(X1=c(1,-1),X2=c("A","B","C")) > >> >> design = design[-1,] > >> >> design > >> > X1 X2 > >> > 2 -1 A > >> > 3 1 B > >> > 4 -1 B > >> > 5 1 C > >> > 6 -1 C > >> > > >> > Here, we first calculate the model matrix for the full model, and then > >> > manually remove the X1 column from the model matrix. This gives us the > >> > model matrix one would expect if X1 were removed from the model. We > then > >> > successfully calculate the covariance matrix. > >> > > >> >> mm = model.matrix(~(X1+X2)^2,data=design) > >> >> mm > >> > (Intercept) X1 X2B X2C X1:X2B X1:X2C > >> > 2 1 -1 0 0 0 0 > >> > 3 1 1 1 0 1 0 > >> > 4 1 -1 1 0 -1 0 > >> > 5 1 1 0 1 0 1 > >> > 6 1 -1 0 1 0 -1 > >> > > >> >> mm = mm[,-2] > >> >> solve(t(mm) %*% mm) > >> > (Intercept) X2B X2C X1:X2B X1:X2C > >> > (Intercept) 1 -1.0 -1.0 0.0 0.0 > >> > X2B -1 1.5 1.0 0.0 0.0 > >> > X2C -1 1.0 1.5 0.0 0.0 > >> > X1:X2B 0 0.0 0.0 0.5 0.0 > >> > X1:X2C 0 0.0 0.0 0.0 0.5 > >> > > >> > Here, we see the actual behavior for model.matrix. The undesired > >> > re-coding > >> > of the model matrix interaction term makes the information matrix > >> > singular. > >> > > >> >> mm = model.matrix(~(X1+X2)^2-X1,data=design) > >> >> mm > >> > (Intercept) X2B X2C X1:X2A X1:X2B X1:X2C > >> > 2 1 0 0 -1 0 0 > >> > 3 1 1 0 0 1 0 > >> > 4 1 1 0 0 -1 0 > >> > 5 1 0 1 0 0 1 > >> > 6 1 0 1 0 0 -1 > >> > > >> >> solve(t(mm) %*% mm) > >> > Error in solve.default(t(mm) %*% mm) : system is computationally > >> > singular: > >> > reciprocal condition number = 5.55112e-18 > >> > > >> > I still believe this is a bug. > >> > > >> > Best regards, > >> > Tyler Morgan-Wall > >> > > >> > On Sun, Oct 15, 2017 at 1:49 AM, Arie ten Cate <arietencate at gmail.com > > > >> > wrote: > >> > > >> >> I think it is not a bug. It is a general property of interactions. > >> >> This property is best observed if all variables are factors > >> >> (qualitative). > >> >> > >> >> For example, you have three variables (factors). You ask for as many > >> >> interactions as possible, except an interaction term between two > >> >> particular variables. When this interaction is not a constant, it is > >> >> different for different values of the remaining variable. More > >> >> precisely: for all values of that variable. In other words: you have > a > >> >> three-way interaction, with all values of that variable. > >> >> > >> >> An even smaller example is the following script with only two > >> >> variables, each being a factor: > >> >> > >> >> df <- expand.grid(X1=c("p","q"), X2=c("A","B","C")) > >> >> print(model.matrix(~(X1+X2)^2 ,data=df)) > >> >> print(model.matrix(~(X1+X2)^2 -X1,data=df)) > >> >> print(model.matrix(~(X1+X2)^2 -X2,data=df)) > >> >> > >> >> The result is: > >> >> > >> >> (Intercept) X1q X2B X2C X1q:X2B X1q:X2C > >> >> 1 1 0 0 0 0 0 > >> >> 2 1 1 0 0 0 0 > >> >> 3 1 0 1 0 0 0 > >> >> 4 1 1 1 0 1 0 > >> >> 5 1 0 0 1 0 0 > >> >> 6 1 1 0 1 0 1 > >> >> > >> >> (Intercept) X2B X2C X1q:X2A X1q:X2B X1q:X2C > >> >> 1 1 0 0 0 0 0 > >> >> 2 1 0 0 1 0 0 > >> >> 3 1 1 0 0 0 0 > >> >> 4 1 1 0 0 1 0 > >> >> 5 1 0 1 0 0 0 > >> >> 6 1 0 1 0 0 1 > >> >> > >> >> (Intercept) X1q X1p:X2B X1q:X2B X1p:X2C X1q:X2C > >> >> 1 1 0 0 0 0 0 > >> >> 2 1 1 0 0 0 0 > >> >> 3 1 0 1 0 0 0 > >> >> 4 1 1 0 1 0 0 > >> >> 5 1 0 0 0 1 0 > >> >> 6 1 1 0 0 0 1 > >> >> > >> >> Thus, in the second result, we have no main effect of X1. Instead, > the > >> >> effect of X1 depends on the value of X2; either A or B or C. In fact, > >> >> this is a two-way interaction, including all three values of X2. In > >> >> the third result, we have no main effect of X2, The effect of X2 > >> >> depends on the value of X1; either p or q. > >> >> > >> >> A complicating element with your example seems to be that your X1 and > >> >> X2 are not factors. > >> >> > >> >> Arie > >> >> > >> >> On Thu, Oct 12, 2017 at 7:12 PM, Tyler <tylermw at