| normal1 {VGAM} | R Documentation |
Maximum likelihood estimation of the two parameters of a univariate normal distribution.
normal1(lmean = "identity", lsd = "loge",
emean = list(), esd = list(), imethod = 1, zero = -2)
lmean, lsd |
Link functions applied to the mean and standard deviation.
See |
emean, esd |
List. Extra argument for the links.
See |
imethod, zero |
See |
This fits a linear model (LM) as the first linear/additive predictor. So, by default, this is just the mean. By default, the log of the standard deviation is the second linear/additive predictor. The Fisher information matrix is diagonal. This VGAM family function can handle multiple responses.
An object of class "vglmff" (see vglmff-class).
The object is used by modelling functions such as vglm,
and vgam.
Yet to do: allow an argument such as sameSD that enables the
standard devations to be the same.
And a parallel argument.
T. W. Yee
Evans, M., Hastings, N. and Peacock, B. (2000) Statistical Distributions, New York: Wiley-Interscience, Third edition.
gaussianff,
posnormal1,
mix2normal1,
Qvar,
tobit,
cennormal1,
fnormal1,
skewnormal1,
dcennormal1,
huber,
studentt,
binormal,
dnorm.
ndata <- data.frame(x2 = rnorm(nn <- 200))
ndata <- transform(ndata, y = rnorm(nn, mean = 1-3*x2, sd = exp(1+0.2*x2)))
fit <- vglm(y ~ x2, normal1(zero = NULL), ndata, trace = TRUE)
coef(fit, matrix = TRUE)
# Generate data from N(mu = theta = 10, sigma = theta) and estimate theta.
theta <- 10
ndata <- data.frame(y = rnorm(100, m = theta, sd = theta))
fit <- vglm(y ~ 1, normal1(lsd = "identity"), ndata,
constraints = list("(Intercept)" = rbind(1, 1)))
coef(fit, matrix = TRUE)