Description Usage Arguments Value References Examples
The following code obtains the mle for the location parameter theta, assuming that the sample is drawn from a logistic distribution with mean (or median) theta and scale parameter 1. The pdf is given in the expression (6.1.8), page 357. The algorithm is a Newton-type procedure discussed in Examples 6.1.2 and 6.2.7. To use it to estimate location for a given sample, standardize the sample first; for example, divide the sample items by the sample standard deviation or the interquartile range.
1 | mlelogistic(x, eps = 1e-04)
|
x |
A numeric vector. |
eps |
This quantity validates that the initial guess is a consistent estimator of theta based on the inputted precision. |
theta1
is the asymptotically efficient estimate of theta. check
is the difference between the initial guess of theta (i.e. theta0
) and the
calculated estimate (i.e. theta1
). realnumstps
is the number of
iterations which was involved in obtaining theta1
.
Hogg, R., McKean, J., Craig, A. (2018) Introduction to Mathematical Statistics, 8th Ed. Boston: Pearson.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | # The following function generates a sample of size n from the logistic
# distribution with location theta and scale 1.
rlogisticd <- function(n, theta) {
u <- runif(n)
rlogisticd <- log(u/(1 - u)) + theta
return(rlogisticd)
}
# The following code generates a sample and fits it using mlelogistic
n <- 50
theta <- 10
x <- rlogisticd(n, theta)
mlelogistic(x)
|
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