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Estimation of the shape parameters of the two-parameter beta distribution plus the probabilities of a 0 and/or a 1.
zoabetaR(lshape1 = "loge", lshape2 = "loge", lpobs0 = "logit",
lpobs1 = "logit", ishape1 = NULL, ishape2 = NULL, trim = 0.05,
type.fitted = c("mean", "pobs0", "pobs1", "beta.mean"),
parallel.shape = FALSE, parallel.pobs = FALSE, zero = NULL)
Details at CommonVGAMffArguments
.
See Links
for more choices.
Details at CommonVGAMffArguments
.
Same as betaR
.
See CommonVGAMffArguments
for more information.
The choice "beta.mean"
mean to return the mean of
the beta distribution; the 0s and 1s are ignored.
See CommonVGAMffArguments
for more information.
Similar to betaR
.
The standard 2-parameter beta distribution has a support on (0,1),
however, many datasets have 0 and/or 1 values too.
This family function handles 0s and 1s (at least one of
them must be present) in
the data set by modelling the probability of a 0 by a
logistic regression (default link is the logit), and similarly
for the probability of a 1. The remaining proportion,
1-pobs0-pobs1
,
of the data comes from a standard beta distribution.
This family function therefore extends betaR
.
One has
# NOT RUN {
nn <- 1000; set.seed(1)
bdata <- data.frame(x2 = runif(nn))
bdata <- transform(bdata,
pobs0 = logit(-2 + x2, inverse = TRUE),
pobs1 = logit(-2 + x2, inverse = TRUE))
bdata <- transform(bdata,
y1 = rzoabeta(nn, shape1 = exp(1 + x2), shape2 = exp(2 - x2),
pobs0 = pobs0, pobs1 = pobs1))
summary(bdata)
fit1 <- vglm(y1 ~ x2, zoabetaR(parallel.pobs = TRUE),
data = bdata, trace = TRUE)
coef(fit1, matrix = TRUE)
summary(fit1)
# }
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