spc.interp
computes the expected frequency spectrum for a
random sample of specified size N, taken from a data set
described by the frequency spectrum object obj
.
1  spc.interp(obj, N, m.max=max(obj$m), allow.extrapolation=FALSE)

obj 
an object of class 
N 
a single nonnegative integer specifying the sample size for which the expected frequency spectrum is calculated 
m.max 
number of spectrum elements listed in the expected
frequency spectrum. By default, as many spectrum elements are
included as the spectrum 
allow.extrapolation 
if 
See the EVm.spc
manpage for more information, especially
concerning binomial extrapolation.
For large frequency spectra, the default value of m.max
may
lead to very long computation times. It is therefore recommended to
specify m.max
explicitly and calculate only as many spectrum
elements as are actually required.
An object of class spc
, representing the expected frequency
spectrum for a random sample of size N
taken from the data set
that is described by obj
.
spc
for more information about frequency spectra and
links to relevant functions
The implementation of spc.interp
is based on the functions
EV.spc
and EVm.spc
. See the respective
manpages for technical details.
vgc.interp
computes expected vocabulary growth curves by
binomial interpolation from a frequency spectrum
sample.spc
takes a single concrete random
subsample from a spectrum and returns the spectrum of the subsample,
unlike spc.interp
, that computes the expected
frequency spectrum for random subsamples of size N
by
binomial interpolation.
1 2 3 4 5 6 7 8 9 10 11 12 13  ## load the Tiger NP expansion spectrum
## (sample size: about 109k tokens)
data(TigerNP.spc)
## interpolated expected frequency subspectrum of 50k tokens
TigerNP.sub.spc < spc.interp(TigerNP.spc,5e+4)
summary(TigerNP.sub.spc)
## previous is slow since it calculates all expected spectrum
## elements; suppose we only need the first 10 expected
## spectrum element frequencies; then we can do:
TigerNP.sub.spc < spc.interp(TigerNP.spc,5e+4,m.max=10) # much faster!
summary(TigerNP.sub.spc)

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
All documentation is copyright its authors; we didn't write any of that.