gmail.com> wrote: > >> >> > Hi, > >> >> > > >> >> > I recently ran into an inconsistency in the way > model.matrix.default > >> >> > handles factor encoding for higher level interactions with > >> >> > categorical > >> >> > variables when the full hierarchy of effects is not present. > >> >> > Depending on > >> >> > which lower level interactions are specified, the factor encoding > >> >> > changes > >> >> > for a higher level interaction. Consider the following minimal > >> >> reproducible > >> >> > example: > >> >> > > >> >> > -------------- > >> >> > > >> >> >> runmatrix = expand.grid(X1=c(1,-1),X2=c(1,-1),X3=c("A","B","C"))> > >> >> model.matrix(~(X1+X2+X3)^3,data=runmatrix) (Intercept) X1 X2 X3B > X3C > >> >> X1:X2 X1:X3B X1:X3C X2:X3B X2:X3C X1:X2:X3B X1:X2:X3C > >> >> > 1 1 1 1 0 0 1 0 0 0 0 > >> >> > 0 0 > >> >> > 2 1 -1 1 0 0 -1 0 0 0 0 > >> >> > 0 0 > >> >> > 3 1 1 -1 0 0 -1 0 0 0 0 > >> >> > 0 0 > >> >> > 4 1 -1 -1 0 0 1 0 0 0 0 > >> >> > 0 0 > >> >> > 5 1 1 1 1 0 1 1 0 1 0 > >> >> > 1 0 > >> >> > 6 1 -1 1 1 0 -1 -1 0 1 0 > >> >> > -1 0 > >> >> > 7 1 1 -1 1 0 -1 1 0 -1 0 > >> >> > -1 0 > >> >> > 8 1 -1 -1 1 0 1 -1 0 -1 0 > >> >> > 1 0 > >> >> > 9 1 1 1 0 1 1 0 1 0 1 > >> >> > 0 1 > >> >> > 10 1 -1 1 0 1 -1 0 -1 0 1 > >> >> > 0 -1 > >> >> > 11 1 1 -1 0 1 -1 0 1 0 -1 > >> >> > 0 -1 > >> >> > 12 1 -1 -1 0 1 1 0 -1 0 -1 > >> >> > 0 1 > >> >> > attr(,"assign") > >> >> > [1] 0 1 2 3 3 4 5 5 6 6 7 7 > >> >> > attr(,"contrasts") > >> >> > attr(,"contrasts")$X3 > >> >> > [1] "contr.treatment" > >> >> > > >> >> > -------------- > >> >> > > >> >> > Specifying the full hierarchy gives us what we expect: the > >> >> > interaction > >> >> > columns are simply calculated from the product of the main effect > >> >> columns. > >> >> > The interaction term X1:X2:X3 gives us two columns in the model > >> >> > matrix, > >> >> > X1:X2:X3B and X1:X2:X3C, matching the products of the main effects. > >> >> > > >> >> > If we remove either the X2:X3 interaction or the X1:X3 interaction, > >> >> > we > >> >> get > >> >> > what we would expect for the X1:X2:X3 interaction, but when we > remove > >> >> > the > >> >> > X1:X2 interaction the encoding for X1:X2:X3 changes completely: > >> >> > > >> >> > -------------- > >> >> > > >> >> >> model.matrix(~(X1+X2+X3)^3-X1:X3,data=runmatrix) (Intercept) > X1 X2 > >> >> X3B X3C X1:X2 X2:X3B X2:X3C X1:X2:X3B X1:X2:X3C > >> >> > 1 1 1 1 0 0 1 0 0 0 > 0 > >> >> > 2 1 -1 1 0 0 -1 0 0 0 > 0 > >> >> > 3 1 1 -1 0 0 -1 0 0 0 > 0 > >> >> > 4 1 -1 -1 0 0 1 0 0 0 > 0 > >> >> > 5 1 1 1 1 0 1 1 0 1 > 0 > >> >> > 6 1 -1 1 1 0 -1 1 0 -1 > 0 > >> >> > 7 1 1 -1 1 0 -1 -1 0 -1 > 0 > >> >> > 8 1 -1 -1 1 0 1 -1 0 1 > 0 > >> >> > 9 1 1 1 0 1 1 0 1 0 > 1 > >> >> > 10 1 -1 1 0 1 -1 0 1 0 > -1 > >> >> > 11 1 1 -1 0 1 -1 0 -1 0 > -1 > >> >> > 12 1 -1 -1 0 1 1 0 -1 0 > 1 > >> >> > attr(,"assign") > >> >> > [1] 0 1 2 3 3 4 5 5 6 6 > >> >> > attr(,"contrasts") > >> >> > attr(,"contrasts")$X3 > >> >> > [1] "contr.treatment" > >> >> > > >> >> > > >> >> > > >> >> >> model.matrix(~(X1+X2+X3)^3-X2:X3,data=runmatrix) (Intercept) > X1 X2 > >> >> X3B X3C X1:X2 X1:X3B X1:X3C X1:X2:X3B X1:X2:X3C > >> >> > 1 1 1 1 0 0 1 0 0 0 > 0 > >> >> > 2 1 -1 1 0 0 -1 0 0 0 > 0 > >> >> > 3 1 1 -1 0 0 -1 0 0 0 > 0 > >> >> > 4 1 -1 -1 0 0 1 0 0 0 > 0 > >> >> > 5 1 1 1 1 0 1 1 0 1 > 0 > >> >> > 6 1 -1 1 1 0 -1 -1 0 -1 > 0 > >> >> > 7 1 1 -1 1 0 -1 1 0 -1 > 0 > >> >> > 8 1 -1 -1 1 0 1 -1 0 1 > 0 > >> >> > 9 1 1 1 0 1 1 0 1 0 > 1 > >> >> > 10 1 -1 1 0 1 -1 0 -1 0 > -1 > >> >> > 11 1 1 -1 0 1 -1 0 1 0 > -1 > >> >> > 12 1 -1 -1 0 1 1 0 -1 0 > 1 > >> >> > attr(,"assign") > >> >> > [1] 0 1 2 3 3 4 5 5 6 6 > >> >> > attr(,"contrasts") > >> >> > attr(,"contrasts")$X3 > >> >> > [1] "contr.treatment" > >> >> > > >> >> > > >> >> >> model.matrix(~(X1+X2+X3)^3-X1:X2,data=runmatrix) (Intercept) > X1 X2 > >> >> X3B X3C X1:X3B X1:X3C