From maechler at stat.math.ethz.ch  Thu Sep 14 10:23:05 2017
From: maechler at stat.math.ethz.ch (Martin Maechler)
Date: Thu, 14 Sep 2017 10:23:05 +0200
Subject: [Rd] vcov and survival
In-Reply-To: <22970.14862.518011.162206@stat.math.ethz.ch>
References: <24682_1505341169_v8DMJSsK025685_9153c6$7uhrg4@ironport10.mayo.edu>
 <ACD1644AA6C67E4FBD0C350625508EC8366C32DC@FHSDB4H16-2.csu.mcmaster.ca>
 <22970.14862.518011.162206@stat.math.ethz.ch>
Message-ID: <22970.15465.494390.45636@stat.math.ethz.ch>

>>>>> Martin Maechler <maechler at stat.math.ethz.ch>
>>>>>     on Thu, 14 Sep 2017 10:13:02 +0200 writes:

>>>>> Fox, John <jfox at mcmaster.ca>
>>>>>     on Wed, 13 Sep 2017 22:45:07 +0000 writes:

    >> Dear Terry,
    >> Even the behaviour of lm() and glm() isn't entirely consistent. In both cases, singularity results in NA coefficients by default, and these are reported in the model summary and coefficient vector, but not in the coefficient covariance matrix:

    >> ----------------	

    >>> mod.lm <- lm(Employed ~ GNP + Population + I(GNP + Population), 
    >> +              data=longley)
    >>> summary(mod.lm)

    >> Call:
    >> lm(formula = Employed ~ GNP + Population + I(GNP + Population), 
    >> data = longley)

    >> Residuals:
    >> Min       1Q   Median       3Q      Max 
    >> -0.80899 -0.33282 -0.02329  0.25895  1.08800 

    >> Coefficients: (1 not defined because of singularities)
    >> Estimate Std. Error t value Pr(>|t|)    
    >> (Intercept)         88.93880   13.78503   6.452 2.16e-05 ***
    >> GNP                  0.06317    0.01065   5.933 4.96e-05 ***
    >> Population          -0.40974    0.15214  -2.693   0.0184 *  
    >> I(GNP + Population)       NA         NA      NA       NA    
    >> ---
    >> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

    >> Residual standard error: 0.5459 on 13 degrees of freedom
    >> Multiple R-squared:  0.9791,	Adjusted R-squared:  0.9758 
    >> F-statistic: 303.9 on 2 and 13 DF,  p-value: 1.221e-11

    >>> vcov(mod.lm)
    >> (Intercept)           GNP Population
    >> (Intercept) 190.0269691  0.1445617813 -2.0954381
    >> GNP           0.1445618  0.0001133631 -0.0016054
    >> Population   -2.0954381 -0.0016053999  0.0231456
    >>> coef(mod.lm)
    >> (Intercept)                 GNP          Population I(GNP + Population) 
    >> 88.93879831          0.06317244         -0.40974292                  NA 
    >>> 
    >>> mod.glm <- glm(Employed ~ GNP + Population + I(GNP + Population), 
    >> +               data=longley)
    >>> summary(mod.glm)

    >> Call:
    >> glm(formula = Employed ~ GNP + Population + I(GNP + Population), 
    >> data = longley)

    >> Deviance Residuals: 
    >> Min        1Q    Median        3Q       Max  
    >> -0.80899  -0.33282  -0.02329   0.25895   1.08800  

    >> Coefficients: (1 not defined because of singularities)
    >> Estimate Std. Error t value Pr(>|t|)    
    >> (Intercept)         88.93880   13.78503   6.452 2.16e-05 ***
    >> GNP                  0.06317    0.01065   5.933 4.96e-05 ***
    >> Population          -0.40974    0.15214  -2.693   0.0184 *  
    >> I(GNP + Population)       NA         NA      NA       NA    
    >> ---
    >> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

    >> (Dispersion parameter for gaussian family taken to be 0.2980278)

    >> Null deviance: 185.0088  on 15  degrees of freedom
    >> Residual deviance:   3.8744  on 13  degrees of freedom
    >> AIC: 30.715

