Description Usage Arguments Details Value See Also Examples

`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 non-negative 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)
``` |

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