Your example is not actually a regression, and not reproducible. Here is
one that is both:
> fm1 <- lm(weight ~ ns(height, df = 5), data = women)
> predict(fm1, newdata=women[1,], se=TRUE)
Error: variable 'ns(height, df = 5)' was fitted with class
"nmatrix.5" but
class "nmatrix.1" was supplied
In addition: Warning message:
'newdata' had 1 rows but variable(s) found have 5 rows
It is only a problem if you try to predict from a single case.
Now take a look at
> attr(terms(fm1), "predvars")[[3]]
ns(height, knots = c(60.8, 63.6, 66.4, 69.2), Boundary.knots = c(58, 72),
intercept = FALSE)
If you apply that, you will get strange results: it is a bug in ns() when
applied to a length-one variable: a drop=TRUE is missing in
basis <- as.matrix((t(qr.qty(qr.const, t(basis))))[, - (1:2)])
On Fri, 1 Sep 2006, Spencer Jones wrote:
> I am fittling a spline to a variable in a regression model, I am then using
> the predict.glm funtion to make some predictions. When I use bs to fit the
> spline I don't have any problems using the predict.glm function however
when
> I use ns I get the following error:
>
>
> Error in model.frame(formula, rownames, variables, varnames, extras,
> extranames, :
> variable lengths differ (found for 'ns(DY, df = 6)')
> In addition: Warning message:
> 'newdata' had 1 rows but variable(s) found have 6 rows
>
> so for whatever reason this code works
>
>
> model. <- glm.nb(CNT ~ WKDY + bs(DY,df=6) + H_FLAG + NH_FLAG + Trend)
> predict(model,newdata=data[i,1:10],type="response",se=TRUE)
>
> but this code does not work
>
> model. <- glm.nb(CNT ~ WKDY + ns(DY,df=6) + H_FLAG + NH_FLAG + Trend)
> predict(model,newdata=data[i,1:10],type="response",se=TRUE)
>
> the two are identical aside from bs vs ns. I looked at the R help and from
> what I could tell, both functions are based on splines.des and they output
a
> matrix of the same dimension.
> Any feedback would be appreciated.
>
> thanks,
>
> Spencer
>
> [[alternative HTML version deleted]]
>
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> and provide commented, minimal, self-contained, reproducible code.
>
--
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 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595