Description Usage Arguments Value Author(s) Examples
Exponential distribution is characterized by the following probability density function,
f(x;λ) = λ \exp(-λ x)
where the domain is nonnegative real number x \in [0,∞) with rate parameter λ > 0.
1 | Exponential(x, weight = NULL)
|
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
rate parameter λ.
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 | # generate data from exponential distribution with lambda=1
x = stats::rexp(100, rate=1)
# fit unweighted
Exponential(x)
## Not run:
# put random weights to see effect of weights
niter = 500
ndata = 200
# generate data as above and fit unweighted MLE
x = stats::rexp(ndata, rate=1)
xmle = Exponential(x)
# iterate
vec.lambda = rep(0,niter)
for (i in 1:niter){
# random weight
ww = abs(stats::rnorm(ndata))
MLE = Exponential(x, weight=ww)
vec.lambda[i] = MLE$lambda
if ((i%%10) == 0){
print(paste0(" iteration ",i,"/",niter," complete.."))
}
}
# distribution of weighted estimates + standard MLE
opar <- par(no.readonly=TRUE)
hist(vec.lambda, main="rate 'lambda'")
abline(v=xmle$lambda, lwd=3, col="red")
par(opar)
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.