Expected Vocabulary Growth Curves of LNRE Model (zipfR)
lnre.vgc computes expected vocabulary growth curves
E[V(N)] according to a LNRE model, returning an object of class
vgc. Data points are returned for the specified values of
N, optionally including estimated variances and/or growth curves
for the spectrum elements E[V_m(N)].
lnre.vgc(model, N, m.max=0, variances=FALSE)
model |
an object belonging to a subclass of |
N |
an increasing sequence of non-negative integers, specifying the sample sizes N for which vocabulary growth data should be calculated |
m.max |
if specified, include vocabulary growth curves
E[V_m(N)] for spectrum elements up to |
variances |
if |
~~ TODO, if any ~~
An object of class vgc, representing the expected vocabulary
growth curve E[V(N)] of the LNRE model lnre, with data
points at the sample sizes N.
If m.max is specified, expected growth curves E[V_m(N)]
for spectrum elements (hapax legomena, dis legomena,
etc.) up to m.max are also computed.
If variances=TRUE, the vgc object includes variance data
for all growth curves.
## load Dickens dataset and estimate lnre models
data(Dickens.spc)
zm <- lnre("zm",Dickens.spc)
fzm <- lnre("fzm",Dickens.spc,exact=FALSE)
gigp <- lnre("gigp",Dickens.spc)
## compute expected V and V_1 growth up to 100 million tokens
## in 100 steps of 1 million tokens
zm.vgc <- lnre.vgc(zm,(1:100)*1e6, m.max=1)
fzm.vgc <- lnre.vgc(fzm,(1:100)*1e6, m.max=1)
gigp.vgc <- lnre.vgc(gigp,(1:100)*1e6, m.max=1)
## compare
plot(zm.vgc,fzm.vgc,gigp.vgc,add.m=1,legend=c("ZM","fZM","GIGP"))
## load Italian ultra- prefix data
data(ItaUltra.spc)
## compute zm model
zm <- lnre("zm",ItaUltra.spc)
## compute vgc up to about twice the sample size
## with variance of V
zm.vgc <- lnre.vgc(zm,(1:100)*70, variances=TRUE)
## plot with confidence intervals derived from variance in
## vgc (with larger datasets, ci will typically be almost
## invisible)
plot(zm.vgc)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.