

Frank,
I'm not sure what is going on. The following test function works for me in both 3.1.1
and 3.2, i.e, the second model matrix has fewer columns. As I indicated to you earlier,
the coxph code removes the strata() columns after creating X because I found it easier to
correctly create the assign attribute.
Can you create a worked example?
require(survival)
testfun < function(formula, data) {
tform < terms(formula, specials="strata")
mf < model.frame(tform, data)
terms2 < terms(mf)
strat < untangle.specials(terms2, "strata")
if (length(strat$terms)) terms2 < terms2[strat$terms]
X < model.matrix(terms2, mf)
X
}
tdata < data.frame(y= 1:10, zed = 1:10, grp = factor(c(1,1,1,2,2,2,1,1,3,3)))
testfun(y ~ zed*grp, tdata)
testfun(y ~ strata(grp)*zed, tdata)
Terry T.
 original message 
For building design matrices for Cox proportional hazards models in the
cph function in the rms package I have always used this construct:
Terms < terms(formula, specials=c("strat", "cluster", "strata"), data=data)
specials < attr(Terms, 'specials')
stra < specials$strat
Terms.ns < Terms
if(length(stra)) {
temp < untangle.specials(Terms.ns, "strat", 1)
Terms.ns < Terms.ns[ temp$terms] #uses [.terms function
}
X < model.matrix(Terms.ns, X)[, 1, drop=FALSE]
The Terms.ns logic removes stratification factor "main effects" so that
if a stratification factor interacts with a nonstratification factor,
only the interaction terms are included, not the strat. factor main
effects. [In a Cox PH model stratification goes into the nonparametric
survival curve part of the model].
Lately this logic quit working; model.matrix keeps the unneeded main
effects in the design matrix. Does anyone know what changed in R that
could have caused this, and possibly a workaround?

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Terry  your example didn't demonstrate the problem because the variable
that interacted with strata (zed) was not a factor variable.
But I had stated the problem incorrectly. It's not that there are too
many strata terms; there are too many nonstrata terms when the variable
interacting with the stratification factor is a factor variable. Here
is a simple example, where I have attached no packages other than the
basic startup packages.
strat < function(x) x
d < expand.grid(a=c('a1','a2'), b=c('b1','b2'))
d$y < c(1,3,2,4)
f < y ~ a * strat(b)
m < model.frame(f, data=d)
Terms < terms(f, specials='strat', data=d)
specials < attr(Terms, 'specials')
temp < survival:::untangle.specials(Terms, 'strat', 1)
Terms < Terms[ temp$terms]
model.matrix(Terms, m)
(Intercept) aa2 aa1:strat(b)b2 aa2:strat(b)b2
1 1 0 0 0
2 1 1 0 0
3 1 0 1 0
4 1 1 0 1
. . .
The column corresponding to a='a1' b='b2' should not be there
(aa1:strat(b)b2).
This does seem to be a change in R. Any help appreciated.
Note that after subsetting out strat terms using Terms[  temp$terms],
Terms attributes factor and term.labels are:
attr(,"factors")
a a:strat(b)
y 0 0
a 1 2
strat(b) 0 1
attr(,"term.labels")
[1] "a" "a:strat(b)"
Frank
On 06/11/2015 08:44 AM, Therneau, Terry M., Ph.D. wrote:
> Frank,
> I'm not sure what is going on. The following test function works for
> me in both 3.1.1 and 3.2, i.e, the second model matrix has fewer
> columns. As I indicated to you earlier, the coxph code removes the
> strata() columns after creating X because I found it easier to correctly
> create the assign attribute.
>
> Can you create a worked example?
>
> require(survival)
> testfun < function(formula, data) {
> tform < terms(formula, specials="strata")
> mf < model.frame(tform, data)
>
> terms2 < terms(mf)
> strat < untangle.specials(terms2, "strata")
> if (length(strat$terms)) terms2 < terms2[strat$terms]
> X < model.matrix(terms2, mf)
> X
> }
>
> tdata < data.frame(y= 1:10, zed = 1:10, grp =
> factor(c(1,1,1,2,2,2,1,1,3,3)))
>
> testfun(y ~ zed*grp, tdata)
>
> testfun(y ~ strata(grp)*zed, tdata)
>
>
> Terry T.
>
>  original message 
>
> For building design matrices for Cox proportional hazards models in the
> cph function in the rms package I have always used this construct:
>
> Terms < terms(formula, specials=c("strat", "cluster", "strata"),
> data=data)
> specials < attr(Terms, 'specials')
> stra < specials$strat
> Terms.ns < Terms
> if(length(stra)) {
> temp < untangle.specials(Terms.ns, "strat", 1)
> Terms.ns < Terms.ns[ temp$terms] #uses [.terms function
> }
> X < model.matrix(Terms.ns, X)[, 1, drop=FALSE]
>
> The Terms.ns logic removes stratification factor "main effects" so that
> if a stratification factor interacts with a nonstratification factor,
> only the interaction terms are included, not the strat. factor main
> effects. [In a Cox PH model stratification goes into the nonparametric
> survival curve part of the model].
>
> Lately this logic quit working; model.matrix keeps the unneeded main
> effects in the design matrix. Does anyone know what changed in R that
> could have caused this, and possibly a workaround?
>
>
> 
>
>


