[R] (-1 as index) OR (envelope for QQ)
Prof Brian D Ripley
ripley at stats.ox.ac.uk
Thu Feb 24 21:14:24 CET 2000
On Thu, 24 Feb 2000, Dan E. Kelley wrote:
> I'm new to R (and to S) and am wondering about code from pages 72 and
> 83 of MASS (Venables+Ripley, 3rd edition), to draw an envelope on a QQ
> plot. Copying from the book, I've got:
>
> #... code whose gist is "a.fit <- nls(..."
> num.points <- length(resid(a.fit))
> qqnorm(residuals(a.fit)) # illustrate data-model residuals
> qqline(residuals(a.fit))
> samp <- cbind(residuals(a.fit), matrix(rnorm(num.points*19),num.points,19))
> samp <- apply(scale(samp), 2, sort)
> rs <- samp[,1]
> xs <- qqnorm(rs, plot=FALSE)$x
> env <- t(apply(samp[,-1], 1, range)) ###########################3
> xyul <- par("usr")
> smidge <- min(diff(c(xyul[1], xs, xyul[2])))/2
> segments(xs-smidge,env[,1], xs+smidge, env[,1])
> segments(xs-smidge,env[,2], xs+smidge, env[,2])
>
> where I've marked a confusing line with ########. From what I gather
> (from section 2.1, or page 5, of "R complements to MASS" by VR),
> indexing is different in R than in S. It's not clear to me whether
> this R-to-S difference is giving me problems. I'm such a newbie that
> I'm not entirely sure what the 'samp[,-1]' is supposed to be doing.
> (Confession: I'm such a newbie that I've spent much of the day typing
> "?apply" and then "?par" etc, working through the program to try to
> figure it out!)
You can look in the R scripts (in the MASS package in the VR bundle) to see
fully tested R versions of the code. That works, although you left out a
line (starting matplot).
samp[, -1] drops the first column (pp.39-40), so the marked line applies
range to the remaining columns.
That code applies to a variable, not residuals, which may or may not
matter depending on your model complexity.
> In any case, my worry is visible in the QQ plot attached. Why do I
> have get so few "ledges" from the segments() calls? It's not clear to
> me how the scale of the data-model deviations come into this problem.
>
> Q: should I first be standardizing the residuals from the data-model
> comparison? I would have guessed that something in this 'QQ envelope'
> would be doing that.
Yes, the `scale' function is already normalizing for you.
--
Brian D. Ripley, ripley at 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 272860 (secr)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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