Lognormal Distribution
Maximum likelihood estimation of the (univariate) lognormal distribution.
lognormal(lmeanlog = "identitylink", lsdlog = "loglink", zero = "sdlog")
lmeanlog, lsdlog |
Parameter link functions applied to the mean and (positive)
sigma (standard deviation) parameter.
Both of these are on the log scale.
See |
zero |
Specifies which
linear/additive predictor is modelled as intercept-only.
For |
A random variable Y has a 2-parameter lognormal distribution if log(Y) is distributed N(mu, sigma^2). The expected value of Y, which is
E(Y) = exp(mu + 0.5 sigma^2)
and not mu, make up the fitted values. The variance of Y is
Var(Y) = [exp(sigma^2) -1] * exp(2 mu + sigma^2).
An object of class "vglmff" (see vglmff-class).
The object is used by modelling functions such as vglm,
and vgam.
T. W. Yee
Kleiber, C. and Kotz, S. (2003). Statistical Size Distributions in Economics and Actuarial Sciences, Hoboken, NJ, USA: Wiley-Interscience.
ldata2 <- data.frame(x2 = runif(nn <- 1000))
ldata2 <- transform(ldata2, y1 = rlnorm(nn, mean = 1 + 2 * x2, sd = exp(-1)),
y2 = rlnorm(nn, mean = 1, sd = exp(-1 + x2)))
fit1 <- vglm(y1 ~ x2, lognormal(zero = 2), data = ldata2, trace = TRUE)
fit2 <- vglm(y2 ~ x2, lognormal(zero = 1), data = ldata2, trace = TRUE)
coef(fit1, matrix = TRUE)
coef(fit2, matrix = TRUE)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.