X2:X3B X2:X3C X1:X2:X3A X1:X2:X3B X1:X2:X3C > >> >> > 1 1 1 1 0 0 0 0 0 0 1 > >> >> > 0 0 > >> >> > 2 1 -1 1 0 0 0 0 0 0 -1 > >> >> > 0 0 > >> >> > 3 1 1 -1 0 0 0 0 0 0 -1 > >> >> > 0 0 > >> >> > 4 1 -1 -1 0 0 0 0 0 0 1 > >> >> > 0 0 > >> >> > 5 1 1 1 1 0 1 0 1 0 0 > >> >> > 1 0 > >> >> > 6 1 -1 1 1 0 -1 0 1 0 0 > >> >> > -1 0 > >> >> > 7 1 1 -1 1 0 1 0 -1 0 0 > >> >> > -1 0 > >> >> > 8 1 -1 -1 1 0 -1 0 -1 0 0 > >> >> > 1 0 > >> >> > 9 1 1 1 0 1 0 1 0 1 0 > >> >> > 0 1 > >> >> > 10 1 -1 1 0 1 0 -1 0 1 0 > >> >> > 0 -1 > >> >> > 11 1 1 -1 0 1 0 1 0 -1 0 > >> >> > 0 -1 > >> >> > 12 1 -1 -1 0 1 0 -1 0 -1 0 > >> >> > 0 1 > >> >> > attr(,"assign") > >> >> > [1] 0 1 2 3 3 4 4 5 5 6 6 6 > >> >> > attr(,"contrasts") > >> >> > attr(,"contrasts")$X3 > >> >> > [1] "contr.treatment" > >> >> > > >> >> > -------------- > >> >> > > >> >> > Here, we now see the encoding for the interaction X1:X2:X3 is now > the > >> >> > interaction of X1 and X2 with a new encoding for X3 where each > factor > >> >> level > >> >> > is represented by its own column. I would expect, given the two > >> >> > column > >> >> > dummy variable encoding for X3, that the X1:X2:X3 column would also > >> >> > be > >> >> two > >> >> > columns regardless of what two-factor interactions we also > specified, > >> >> > but > >> >> > in this case it switches to three. If other two factor interactions > >> >> > are > >> >> > missing in addition to X1:X2, this issue still occurs. This also > >> >> > happens > >> >> > regardless of the contrast specified in contrasts.arg for X3. I > don't > >> >> > see > >> >> > any reasoning for this behavior given in the documentation, so I > >> >> > suspect > >> >> it > >> >> > is a bug. > >> >> > > >> >> > Best regards, > >> >> > Tyler Morgan-Wall > >> >> > > >> >> > [[alternative HTML version deleted]] > >> >> > > >> >> > ______________________________________________ > >> >> > R-devel at r-project.org mailing list > >> >> > https://stat.ethz.ch/mailman/listinfo/r-devel > >> >> > >> > > >> > [[alternative HTML version deleted]] > >> > > >> > ______________________________________________ > >> > R-devel at r-project.org mailing list > >> > https://stat.ethz.ch/mailman/listinfo/r-devel > > > > >[[alternative HTML version deleted]]
Arie ten Cate
2017-Nov-04 10:50 UTC
[Rd] Bug in model.matrix.default for higher-order interaction encoding when specific model terms are missing
Hello Tyler,
I rephrase my previous mail, as follows:
In your example, T_i = X1:X2:X3. Let F_j = X3. (The numerical
variables X1 and X2 are not encoded at all.) Then T_{i(j)} = X1:X2,
which in the example is dropped from the model. Hence the X3 in T_i
must be encoded by dummy variables, as indeed it is.
Arie
On Thu, Nov 2, 2017 at 4:11 PM, Tyler <tylermw at gmail.com>
wrote:> Hi Arie,
>
> The book out of which this behavior is based does not use factor (in this
> section) to refer to categorical factor. I will again point to this
> sentence, from page 40, in the same section and referring to the behavior
> under question, that shows F_j is not limited to categorical factors:
> "Numeric variables appear in the computations as themselves, uncoded.
> Therefore, the rule does not do anything special for them, and it remains
> valid, in a trivial sense, whenever any of the F_j is numeric rather than
> categorical."
>
> Note the "... whenever any of the F_j is numeric rather than
categorical."
> Factor here is used in the more general sense of the word, not referring to
> the R type "factor." The behavior of R does not match the
heuristic that
> it's citing.
>
> Best regards,
> Tyler
>
> On Thu, Nov 2, 2017 at 2:51 AM, Arie ten Cate <arietencate at
gmail.com> wrote:
>>
>> Hello Tyler,
>>
>> Thank you for searching for, and finding, the basic description of the
>> behavior of R in this matter.
>>
>> I think your example is in agreement with the book.
