From: John Fox <jfox_at_mcmaster.ca>

Date: Sat 19 Feb 2005 - 07:27:12 EST

John Fox

Department of Sociology

McMaster University

Hamilton, Ontario

Canada L8S 4M4

905-525-9140x23604

http://socserv.mcmaster.ca/jfox

R-devel@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-devel Received on Sat Feb 19 07:40:36 2005

Date: Sat 19 Feb 2005 - 07:27:12 EST

Brian's point that NAs, say, in x2, can influence the basis for poly(x1, 2) is disquieting, but note that this can happen now if there are no NAs in x1. The point, therefore, doesn't really justify the current behaviour of poly(). Indeed, if there are NAs in x2 but not in x1, the columns representing poly(x1, 2) won't be orthogonal in the subset of cases used in the model fit (though they do provide a correct basis for the term).

Regards,

John

John Fox

Department of Sociology

McMaster University

Hamilton, Ontario

Canada L8S 4M4

905-525-9140x23604

http://socserv.mcmaster.ca/jfox

> -----Original Message-----

*> From: r-help-bounces@stat.math.ethz.ch
**> [mailto:r-help-bounces@stat.math.ethz.ch] On Behalf Of Liaw, Andy
**> Sent: Tuesday, February 15, 2005 8:31 AM
**> To: 'Prof Brian Ripley'
**> Cc: r-help@stat.math.ethz.ch; 'Markus Jantti'
**> Subject: RE: [R] using poly in a linear regression in the
**> presence of NAf ails (despite subsetting them out)
**>
**> My apologies: It's another case of me not thinking
**> statistically... It may also help those of us whose brains
**> run at slow clock speeds to have ?poly, ?bs and ?ns mention
**> how they react to NAs.
**>
**> Best,
**> Andy
**>
**>
**> > From: Prof Brian Ripley
**> >
**> > Andy,
**> >
**> > I don't think it is a bug. The problem is that poly(x, 2)
**> depends on
**> > the possible set of x values, and so needs to know all of
**> them, unlike
**> > e.g. log(x) which is observation-by-observation. Silently omitting
**> > missing values is not a good idea in such cases, especially if the
**> > values are missing in other variables (which is what na.action is
**> > likely to do).
**> >
**> > I would say models with poly, ns, bs etc are inadvisable in the
**> > presence of missing values in their argument. We could make poly()
**> > give an informative message, though.
**> >
**> > Brian
**> >
**> >
**> > On Mon, 14 Feb 2005, Liaw, Andy wrote:
**> >
**> > > This smells like a bug to me. The error is triggered by the line:
**> > >
**> > > variables <- eval(predvars, data, env)
**> > >
**> > > inside model.frame.default(). At that point, na.action
**> has not been
**> > > applied, so poly() ended being called on data that still
**> > contains missing
**> > > values. The qr() that issued the error is for generating
**> > the orthogonal
**> > > basis when evaluating poly(), not for fitting the linear
**> > model itself.
**> > >
**> > > Essentially, calling
**> > >
**> > > model.frame(y ~ poly(x, 2), data=data.frame(x=c(NA, 1:3),
**> > y=rnorm(4)),
**> > > na.action=na.omit)
**> > >
**> > > would show the same error. The obvious workaround is to
**> > omit cases with NAs
**> > > before calling lm().
**> > >
**> > > Andy
**> > >
**> > >> From: Markus Jäntti
**> > >>
**> > >> I ran into a to me surprising result on running lm with an
**> > orthogonal
**> > >> polynomial among the predictors.
**> > >>
**> > >> The lm command resulted in
**> > >>
**> > >> Error in qr(X) : NA/NaN/Inf in foreign function call
**> (arg 1) Error
**> > >> during wrapup:
**> > >>
**> > >> despite my using a "subset" in the call to get rid of NA's.
**> > >>
**> > >> poly is apparently evaluated before any NA's are
**> subsetted out of
**> > >> the data.
