Description Usage Arguments Value Author(s) Examples
Gompertz distribution is characterized by the following probability density function,
f(x;η, b) = bη \exp(bx) \exp(η) \exp(-η e^{bx})
where the domain is x \in [0,∞) with two parameters η > 0 for shape and b > 0 for scale.
1 |
x |
a length-n vector of nonnegative real numbers. |
weight |
a length-n weight vector. If set as |
a named list containing (weighted) MLE of
shape parameter η.
scale parameter b.
Kisung You
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | # generate data from half normal
x = abs(stats::rnorm(100))
# fit unweighted
Gompertz(x)
## Not run:
# put random weights to see effect of weights
niter = 500
ndata = 200
# generate data as above and fit unweighted MLE
x = abs(stats::rnorm(ndata))
xmle = Gompertz(x)
# iterate
vec.eta = rep(0,niter)
vec.b = rep(0,niter)
for (i in 1:niter){
# random weight
ww = abs(stats::rnorm(ndata))
MLE = Gompertz(x, weight=ww)
vec.eta[i] = MLE$eta
vec.b[i] = MLE$b
if ((i%%10) == 0){
print(paste0(" iteration ",i,"/",niter," complete.."))
}
}
# distribution of weighted estimates + standard MLE
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2))
hist(vec.eta, main="shape 'eta'")
abline(v=xmle$eta, lwd=3, col="red")
hist(vec.b, main="scale 'b'")
abline(v=xmle$b, lwd=3, col="blue")
par(opar)
## End(Not run)
|
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