>>
>> But let me first note the following. You write: "F_j refers to a
>> factor (variable) in a model and not a categorical factor".
However:
>> "a factor is a vector object used to specify a discrete
>> classification" (start of chapter 4 of "An Introduction to
R".) You
>> might also see the description of the R function factor().
>>
>> You note that the book says about a factor F_j:
>> "... F_j is coded by contrasts if T_{i(j)} has appeared in the
>> formula and by dummy variables if it has not"
>>
>> You find:
>> "However, the example I gave demonstrated that this dummy
variable
>> encoding only occurs for the model where the missing term is the
>> numeric-numeric interaction, ~(X1+X2+X3)^3-X1:X2."
>>
>> We have here T_i = X1:X2:X3. Also: F_j = X3 (the only factor). Then
>> T_{i(j)} = X1:X2, which is dropped from the model. Hence the X3 in T_i
>> must be encoded by dummy variables, as indeed it is.
>>
>> Arie
>>
>> On Tue, Oct 31, 2017 at 4:01 PM, Tyler <tylermw at gmail.com>
wrote:
>> > Hi Arie,
>> >
>> > Thank you for your further research into the issue.
>> >
>> > Regarding Stata: On the other hand, JMP gives model matrices that
use
>> > the
>> > main effects contrasts in computing the higher order interactions,
>> > without
>> > the dummy variable encoding. I verified this both by analyzing the
>> > linear
>> > model given in my first example and noting that JMP has one more
degree
>> > of
>> > freedom than R for the same model, as well as looking at the
generated
>> > model
>> > matrices. It's easy to find a design where JMP will allow us
fit our
>> > model
>> > with goodness-of-fit estimates and R will not due to the extra
degree(s)
>> > of
>> > freedom required. Let's keep the conversation limited to R.
>> >
>> > I want to refocus back onto my original bug report, which was not
for a
>> > missing main effects term, but rather for a missing lower-order
>> > interaction
>> > term. The behavior of model.matrix.default() for a missing main
effects
>> > term
>> > is a nice example to demonstrate how model.matrix encodes with
dummy
>> > variables instead of contrasts, but doesn't demonstrate the
inconsistent
>> > behavior my bug report highlighted.
>> >
>> > I went looking for documentation on this behavior, and the issue
stems
>> > not
>> > from model.matrix.default(), but rather the terms() function in
>> > interpreting
>> > the formula. This "clever" replacement of contrasts by
dummy variables
>> > to
>> > maintain marginality (presuming that's the reason) is not
described
>> > anywhere
>> > in the documentation for either the model.matrix() or the terms()
>> > function.
>> > In order to find a description for the behavior, I had to look in
the
>> > underlying C code, buried above the "TermCode" function
of the "model.c"
>> > file, which says:
>> >
>> > "TermCode decides on the encoding of a model term. Returns 1
if variable
>> > ``whichBit'' in ``thisTerm'' is to be encoded by
contrasts and 2 if it
>> > is to
>> > be encoded by dummy variables. This is decided using the
heuristic
>> > described in Statistical Models in S, page 38."
>> >
>> > I do not have a copy of this book, and I suspect most R users do
not as
>> > well. Thankfully, however, some of the pages describing this
behavior
>> > were
>> > available as part of Amazon's "Look Inside"
feature--but if not for
>> > that, I
>> > would have no idea what heuristic R was using. Since those pages
could
>> > made
>> > unavailable by Amazon at any time, at the very least we have an
problem
>> > with
>> > a lack of documentation.
>> >
>> > However, I still believe there is a bug when comparing R's
>> > implementation to
>> > the heuristic described in the book. From Statistical Models in S,
page
>> > 38-39:
>> >
>> > "Suppose F_j is any factor included in term T_i. Let T_{i(j)}
denote the
>> > margin of T_i for factor F_j--that is, the term obtained by
dropping F_j
>> > from T_i. We say that T_{i(j)} has appeared in the formula if
there is
>> > some
>> > term T_i' for i' < i such that T_i' contains all
the factors appearing
>> > in
>> > T_{i(j)}. The usual case is that T_{i(j)} itself is one of the
preceding
>> > terms. Then F_j is coded by contrasts if T_{i(j)} has appeared in
the
>> > formula and by dummy variables if it has not"
>> >
>> > Here, F_j refers to a factor (variable) in a model and not a
categorical
>> > factor, as specified later in that section (page 40):
"Numeric variables
>> > appear in the computations as themselves, uncoded. Therefore, the
rule
>> > does
>> > not do anything special for them, and it remains valid, in a
trivial
>> > sense,
>> > whenever any of the F_j is numeric rather than categorical."
>> >
>> > Going back to my original example with three variables: X1
(numeric), X2
>> > (numeric), X3 (categorical). This heuristic prescribes encoding
X1:X2:X3
>> > with contrasts as long as X1:X2, X1:X3, and X2:X3 exist in the
formula.
>> > When
>> > any of the preceding terms do not exist, this heuristic tells us
to use
>> > dummy variables to encode the interaction (e.g. "F_j [the
interaction
>> > term]
>> > is coded ... by dummy variables if it [any of the marginal terms
>> > obtained by
>> > dropping a single factor in the interaction] has not [appeared in
the
>> > formula]"). However, the example I gave demonstrated that
this dummy
>> > variable encoding only occurs for the model where the missing term
is
>> > the
>> > numeric-numeric interaction, "~(X1+X2+X3)^3-X1:X2".
Otherwise, the
>> > interaction term X1:X2:X3 is encoded by contrasts, not dummy
variables.
