What is the recommended way of accessing distribution parameters (mu,
sigma, ...) from the model fit? The indention is to use those values in
d/p/q/r calls. I came out with a wrapper
gamlss_fit <- function(x, family) {
fit <- gamlssML(x, family = family)
mu <- fitted(fit, "mu")[1]
sigma <- fitted(fit, "sigma")[1]
nu <- ifelse("nu" %in% fit$parameters, fitted(fit, "nu")[1], NA)
tau <- ifelse("tau" %in% fit$parameters, fitted(fit, "tau")[1], NA)
list(mu = mu, sigma = sigma, nu = nu, tau = tau)
}
Alternatively, I could write functions to emulate logspline calls to
density estimation.
I suspect I must be missing something obvious. I am relatively new to R.
George
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