Nothing
vgc <- function (N, V, Vm=NULL, VV=NULL, VVm=NULL, expected=FALSE, check=TRUE)
{
if (length(N) != length(V)) stop("'N' and 'V' must have the same length")
if (any(N < 0)) stop("sample sizes 'N' must be non-negative integers")
if (check) {
if (any(diff(N) < 0)) stop("sample sizes 'N' must be increasing")
if (any(V < 0)) stop("vocabulary sizes 'V' must be non-negative")
if (any(diff(V) < 0)) stop("inconsistent decrease in vocabulary size 'V'")
}
N <- as.double(N) # make sure to avoid integer overflows
V <- as.double(V)
if (!missing(Vm)) {
if (!is.list(Vm)) Vm <- as.list(Vm) # allow V1 to be specified as plain vector
for (v.m in Vm) {
if (!(is.numeric(v.m))) stop("elements of 'Vm' must be numeric vectors")
if (length(v.m) != length(N)) stop("vectors in 'Vm' must have the same length as 'N' and 'V'")
if (check) {
if (any(v.m < 0)) stop("spectrum elements in 'Vm' must be non-negative")
}
}
Vm <- lapply(Vm, as.double) # make sure to avoid integer overflows
}
m.max <- length(Vm) # m.max = 0 if Vm is not specified
## -- this limitation seems arbitrary, so it has been disabled
## if (m.max > 9) stop("at most 9 spectrum elements allowed in 'Vm'")
variances <- !missing(VV)
if (variances && !is.list(VVm)) VVm <- list(VVm) # same as above
m.max.var <- length(VVm) # spectrum elements for which variances are specified
if (variances && (m.max.var != m.max))
stop("variances 'VVm' must be specified for the same spectrum elements as in 'Vm'")
if (!variances && m.max.var > 0)
stop("variance 'VV' missing (but variances 'VVm' are specified)")
if (variances) {
for (vv.m in VVm) {
if (!is.numeric(vv.m)) stop("elements of 'VVm' must be numeric vectors")
if (length(vv.m) != length(N)) stop("vectors in 'VVm' must have the same length as 'N' and 'V'")
if (check) {
if (any(vv.m < 0)) stop("variances in 'VVm' must be non-negative")
}
}
VVm <- lapply(VVm, as.double)
}
vgc <- data.frame(N=N, V=V)
if (variances) vgc$VV <- VV
if (m.max > 0) {
for (i in 1:m.max) {
vgc[[ paste("V", i, sep="") ]] <- Vm[[i]]
if (variances) vgc[[ paste("VV", i, sep="") ]] <- VVm[[i]]
}
}
attr(vgc, "m.max") <- m.max
attr(vgc, "expected") <- expected || variances
attr(vgc, "hasVariances") <- variances
class(vgc) <- c("vgc", class(vgc))
vgc
}
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