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
an object of class
a single non-negative integer specifying the sample size for which the expected frequency spectrum is calculated
number of spectrum elements listed in the expected
frequency spectrum. By default, as many spectrum elements are
included as the spectrum
EVm.spc manpage for more information, especially
concerning binomial extrapolation.
For large frequency spectra, the default value of
lead to very long computation times. It is therefore recommended to
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
spc for more information about frequency spectra and
links to relevant functions
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,
spc.interp, that computes the expected
frequency spectrum for random subsamples of size
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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.