>> > This
>> > is inconsistent with the description of the intended behavior
given in
>> > the
>> > book.
>> >
>> > Best regards,
>> > Tyler
>> >
>> >
>> > On Fri, Oct 27, 2017 at 2:18 PM, Arie ten Cate <arietencate at
gmail.com>
>> > wrote:
>> >>
>> >> Hello Tyler,
>> >>
>> >> I want to bring to your attention the following document:
"What
>> >> happens if you omit the main effect in a regression model with
an
>> >> interaction?"
>> >>
>> >>
(https://stats.idre.ucla.edu/stata/faq/what-happens-if-you-omit-the-main-effect-in-a-regression-model-with-an-interaction).
>> >> This gives a useful review of the problem. Your example is
Case 2: a
>> >> continuous and a categorical regressor.
>> >>
>> >> The numerical examples are coded in Stata, and they give the
same
>> >> result as in R. Hence, if this is a bug in R then it is also a
bug in
>> >> Stata. That seems very unlikely.
>> >>
>> >> Here is a simulation in R of the above mentioned Case 2 in
Stata:
>> >>
>> >> df <-
expand.grid(socst=c(-1:1),grp=c("1","2","3","4"))
>> >> print("Full model")
>> >> print(model.matrix(~(socst+grp)^2 ,data=df))
>> >> print("Example 2.1: drop socst")
>> >> print(model.matrix(~(socst+grp)^2 -socst ,data=df))
>> >> print("Example 2.2: drop grp")
>> >> print(model.matrix(~(socst+grp)^2 -grp ,data=df))
>> >>
>> >> This gives indeed the following regressors:
>> >>
>> >> "Full model"
>> >> (Intercept) socst grp2 grp3 grp4 socst:grp2 socst:grp3
socst:grp4
>> >> "Example 2.1: drop socst"
>> >> (Intercept) grp2 grp3 grp4 socst:grp1 socst:grp2 socst:grp3
socst:grp4
>> >> "Example 2.2: drop grp"
>> >> (Intercept) socst socst:grp2 socst:grp3 socst:grp4
>> >>
>> >> There is a little bit of R documentation about this, based on
the
>> >> concept of marginality, which typically forbids a model having
an
>> >> interaction but not the corresponding main effects. (You might
see the
>> >> references in
https://en.wikipedia.org/wiki/Principle_of_marginality )
>> >> See "An Introduction to R", by Venables and
Smith and the R Core
>> >> Team. At the bottom of page 52 (PDF: 57) it says:
"Although the
>> >> details are complicated, model formulae in R will normally
generate
>> >> the models that an expert statistician would expect, provided
that
>> >> marginality is preserved. Fitting, for [a contrary] example, a
model
>> >> with an interaction but not the corresponding main effects
will in
>> >> general lead to surprising results ....".
>> >> The Reference Manual states that the R functions
dropterm() and
>> >> addterm() resp. drop or add only terms such that marginality
is
>> >> preserved.
>> >>
>> >> Finally, about your singular matrix t(mm)%*%mm. This is in
fact
>> >> Example 2.1 in Case 2 discussed above. As discussed there, in
Stata
>> >> and in R the drop of the continuous variable has no effect on
the
>> >> degrees of freedom here: it is just a reparameterisation of
the full
>> >> model, protecting you against losing marginality... Hence the
>> >> model.matrix 'mm' is still square and nonsingular
after the drop of
>> >> X1, unless of course when a row is removed from the matrix
'design'
>> >> when before creating 'mm'.
>> >>
>> >> Arie
>> >>
>> >> On Sun, Oct 15, 2017 at 7:05 PM, Tyler <tylermw at
gmail.com> wrote:
>> >> > You could possibly try to explain away the behavior for a
missing
>> >> > main
>> >> > effects term, since without the main effects term we
don't have main
>> >> > effect
>> >> > columns in the model matrix used to compute the
interaction columns
>> >> > (At
>> >> > best this is undocumented behavior--I still think
it's a bug, as we
>> >> > know
>> >> > how we would encode the categorical factors if they were
in fact
>> >> > present.
>> >> > It's either specified in contrasts.arg or using the
default set in
>> >> > options). However, when all the main effects are present,
why would
>> >> > the
>> >> > three-factor interaction column not simply be the product
of the main
>> >> > effect columns? In my example: we know X1, we know X2,
and we know
>> >> > X3.
>> >> > Why
>> >> > does the encoding of X1:X2:X3 depend on whether we
specified a
>> >> > two-factor
>> >> > interaction, AND only changes for specific missing
interactions?
>> >> >
>> >> > In addition, I can use a two-term example similar to
yours to show
>> >> > how
>> >> > this
>> >> > behavior results in a singular covariance matrix when,
given the
>> >> > desired
>> >> > factor encoding, it should not be singular.
>> >> >
>> >> > We start with a full factorial design for a two-level
continuous
>> >> > factor
>> >> > and
>> >> > a three-level categorical factor, and remove a single
row. This
>> >> > design
>> >> > matrix does not leave enough degrees of freedom to
determine
>> >> > goodness-of-fit, but should allow us to obtain parameter
estimates.
>> >> >
>> >> >> design =
expand.grid(X1=c(1,-1),X2=c("A","B","C"))
>> >> >> design = design[-1,]
>> >> >> design
>> >> > X1 X2
>> >> > 2 -1 A
>> >> > 3 1 B
>> >> > 4 -1 B
>> >> > 5 1 C
>> >> > 6 -1 C
>> >> >
>> >> > Here, we first calculate the model matrix for the full
model, and
>> >> > then
>> >> > manually remove the X1 column from the model matrix. This
gives us
>> >> > the
>> >> > model matrix one would expect if X1 were removed from the
model. We
>> >> > then
>> >> > successfully calculate the covariance matrix.
