# NOT RUN {
nbcanlink("mu", short = FALSE)
mymu <- 1:10 # Test some basic operations:
kmatrix <- cbind(runif(length(mymu)))
eta1 <- nbcanlink(mymu, size = kmatrix)
ans2 <- nbcanlink(eta1, size = kmatrix, inverse = TRUE)
max(abs(ans2 - mymu)) # Should be 0
# }
# NOT RUN {
mymu <- seq(0.5, 10, length = 101)
kmatrix <- matrix(10, length(mymu), 1)
plot(nbcanlink(mymu, size = kmatrix) ~ mymu, las = 1,
type = "l", col = "blue", xlab = expression({mu}))
# }
# NOT RUN {
# Estimate the parameters from some simulated data
ndata <- data.frame(x2 = runif(nn <- 100))
ndata <- transform(ndata, eta1 = -1 - 1 * x2, # eta1 < 0
size1 = exp(1),
size2 = exp(2))
ndata <- transform(ndata,
mu1 = nbcanlink(eta1, size = size1, inverse = TRUE),
mu2 = nbcanlink(eta1, size = size2, inverse = TRUE))
ndata <- transform(ndata, y1 = rnbinom(nn, mu = mu1, size = size1),
y2 = rnbinom(nn, mu = mu2, size = size2))
summary(ndata)
nbcfit <- vglm(cbind(y1, y2) ~ x2,
negbinomial(lmu = "nbcanlink", imethod = 1), # Try this
# negbinomial(lmu = "nbcanlink", imethod = 2), # Try this
data = ndata, trace = TRUE)
coef(nbcfit, matrix = TRUE)
summary(nbcfit)
# }
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