    >> Number of Fisher Scoring iterations: 2

    >>> coef(mod.glm)
    >> (Intercept)                 GNP          Population I(GNP + Population) 
    >> 88.93879831          0.06317244         -0.40974292                  NA 
    >>> vcov(mod.glm)
    >> (Intercept)           GNP Population
    >> (Intercept) 190.0269691  0.1445617813 -2.0954381
    >> GNP           0.1445618  0.0001133631 -0.0016054
    >> Population   -2.0954381 -0.0016053999  0.0231456

    >> ----------------	

    >> Moreoever, lm() has a singular.ok() argument that defaults to TRUE, but glm() doesn't have this argument:

    >> ----------------	

    >>> mod.lm <- lm(Employed ~ GNP + Population + I(GNP + Population), 
    >> +              data=longley, singular.ok=FALSE)
    >> Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 
    >> singular fit encountered

    >> ----------------	

    >> In my opinion, singularity should at least produce a warning, both in calls to lm() and glm(), and in summary() output. Even better, again in my opinion, would be to produce an error by default in this situation, but doing so would likely break too much existing code. 

    > Yes, I would not want to change.  Note that this is from S
    > already, i.e., long "ingrained".  I think there one argument was
    > that there are situations with factor predictors of many levels
    > and conceptually their 2- or even 3-way interactions (!)
    > where it is neat to just fit the model, (-> get residuals and
    > fitted values) and also see implicitly the "necessary rank" of
    > prediction space, or rather even more specifically, you see for
    > every factor how many levels are "distinguishable"/useful for
    > prediction, given the data.

    >> I prefer NA to 0 for the redundant coefficients because it at least suggests that the decision about what to exclude is arbitrary, and of course simply excluding coefficients isn't the only way to proceed. 

    > I'm less modest and would say *definitely*, NA's are highly
    > prefered in such a situation.

    >> Finally, the differences in behaviour between coef() and vcov() and between lm() and glm() aren't really sensible.

    > I really haven't seen any difference between lm() and glm() in
    > the example above.  Maybe you can point them out for me.

.. now I saw it:
   lm() has  a 'singular.ok = TRUE' argument
   which you can set to FALSE if you prefer an error to NA coefficients.

I also agree with you John that it would be nice if  glm() also
got such an argument.
Patches are welcome and seem easy. Nowadays we prefer them
as attachments (diff/patch file!) at R's
  https://bugs.r-project.org bugzilla 
against the svn source, here
  https://svn.r-project.org/R/trunk/src/library/stats/R/glm.R
and
  https://svn.r-project.org/R/trunk/src/library/stats/man/glm.Rd

    > I do quite agree that  vcov() should be compatible with
    > coef() [and summary()]  for both 'lm' and 'glm' methods, i.e.,
    > should get NA rows and columns there.  This would require
    > eliminating these before e.g. using it in solve(<vcov>, *) etc,
    > but I think it would be a good idea that the useR must deal with
    > these NAs actively.

    > Shall "we" try and see the fallout in CRAN space?

    >> Maybe there's some reason for all this that escapes me.
    > (for the first one---"no error"--- I gave a reason)

    >> Best,
    >> John

    >> --------------------------------------
    >> John Fox, Professor Emeritus
    >> McMaster University
    >> Hamilton, Ontario, Canada
    >> Web: socserv.mcmaster.ca/jfox




    >>> -----Original Message-----
    >>> From: R-devel [mailto:r-devel-bounces at r-project.org] On Behalf Of
    >>> Therneau, Terry M., Ph.D.
    >>> Sent: Wednesday, September 13, 2017 6:19 PM
    >>> To: r-devel at r-project.org
    >>> Subject: [Rd] vcov and survival
    >>> 
    >>> I have just noticed a difference in behavior between coxph and lm/glm:
    >>> if one or more of the coefficients from the fit in NA, then lm and glm
    >>> omit that row/column from the variance matrix; while coxph retains it
    >>> but sets the values to zero.
    >>> 
    >>> Is this something that should be "fixed", i.e., made to agree? I
    >>> suspect that doing so will break other packages, but then NA coefs are
    >>> rather rare so perhaps not.
    >>> 
    >>> Terry Therneau



[Rd] vcov and survival
Martin Maechler