logistic: Logistic Distribution Family Function

Description

Estimates the location and scale parameters of the logistic distribution by maximum likelihood estimation.

Usage

logistic1(llocation = "identity", elocation = list(),
          scale.arg = 1, method.init = 1)
logistic2(llocation = "identity", lscale = "loge",
          elocation = list(), escale = list(),
          ilocation = NULL, iscale = NULL, method.init = 1, zero = NULL)

Arguments

llocation, lscale

Parameter link functions applied to the location parameter $l$ and scale parameter $s$. See Links for more choices, and CommonVGAMffArguments

elocation, escale

List. Extra argument for each of the links. See earg in Links for general information.

scale.arg

Known positive scale parameter (called $s$ below).

ilocation, iscale

See CommonVGAMffArguments for more information.

method.init, zero

See CommonVGAMffArguments for more information.

Value

An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm, rrvglm and vgam.

Details

The two-parameter logistic distribution has a density that can be written as

f (y; l, s) = \frac{\exp [- (y - l) / s]}{s {(1 + \exp [- (y - l) / s])}^{2}}

where $s > 0$ is the scale parameter, and $l$ is the location parameter. The response $-\infty

A logistic distribution with scale = 0.65 (see dlogis) resembles dt with df = 7; see logistic1 and studentt.

logistic1 estimates the location parameter only while logistic2 estimates both parameters. By default, $\eta_1 = l$ and $\eta_2 = \log(s)$ for logistic2.

References

Johnson, N. L. and Kotz, S. and Balakrishnan, N. (1994) Continuous Univariate Distributions, 2nd edition, Volume 1, New York: Wiley. Chapter 15.

Evans, M., Hastings, N. and Peacock, B. (2000) Statistical Distributions, New York: Wiley-Interscience, Third edition.

Castillo, E., Hadi, A. S., Balakrishnan, N. Sarabia, J. S. (2005) Extreme Value and Related Models with Applications in Engineering and Science, Hoboken, N.J.: Wiley-Interscience, p.130.

deCani, J. S. and Stine, R. A. (1986) A note on Deriving the Information Matrix for a Logistic Distribution, The American Statistician, 40, 220--222.

Examples

Run this code

# location unknown, scale known
ldat1 = data.frame(x = runif(nn <- 500))
ldat1 = transform(ldat1, y = rlogis(nn, loc = 1+5*x, scale = 4))
fit = vglm(y ~ x, logistic1(scale = 4), ldat1, trace = TRUE, crit = "c")
coef(fit, matrix = TRUE)

# Both location and scale unknown
ldat2 = data.frame(x = runif(nn <- 2000))
ldat2 = transform(ldat2, y = rlogis(nn, loc = 1+5*x, scale = exp(0+1*x)))
fit = vglm(y ~ x, logistic2, ldat2)
coef(fit, matrix = TRUE)
vcov(fit)
summary(fit)

Run the code above in your browser using DataLab

The Learning Leader's Guide to AI Literacy