**> > >>
**> > >> Example code (attached to this e-mail as as a script):
**> > >> > ## generate some data
**> > >> > n <- 50
**> > >> > x <- runif(n)
**> > >> > a0 <- 10
**> > >> > a1 <- .5
**> > >> > sigma.e <- 1
**> > >> > y <- a0 + a1*x + rnorm(n)*sigma.e tmp.d <- data.frame(y, x)
**> > >> > rm(list=c("n", "x", "a0", "a1", "sigma.e", "y"))
**> > >> >
**> > >> > print(lm.1 <- lm(y ~ poly(x, 2), data = tmp.d)
**> > >> +
**> > >> + ## now make a few NA's
**> > >> +
**> > >> + tmp.d$x[1:2] <- rep(NA, 2)
**> > >> Error: syntax error
**> > >> Error during wrapup:
**> > >> >
**> > >> > ## this fails, just as it should
**> > >> > print(lm.1 <- lm(y ~ poly(x, 2), data = tmp.d))
**> > >>
**> > >> Call:
**> > >> lm(formula = y ~ poly(x, 2), data = tmp.d)
**> > >>
**> > >> Coefficients:
**> > >> (Intercept) poly(x, 2)1 poly(x, 2)2
**> > >> 10.380 -0.242 -1.441
**> > >>
**> > >> >
**> > >> > ## these also fail, but should not?
**> > >> >
**> > >> > print(lm.2 <- lm(y ~ poly(x, 2), data = tmp.d, subset =
**> > !is.na(x)))
**> > >>
**> > >> Call:
**> > >> lm(formula = y ~ poly(x, 2), data = tmp.d, subset = !is.na(x))
**> > >>
**> > >> Coefficients:
**> > >> (Intercept) poly(x, 2)1 poly(x, 2)2
**> > >> 10.380 -0.242 -1.441
**> > >>
**> > >> > print(lm.3 <- lm(y ~ poly(x, 2), data = tmp.d, na.action =
**> > >> na.omit))
**> > >>
**> > >> Call:
**> > >> lm(formula = y ~ poly(x, 2), data = tmp.d, na.action = na.omit)
**> > >>
**> > >> Coefficients:
**> > >> (Intercept) poly(x, 2)1 poly(x, 2)2
**> > >> 10.380 -0.242 -1.441
**> > >>
**> > >> >
**> > >> > ## but this works
**> > >> >
**> > >> > print(lm.3 <- lm(y ~ poly(x, 2), data = subset(tmp.d, subset =
**> > >> !is.na(x))))
**> > >>
**> > >> Call:
**> > >> lm(formula = y ~ poly(x, 2), data = subset(tmp.d, subset =
**> > !is.na(x)))
**> > >>
**> > >> Coefficients:
**> > >> (Intercept) poly(x, 2)1 poly(x, 2)2
**> > >> 10.380 -0.242 -1.441
**> > >>
**> > >> --------------------
**> > >>
**> > >> The documentation of lm is *not* misleading at this point,
**> > saying that
**> > >>
**> > >> subset an optional vector specifying a subset of
**> > >> observations to be
**> > >> used in the fitting process.
**> > >>
**> > >> which implies that data are subsetted once lm.fit is called.
**> > >> All the same, this behavior is a little unexpected to me.
**> > >> Is it to be considered a feature, that is, does it produce
**> > beneficial
**> > >> side effects which explain why it works as it does?
**> > >>
**> > >> Regards,
**> > >>
**> > >> Markus
**> > >>
**> > >> I am running R on a Debian testing system with kernel 2.6.10 and
**> > >>
**> > >> > version
**> > >> _
**> > >> platform i386-pc-linux-gnu
**> > >> arch i386
**> > >> os linux-gnu
**> > >> system i386, linux-gnu
**> > >> status
**> > >> major 2
**> > >> minor 0.1
**> > >> year 2004
**> > >> month 11
**> > >> day 15
**> > >> language R
**> > >> --
**> > >> Markus Jantti
**> > >> Abo Akademi University
**> > >> markus.jantti@iki.fi
**> > >> http://www.iki.fi/~mjantti
**> > >>
**> > >
**> > > ______________________________________________
**> > > R-help@stat.math.ethz.ch mailing list
**> > > https://stat.ethz.ch/mailman/listinfo/r-help
**> > > PLEASE do read the posting guide!
**> > http://www.R-project.org/posting-guide.html
**> > >
**> >
**> > --
**> > Brian D.
**> > Ripley, ripley@stats.ox.ac.uk
**> > Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
**> > University of Oxford, Tel: +44 1865 272861 (self)
**> > 1 South Parks Road, +44 1865 272866 (PA)
**> > Oxford OX1 3TG, UK Fax: +44 1865 272595
**> >
**>
**> ______________________________________________
**> R-help@stat.math.ethz.ch mailing list
**> https://stat.ethz.ch/mailman/listinfo/r-help
**> PLEASE do read the posting guide!
**> http://www.R-project.org/posting-guide.html
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https://stat.ethz.ch/mailman/listinfo/r-devel Received on Sat Feb 19 07:40:36 2005

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