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)