>> >> >
>> >> >> mm = model.matrix(~(X1+X2)^2,data=design)
>> >> >> mm
>> >> > (Intercept) X1 X2B X2C X1:X2B X1:X2C
>> >> > 2 1 -1 0 0 0 0
>> >> > 3 1 1 1 0 1 0
>> >> > 4 1 -1 1 0 -1 0
>> >> > 5 1 1 0 1 0 1
>> >> > 6 1 -1 0 1 0 -1
>> >> >
>> >> >> mm = mm[,-2]
>> >> >> solve(t(mm) %*% mm)
>> >> > (Intercept) X2B X2C X1:X2B X1:X2C
>> >> > (Intercept) 1 -1.0 -1.0 0.0 0.0
>> >> > X2B -1 1.5 1.0 0.0 0.0
>> >> > X2C -1 1.0 1.5 0.0 0.0
>> >> > X1:X2B 0 0.0 0.0 0.5 0.0
>> >> > X1:X2C 0 0.0 0.0 0.0 0.5
>> >> >
>> >> > Here, we see the actual behavior for model.matrix. The
undesired
>> >> > re-coding
>> >> > of the model matrix interaction term makes the
information matrix
>> >> > singular.
>> >> >
>> >> >> mm = model.matrix(~(X1+X2)^2-X1,data=design)
>> >> >> mm
>> >> > (Intercept) X2B X2C X1:X2A X1:X2B X1:X2C
>> >> > 2 1 0 0 -1 0 0
>> >> > 3 1 1 0 0 1 0
>> >> > 4 1 1 0 0 -1 0
>> >> > 5 1 0 1 0 0 1
>> >> > 6 1 0 1 0 0 -1
>> >> >
>> >> >> solve(t(mm) %*% mm)
>> >> > Error in solve.default(t(mm) %*% mm) : system is
computationally
>> >> > singular:
>> >> > reciprocal condition number = 5.55112e-18
>> >> >
>> >> > I still believe this is a bug.
>> >> >
>> >> > Best regards,
>> >> > Tyler Morgan-Wall
>> >> >
>> >> > On Sun, Oct 15, 2017 at 1:49 AM, Arie ten Cate
>> >> > <arietencate at gmail.com>
>> >> > wrote:
>> >> >
>> >> >> I think it is not a bug. It is a general property of
interactions.
>> >> >> This property is best observed if all variables are
factors
>> >> >> (qualitative).
>> >> >>
>> >> >> For example, you have three variables (factors). You
ask for as many
>> >> >> interactions as possible, except an interaction term
between two
>> >> >> particular variables. When this interaction is not a
constant, it is
>> >> >> different for different values of the remaining
variable. More
>> >> >> precisely: for all values of that variable. In other
words: you have
>> >> >> a
>> >> >> three-way interaction, with all values of that
variable.
>> >> >>
>> >> >> An even smaller example is the following script with
only two
>> >> >> variables, each being a factor:
>> >> >>
>> >> >> df <-
expand.grid(X1=c("p","q"),
X2=c("A","B","C"))
>> >> >> print(model.matrix(~(X1+X2)^2 ,data=df))
>> >> >> print(model.matrix(~(X1+X2)^2 -X1,data=df))
>> >> >> print(model.matrix(~(X1+X2)^2 -X2,data=df))
>> >> >>
>> >> >> The result is:
>> >> >>
>> >> >> (Intercept) X1q X2B X2C X1q:X2B X1q:X2C
>> >> >> 1 1 0 0 0 0 0
>> >> >> 2 1 1 0 0 0 0
>> >> >> 3 1 0 1 0 0 0
>> >> >> 4 1 1 1 0 1 0
>> >> >> 5 1 0 0 1 0 0
>> >> >> 6 1 1 0 1 0 1
>> >> >>
>> >> >> (Intercept) X2B X2C X1q:X2A X1q:X2B X1q:X2C
>> >> >> 1 1 0 0 0 0 0
>> >> >> 2 1 0 0 1 0 0
>> >> >> 3 1 1 0 0 0 0
>> >> >> 4 1 1 0 0 1 0
>> >> >> 5 1 0 1 0 0 0
>> >> >> 6 1 0 1 0 0 1
>> >> >>
>> >> >> (Intercept) X1q X1p:X2B X1q:X2B X1p:X2C X1q:X2C
>> >> >> 1 1 0 0 0 0 0
>> >> >> 2 1 1 0 0 0 0
>> >> >> 3 1 0 1 0 0 0
>> >> >> 4 1 1 0 1 0 0
>> >> >> 5 1 0 0 0 1 0
>> >> >> 6 1 1 0 0 0 1
>> >> >>
>> >> >> Thus, in the second result, we have no main effect of
X1. Instead,
>> >> >> the
>> >> >> effect of X1 depends on the value of X2; either A or
B or C. In
>> >> >> fact,
>> >> >> this is a two-way interaction, including all three
values of X2. In
>> >> >> the third result, we have no main effect of X2, The
effect of X2
>> >> >> depends on the value of X1; either p or q.
>> >> >>
>> >> >> A complicating element with your example seems to be
that your X1
>> >> >> and
>> >> >> X2 are not factors.
