zipfR (version 0.6-66)

N-V-Vm: Access Methods for Observed Frequency Data (zipfR)

Description

N, V and Vm are generic methods that can (and should) be used to access observed frequency data for objects of class tfl, spc, vgc and lnre. The precise behaviour of the functions depends on the class of the object, but in general N returns the sample size, V the vocabulary size, and Vm one or more selected elements of the frequency spectrum.

Usage

N(obj, ...)
  V(obj, ...)
  Vm(obj, m, ...)

Arguments

obj

an object of class tfl (type frequency list), spc (frequency spectrum), vgc (vocabulary growth curve) or lnre (LNRE model)

m

positive integer value determining the frequency class \(m\) to be returned (or a vector of such values).

...

additional arguments passed on to the method implementation (see respective manpages for details)

Value

For a frequency spectrum (class spc), N returns the sample size, V returns the vocabulary size, and Vm returns individual spectrum elements.

For a type frequency list (class tfl), N returns the sample size and V returns the vocabulary size corresponding to the list. Vm returns a single spectrum element from the corresponding frequency spectrum, and may only be called with a single value m.

For a vocabulary growth curve (class vgc), N returns the vector of sample sizes and V the vector of vocabulary sizes. Vm may only be called with a single value m and returns the corresponding vector from the vgc object (if present).

For a LNRE model (class lnre) estimated from an observed frequency spectrum, the methods N, V and Vm return information about this frequency spectrum.

Details

For tfl and vgc objects, the Vm method allows only a single value m to be specified.

See Also

For details on the implementations of these methods, see N.tfl, N.spc, N.vgc, etc. When applied to an LNRE model, the methods return information about the observed frequency spectrum from which the model was estimated, so the manpages for N.spc are relevant in this case.

Expected vocabulary size and frequency spectrum for a sample of size \(N\) according to a LNRE model can be computed with the analogous methods EV and EVm. The corresponding variances are obtained with the VV and VVm methods, which can also be applied to expected or interpolated frequency spectra and vocabulary growth curves.

Examples

# NOT RUN {
## load Brown spc and tfl
data(Brown.spc)
data(Brown.tfl)

## you can extract N, V and Vm (for a specific m)
## from either structure
N(Brown.spc)
N(Brown.tfl)

V(Brown.spc)
V(Brown.tfl)

Vm(Brown.spc,1)
Vm(Brown.tfl,1)

## you can extract the same info also from a lnre model estimated
## from these data (NB: these are the observed quantities; for the
## expected values predicted by the model use EV and EVm instead!)
model <- lnre("gigp",Brown.spc)
N(model)
V(model)
Vm(model,1)

## Baayen's P:
Vm(Brown.spc,1)/N(Brown.spc)

## when input is a spectrum (and only then) you can specify a vector
## of m's; e.g., to obtain class sizes of first 5 spectrum elements
## you can write:
Vm(Brown.spc,1:5)

## the Brown vgc
data(Brown.emp.vgc)

## with a vgc as input, N, V and Vm return vectors of the respective
## values for each sample size listed in the vgc
Ns <- N(Brown.emp.vgc)
Vs <- V(Brown.emp.vgc)
V1s <- Vm(Brown.emp.vgc,1)

head(Ns)
head(Vs)
head(V1s)

## since the last sample size in Brown.emp.vgc
## corresponds to the full Brown, the last elements
## of the Ns, Vs and V1s vectors are the same as
## the quantities extracted from the spectrum and
## tfl:
Ns[length(Ns)]
Vs[length(Vs)]
V1s[length(V1s)]

# }

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