Access Methods for Observed Frequency Data (zipfR)
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.
N(obj, ...) V(obj, ...) Vm(obj, m, ...)
obj |
an object of class |
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) |
For tfl and vgc objects, the Vm method allows
only a single value m to be specified.
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.
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.
## 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)]Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.