>> >> >>
>> >> >> Arie
>> >> >>
>> >> >> On Thu, Oct 12, 2017 at 7:12 PM, Tyler <tylermw at
gmail.com> wrote:
>> >> >> > Hi,
>> >> >> >
>> >> >> > I recently ran into an inconsistency in the way
>> >> >> > model.matrix.default
>> >> >> > handles factor encoding for higher level
interactions with
>> >> >> > categorical
>> >> >> > variables when the full hierarchy of effects is
not present.
>> >> >> > Depending on
>> >> >> > which lower level interactions are specified,
the factor encoding
>> >> >> > changes
>> >> >> > for a higher level interaction. Consider the
following minimal
>> >> >> reproducible
>> >> >> > example:
>> >> >> >
>> >> >> > --------------
>> >> >> >
>> >> >> >> runmatrix =
expand.grid(X1=c(1,-1),X2=c(1,-1),X3=c("A","B","C"))>
>> >> >> model.matrix(~(X1+X2+X3)^3,data=runmatrix)
(Intercept) X1 X2 X3B
>> >> >> X3C
>> >> >> X1:X2 X1:X3B X1:X3C X2:X3B X2:X3C X1:X2:X3B X1:X2:X3C
>> >> >> > 1 1 1 1 0 0 1 0 0
0 0
>> >> >> > 0 0
>> >> >> > 2 1 -1 1 0 0 -1 0 0
0 0
>> >> >> > 0 0
>> >> >> > 3 1 1 -1 0 0 -1 0 0
0 0
>> >> >> > 0 0
>> >> >> > 4 1 -1 -1 0 0 1 0 0
0 0
>> >> >> > 0 0
>> >> >> > 5 1 1 1 1 0 1 1 0
1 0
>> >> >> > 1 0
>> >> >> > 6 1 -1 1 1 0 -1 -1 0
1 0
>> >> >> > -1 0
>> >> >> > 7 1 1 -1 1 0 -1 1 0
-1 0
>> >> >> > -1 0
>> >> >> > 8 1 -1 -1 1 0 1 -1 0
-1 0
>> >> >> > 1 0
>> >> >> > 9 1 1 1 0 1 1 0 1
0 1
>> >> >> > 0 1
>> >> >> > 10 1 -1 1 0 1 -1 0 -1
0 1
>> >> >> > 0 -1
>> >> >> > 11 1 1 -1 0 1 -1 0 1
0 -1
>> >> >> > 0 -1
>> >> >> > 12 1 -1 -1 0 1 1 0 -1
0 -1
>> >> >> > 0 1
>> >> >> > attr(,"assign")
>> >> >> > [1] 0 1 2 3 3 4 5 5 6 6 7 7
>> >> >> > attr(,"contrasts")
>> >> >> > attr(,"contrasts")$X3
>> >> >> > [1] "contr.treatment"
>> >> >> >
>> >> >> > --------------
>> >> >> >
>> >> >> > Specifying the full hierarchy gives us what we
expect: the
>> >> >> > interaction
>> >> >> > columns are simply calculated from the product
of the main effect
>> >> >> columns.
>> >> >> > The interaction term X1:X2:X3 gives us two
columns in the model
>> >> >> > matrix,
>> >> >> > X1:X2:X3B and X1:X2:X3C, matching the products
of the main
>> >> >> > effects.
>> >> >> >
>> >> >> > If we remove either the X2:X3 interaction or the
X1:X3
>> >> >> > interaction,
>> >> >> > we
>> >> >> get
>> >> >> > what we would expect for the X1:X2:X3
interaction, but when we
>> >> >> > remove
>> >> >> > the
>> >> >> > X1:X2 interaction the encoding for X1:X2:X3
changes completely:
>> >> >> >
>> >> >> > --------------
>> >> >> >
>> >> >> >>
model.matrix(~(X1+X2+X3)^3-X1:X3,data=runmatrix) (Intercept) X1
>> >> >> >> X2
>> >> >> X3B X3C X1:X2 X2:X3B X2:X3C X1:X2:X3B X1:X2:X3C
>> >> >> > 1 1 1 1 0 0 1 0 0
0
>> >> >> > 0
>> >> >> > 2 1 -1 1 0 0 -1 0 0
0
>> >> >> > 0
>> >> >> > 3 1 1 -1 0 0 -1 0 0
0
>> >> >> > 0
>> >> >> > 4 1 -1 -1 0 0 1 0 0
0
>> >> >> > 0
>> >> >> > 5 1 1 1 1 0 1 1 0
1
>> >> >> > 0
>> >> >> > 6 1 -1 1 1 0 -1 1 0
-1
>> >> >> > 0
>> >> >> > 7 1 1 -1 1 0 -1 -1 0
-1
>> >> >> > 0
>> >> >> > 8 1 -1 -1 1 0 1 -1 0
1
>> >> >> > 0
>> >> >> > 9 1 1 1 0 1 1 0 1
0
>> >> >> > 1
>> >> >> > 10 1 -1 1 0 1 -1 0 1
0
>> >> >> > -1
>> >> >> > 11 1 1 -1 0 1 -1 0 -1
0
>> >> >> > -1
>> >> >> > 12 1 -1 -1 0 1 1 0 -1
0
>> >> >> > 1
>> >> >> > attr(,"assign")
>> >> >> > [1] 0 1 2 3 3 4 5 5 6 6
>> >> >> > attr(,"contrasts")
>> >> >> > attr(,"contrasts")$X3
>> >> >> > [1] "contr.treatment"
>> >> >> >
>> >> >> >
>> >> >> >
>> >> >> >>
model.matrix(~(X1+X2+X3)^3-X2:X3,data=runmatrix) (Intercept) X1
>> >> >> >> X2
