| null.space.dimension {mgcv} | R Documentation |
The thin plate spline penalties give zero penalty to some
functions. The space of these functions is spanned by a set of
polynomial terms. null.space.dimension finds the dimension of this space, M, given
the number of covariates that the smoother is a function of, d,
and the order of the smoothing penalty, m. If m does not
satisfy 2m>d then the smallest possible dimension
for the null space is found given d and the requirement that
the smooth should be visually smooth. null.space.basis.powers()
produces an M by d array, the ith row of which gives the
powers of each of the d covariates featuring in the ith basis
term. null.space.basis.names() gives the equivalent information
as a list of basis labels - the key feature of which is that the same
term will always have the same label, irrespective of the order in
which variables are supplied to the routine. For some models a smooth
may be multiplied by a by variable: in such cases the basis
labels can include this name as well, but note that the constant label
will be returned as if the by variable didn't exist (to meet
requirements of function gam.side.conditions).
null.space.dimension(d,m) null.space.basis.powers(m,d) null.space.basis.labels(names,m,by="NA")
d |
is a positive integer - the number of variables of which the t.p.s. is a function. |
m |
a non-negative integer giving the order of the penalty functional, or signalling that the default order should be used. |
names |
is an array of d variable names |
by |
the name of any by variable, which multiplies all the
terms in the penalty null space basis. |
Thin plate splines are only visually smooth if the order of the wiggliness penalty, m, satisfies 2m > d+1. If 2m<d+1 then this routine finds the smallest m giving visual smoothness for the given d, otherwise the supplied m is used. The null space dimension is given by:
M=(m+d+1)!/(d!(m-d)!
which is the value returned.
null.space.dimension returns an integer (array), the null space dimension
M: this is the only one of these functions that accepts array arguments.
null.space.basis.powers produces a 2-d array,
p, say: each row corresponds to one of the M basis vectors, while each column
corresponds to one covariate. So if x1, x2, x3, etc
are the covariates, the ith basis vector is given by
x1^p[i,1]*x2^p[i,2]*x3^p[i,3]....
null.space.basis.labels() just produces uniques labels for the
basis terms.
This routine operates symbolically, not numerically: your model may still be numerically un-identifiable, and the routine can't tell if two variables with different names are really the same.
Simon N. Wood simon@stats.gla.ac.uk
Wood, S.N. (2003) Thin plate regression splines. J.R.Statist.Soc.B 65(1):95-114
http://www.stats.gla.ac.uk/~simon/
null.space.dimension(2,0)
null.space.basis.powers(2,2)
null.space.basis.labels(c("x","z"),m=2)
null.space.basis.labels(c("z","x"),m=2) # labels are invariant