spc objects are used to represent
a word frequency spectrum (either an observed spectrum or the expected
spectrum of a LNRE model at a given sample size).
spc constructor function, an object can be initialized
directly from the specified data vectors. It is more common to read
an observed spectrum from a disk file with
compute an expected spectrum with
spc objects should always be treated as read-only.
integer vector of frequency classes m (if omitted,
vector of corresponding class sizes V_m (may be fractional for expected frequency spectrum E[V_m])
optional vector of estimated variances Var[V_m] (for expected frequency spectrum only)
total sample size N and vocabulary size V of
frequency spectrum. While these values are usually determined
variance Var[V] of expected
vocabulary size. If
highest frequency class m listed in incomplete
spc object is a data frame with the following variables:
frequency class m, an integer vector
class size, i.e. number V_m of types in frequency class m (either observed class size from a sample or expected class size E[V_m] based on a LNRE model)
optional: estimated variance V[V_m] of expected class size (only meaningful for expected spectrum derived from LNRE model)
The following attributes are used to store additional information about the frequency spectrum:
if non-zero, the frequency spectrum is
incomplete and lists only frequency classes up to
sample size N and vocabulary size V
of the frequency spectrum. For a complete frequency spectrum,
these values could easily be determined from
Vm, but they are essential for an incomplete spectrum.
variance of expected vocabulary size; only present
TRUE. Note that
have the value
NA is the user failed to specify it.
TRUE, frequency spectrum lists
expected class sizes E[V_m] (rather than observed
sizes V_m). Note that the
VVm variable is only
allowed for an expected frequency spectrum.
indicates whether or not the
variable is present
An object of class
spc representing the specified frequency
spectrum. This object should be treated as read-only (although such
behaviour cannot be enforced in R).
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## load Brown imaginative prose spectrum and inspect it data(BrownImag.spc) summary(BrownImag.spc) print(BrownImag.spc) plot(BrownImag.spc) N(BrownImag.spc) V(BrownImag.spc) Vm(BrownImag.spc,1) Vm(BrownImag.spc,1:5) ## compute ZM model, and generate PARTIAL expected spectrum ## with variances for a sample of 10 million tokens zm <- lnre("zm",BrownImag.spc) zm.spc <- lnre.spc(zm,1e+7,variances=TRUE) ## inspect extrapolated spectrum summary(zm.spc) print(zm.spc) plot(zm.spc,log="x") N(zm.spc) V(zm.spc) VV(zm.spc) Vm(zm.spc,1) VVm(zm.spc,1) ## generate an artificial Zipfian-looking spectrum ## and take a look at it zipf.spc <- spc(round(1000/(1:1000)^2)) summary(zipf.spc) plot(zipf.spc) ## see manpages of lnre, and the various *.spc mapages ## for more examples of spc usage
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