expexpff1(lrate = "loge", irate = NULL, ishape = 1)
Links
for more choices.ishape
."vglmff"
(see vglmff-class
).
The object is used by modelling functions such as vglm
and vgam
.summary
of the model may be wrong.expexpff
for details about the exponentiated
exponential distribution. This family function uses a different
algorithm for fitting the model. Given $\lambda$,
the MLE of $\alpha$ can easily be solved in terms of
$\lambda$. This family function maximizes a profile
(concentrated) likelihood with respect to $\lambda$.
Newton-Raphson is used, which compares with Fisher scoring with
expexpff
.expexpff
,
CommonVGAMffArguments
.# Ball bearings data (number of million revolutions before failure)
edata <- data.frame(bbearings = c(17.88, 28.92, 33.00, 41.52, 42.12, 45.60,
48.80, 51.84, 51.96, 54.12, 55.56, 67.80, 68.64, 68.64,
68.88, 84.12, 93.12, 98.64, 105.12, 105.84, 127.92,
128.04, 173.40))
fit <- vglm(bbearings ~ 1, expexpff1(ishape = 4), trace = TRUE,
maxit = 250, checkwz = FALSE, data = edata)
coef(fit, matrix = TRUE)
Coef(fit) # Authors get c(0.0314, 5.2589) with log-lik -112.9763
logLik(fit)
fit@misc$shape # Estimate of shape
# Failure times of the airconditioning system of an airplane
eedata <- data.frame(acplane = c(23, 261, 87, 7, 120, 14, 62, 47,
225, 71, 246, 21, 42, 20, 5, 12, 120, 11, 3, 14,
71, 11, 14, 11, 16, 90, 1, 16, 52, 95))
fit <- vglm(acplane ~ 1, expexpff1(ishape = 0.8), trace = TRUE,
maxit = 50, checkwz = FALSE, data = eedata)
coef(fit, matrix = TRUE)
Coef(fit) # Authors get c(0.0145, 0.8130) with log-lik -152.264
logLik(fit)
fit@misc$shape # Estimate of shape
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