>> >> >> X3B X3C X1:X2 X1:X3B X1:X3C X1:X2:X3B X1:X2:X3C
>> >> >> > 1 1 1 1 0 0 1 0 0
0
>> >> >> > 0
>> >> >> > 2 1 -1 1 0 0 -1 0 0
0
>> >> >> > 0
>> >> >> > 3 1 1 -1 0 0 -1 0 0
0
>> >> >> > 0
>> >> >> > 4 1 -1 -1 0 0 1 0 0
0
>> >> >> > 0
>> >> >> > 5 1 1 1 1 0 1 1 0
1
>> >> >> > 0
>> >> >> > 6 1 -1 1 1 0 -1 -1 0
-1
>> >> >> > 0
>> >> >> > 7 1 1 -1 1 0 -1 1 0
-1
>> >> >> > 0
>> >> >> > 8 1 -1 -1 1 0 1 -1 0
1
>> >> >> > 0
>> >> >> > 9 1 1 1 0 1 1 0 1
0
>> >> >> > 1
>> >> >> > 10 1 -1 1 0 1 -1 0 -1
0
>> >> >> > -1
>> >> >> > 11 1 1 -1 0 1 -1 0 1
0
>> >> >> > -1
>> >> >> > 12 1 -1 -1 0 1 1 0 -1
0
>> >> >> > 1
>> >> >> > attr(,"assign")
>> >> >> > [1] 0 1 2 3 3 4 5 5 6 6
>> >> >> > attr(,"contrasts")
>> >> >> > attr(,"contrasts")$X3
>> >> >> > [1] "contr.treatment"
>> >> >> >
>> >> >> >
>> >> >> >>
model.matrix(~(X1+X2+X3)^3-X1:X2,data=runmatrix) (Intercept) X1
>> >> >> >> X2
>> >> >> X3B X3C X1:X3B X1:X3C X2:X3B X2:X3C X1:X2:X3A
X1:X2:X3B X1:X2:X3C
>> >> >> > 1 1 1 1 0 0 0 0
0 0 1
>> >> >> > 0 0
>> >> >> > 2 1 -1 1 0 0 0 0
0 0 -1
>> >> >> > 0 0
>> >> >> > 3 1 1 -1 0 0 0 0
0 0 -1
>> >> >> > 0 0
>> >> >> > 4 1 -1 -1 0 0 0 0
0 0 1
>> >> >> > 0 0
>> >> >> > 5 1 1 1 1 0 1 0
1 0 0
>> >> >> > 1 0
>> >> >> > 6 1 -1 1 1 0 -1 0
1 0 0
>> >> >> > -1 0
>> >> >> > 7 1 1 -1 1 0 1 0
-1 0 0
>> >> >> > -1 0
>> >> >> > 8 1 -1 -1 1 0 -1 0
-1 0 0
>> >> >> > 1 0
>> >> >> > 9 1 1 1 0 1 0 1
0 1 0
>> >> >> > 0 1
>> >> >> > 10 1 -1 1 0 1 0 -1
0 1 0
>> >> >> > 0 -1
>> >> >> > 11 1 1 -1 0 1 0 1
0 -1 0
>> >> >> > 0 -1
>> >> >> > 12 1 -1 -1 0 1 0 -1
0 -1 0
>> >> >> > 0 1
>> >> >> > attr(,"assign")
>> >> >> > [1] 0 1 2 3 3 4 4 5 5 6 6 6
>> >> >> > attr(,"contrasts")
>> >> >> > attr(,"contrasts")$X3
>> >> >> > [1] "contr.treatment"
>> >> >> >
>> >> >> > --------------
>> >> >> >
>> >> >> > Here, we now see the encoding for the
interaction X1:X2:X3 is now
>> >> >> > the
>> >> >> > interaction of X1 and X2 with a new encoding for
X3 where each
>> >> >> > factor
>> >> >> level
>> >> >> > is represented by its own column. I would
expect, given the two
>> >> >> > column
>> >> >> > dummy variable encoding for X3, that the
X1:X2:X3 column would
>> >> >> > also
>> >> >> > be
>> >> >> two
>> >> >> > columns regardless of what two-factor
interactions we also
>> >> >> > specified,
>> >> >> > but
>> >> >> > in this case it switches to three. If other two
factor
>> >> >> > interactions
>> >> >> > are
>> >> >> > missing in addition to X1:X2, this issue still
occurs. This also
>> >> >> > happens
>> >> >> > regardless of the contrast specified in
contrasts.arg for X3. I
>> >> >> > don't
>> >> >> > see
>> >> >> > any reasoning for this behavior given in the
documentation, so I
>> >> >> > suspect
>> >> >> it
>> >> >> > is a bug.
>> >> >> >
>> >> >> > Best regards,
>> >> >> > Tyler Morgan-Wall
>> >> >> >
>> >> >> > [[alternative HTML version deleted]]
>> >> >> >
>> >> >> > ______________________________________________
Possibly Parallel Threads
- Bug in model.matrix.default for higher-order interaction encoding when specific model terms are missing
- Bug in model.matrix.default for higher-order interaction encoding when specific model terms are missing
- Bug in model.matrix.default for higher-order interaction encoding when specific model terms are missing
- Bug in model.matrix.default for higher-order interaction encoding when specific model terms are missing
- Bug in model.matrix.default for higher-order interaction encoding when specific model terms are missing