# Predict gamlss one-inflated beta model

How do you obtain predicted probabilities for the one-inflated component (nu model) of a one-inflated beta regression in gamlss?

I have built the following model

zib <- gamlss(prop.abun.max ~ season + time + temp +
last.rain.bom + rain + wind + cloud +
re(random = ~1|site),
sigma.formula = ~1,
nu.formula = ~ season + time + temp +
last.rain.bom + rain + wind + cloud +
re(random = ~1|site),
family = BEINF1,
data = na.omit(subset2))


I obtain predicted probabilities for the beta distribution component of my model (the mu model) using

  head(predict(zib, what = "mu", type = "response"))
 0.7519171 0.7366541 0.7605794 0.6904190 0.7578658 0.7280828


This produces values in the range 0-1 which I assume are predicted probabilities.

However, similar code that references the one-inflated component of my model (the nu model) obtains values that are on the range 0.3-4.1. These values are clearly not predicted probabilities as many values are greater than 1.

  head(predict(zib, what = "nu", type = "response"), n = 10)
 0.6079466 0.9698540 0.7028005 0.6680394 0.6896672 0.6375064 0.6461947 0.6620159 1.2440965 0.7722830


The best post I have found on this is here. This posts asks a similar question for a zero- and one-inflated beta model in gamlss. However, this answer is not applicable to a one-inflated beta model only (i.e without the zero-inflation) and the reference text suggested by the post no longer seems to be available.

Any advice/assistance to obtain the correct predicted probabilities for the one-inflated component (nu model) of a one-inflated beta regression in gamlss would be very much appreciated?

• Are you sure they aren't on the logit scale? Sep 9, 2021 at 20:46
• $\nu$ is parametrized as $\nu = p_1/(1 - p_1)$, the odds of a $1$. Fitting is done on the log-odds (logit) scale. So in order to calculate $p_1$, you have to take $\nu/(1 + \nu)$, as explained in the post you linked to. Sep 9, 2021 at 21:15