On Thu, 31 May 2007, Søren Højsgaard wrote:
Dear Prof Ripley and the list
Apologies for an unfortunate typing error. What I meant was:
predict(fm2DNase1)
[1] 0.001424337 0.001424337 0.028883648 0.028883648 0.119576734 0.119576734
0.382791057 ...
nd <- as.data.frame(DNase1)
DNase1 is already a data frame.
class(nd)
[1] "data.frame"
predict(fm2DNase1,newdata=nd)
Error in if (sum(wrong) == 1) stop(gettextf("variable '%s' was fitted with class \"%s\" but
class \"%s\" was supplied", :
missing value where TRUE/FALSE needed
I know I don't have to supply newdata to just get the fitted values, but I want
to enter another dataframe later...
It's picky: you need to supply just the named variables needed to fit the
model, here
predict(fm2DNase1, newdata=DNase1[2:3])
(That's not obvious from the documentation, of course.)
I've made it less picky in R-patched, but nls is not written in the same
way as 'standard' R modelling functions like lm, which would use
model.frame to extract the variables from 'newdata'. Doing so is not
entirely straightforward as there is no record of what variables are
needed for the RHS alone. The less picky form is also less safe: it is
too easy to get the wrong variables since the variables used for fitting
are visible in the environment used for prediction.
This needs something of a rethink in the light of the experience of years
of other prediction methods, but until then use with care.
Regards
Søren
________________________________
Fra: Prof Brian Ripley [mailto:[EMAIL PROTECTED]
Sendt: to 31-05-2007 14:28
Til: Søren Højsgaard
Cc: [email protected]
Emne: Re: [R] predict.nls - gives error but only on some nls objects
Why do you think feeding a model fit (fm2DNase1) is suitable 'newdata'?.
From the help page
newdata: A named list or data frame in which to look for variables
with which to predict. If 'newdata' is missing the fitted
values at the original data points are returned.
It is the unsuitable 'newdata' that is causing the error.
On Thu, 31 May 2007, Søren Højsgaard wrote:
Dear list,
I have encountered a problem with predict.nls (Windows XP, R.2.5.0), but I am
not sure if it is a bug...
On the nls man page, an example is:
DNase1 <- subset(DNase, Run == 1)
fm2DNase1 <- nls(density ~ 1/(1 + exp((xmid - log(conc))/scal)),
data = DNase1,
start = list(xmid = 0, scal = 1))
alg = "plinear", trace = TRUE)
Now consider prediction:
predict(fm2DNase1)
[1] 0.001424337 0.001424337 0.028883648 0.028883648 .....
predict(fm2DNase1,newdata=fm2DNase1)
Error in if (sum(wrong) == 1) stop(gettextf("variable '%s' was fitted with class \"%s\" but
class \"%s\" was supplied", :
missing value where TRUE/FALSE needed
What causes the trouble is the call to .checkMFClasses(cl, newdata) in
predict.nls.
Incidently, on the predict.nls page the example works:
fm <- nls(demand ~ SSasympOrig(Time, A, lrc), data = BOD)
predict(fm)
[1] 7.887449 12.524977 15.251673 16.854870 17.797490 18.677580
predict(fm,newdata=BOD)
[1] 7.887449 12.524977 15.251673 16.854870 17.797490 18.677580
attr(,"gradient")
A lrc
[1,] 0.4120369 5.977499
[2,] 0.6542994 7.029098
....
Is there a bug, or am I overlooking something??
Regards
Søren
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--
Brian D. Ripley, [EMAIL PROTECTED]
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
--
Brian D. Ripley, [EMAIL PROTECTED]
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
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