Frank E Harrell Jr Professor and Chairman School of Medicine
Department of *Biostatistics* *Vanderbilt University*
______________________________________________
[hidden email] mailing list  To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide http://www.Rproject.org/postingguide.htmland provide commented, minimal, selfcontained, reproducible code.
Frank Harrell
Department of Biostatistics, Vanderbilt University


Frank,
I don't think there is any way to "fix" your problem except the way that I did it.
library(survival)
tdata < data.frame(y=c(1,3,3,5, 5,7, 7,9, 9,13),
x1=factor(letters[c(1,1,1,1,1,2,2,2,2,2)]),
x2= c(1,2,1,2,1,2,1,2,1,2))
fit1 < lm( y ~ x1 * strata(x2)  strata(x2), tdata)
coef(fit1)
(Intercept) x1b x1a:strata(x2)x2=2 x1b:strata(x2)x2=2
3.000000 5.000000 1.000000 1.666667
Your code is calling model.matrix with the same model frame and terms structure as the lm
call above (I checked). In your case you know that the underlying model has 2 intercepts
(strata), one for the group with x2=1 and another for the group with x2=2, but how is the
model.matrix routine supposed to guess that? It can't, so model.matrix returns the proper
result for the lm call. As seen above the result is not singular, while for the Cox model
it is singular due to the extra intercept.
This is simply an extension of leaving the "intercept" term in the model and then removing
that column from the returned X matrix, which is necessary to have the correct coding for
ordinary factor variables, something we've both done since day 1. In order for
model.matrix to do the right thing with interactions, it has to know how many intercepts
there actually are.
I've come to the conclusion that the entire thrust of 'contrasts' in S was wrong headed,
i.e., the "remove redundant columns from the X matrix ahead of time" logic. It is simply
not possible for the model.matrix routine to guess correctly for all y and x combinations,
something that been acknowledged in R by changing the default for "singular.ok" to TRUE.
Dealing with this after the fact via a good contrast function (a la SAS  heresy!) would
have been a much better design choice. But as long as I'm in R the coxph routine tries to
be a good citizen.
Terry T.
______________________________________________
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Thank you very much Terry. I'm still puzzled at why this worked a year
ago. What changed? I'd very much like to reverse the change by setting
an argument somewhere or manipulating the terms object.
I echo your sentiments about the general approach.
Frank
On 06/15/2015 09:05 AM, Therneau, Terry M., Ph.D. wrote:
> Frank,
> I don't think there is any way to "fix" your problem except the way
> that I did it.
>
> library(survival)
> tdata < data.frame(y=c(1,3,3,5, 5,7, 7,9, 9,13),
> x1=factor(letters[c(1,1,1,1,1,2,2,2,2,2)]),
> x2= c(1,2,1,2,1,2,1,2,1,2))
>
> fit1 < lm( y ~ x1 * strata(x2)  strata(x2), tdata)
> coef(fit1)
> (Intercept) x1b x1a:strata(x2)x2=2
> x1b:strata(x2)x2=2
> 3.000000 5.000000 1.000000 1.666667
>
> Your code is calling model.matrix with the same model frame and terms
> structure as the lm call above (I checked). In your case you know
> that the underlying model has 2 intercepts (strata), one for the group
> with x2=1 and another for the group with x2=2, but how is the
> model.matrix routine supposed to guess that? It can't, so
> model.matrix returns the proper result for the lm call. As seen above
> the result is not singular, while for the Cox model it is singular due
> to the extra intercept.
>
> This is simply an extension of leaving the "intercept" term in the
> model and then removing that column from the returned X matrix, which
> is necessary to have the correct coding for ordinary factor variables,
> something we've both done since day 1. In order for model.matrix to
> do the right thing with interactions, it has to know how many
> intercepts there actually are.
>
> I've come to the conclusion that the entire thrust of 'contrasts' in S
> was wrong headed, i.e., the "remove redundant columns from the X
> matrix ahead of time" logic. It is simply not possible for the
> model.matrix routine to guess correctly for all y and x combinations,
> something that been acknowledged in R by changing the default for
> "singular.ok" to TRUE. Dealing with this after the fact via a good
> contrast function (a la SAS  heresy!) would have been a much better
> design choice. But as long as I'm in R the coxph routine tries to be
> a good citizen.
>
> Terry T.


Frank E Harrell Jr Professor and Chairman School of Medicine
Department of *Biostatistics* *Vanderbilt University*
[[alternative HTML version deleted]]
______________________________________________
[hidden email] mailing list  To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide http://www.Rproject.org/postingguide.htmland provide commented, minimal, selfcontained, reproducible code.
Frank Harrell
Department of Biostatistics, Vanderbilt University

