| 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(), zero=NULL)
lmean |
Link function applied to the mean.
See Links for more choices.
|
lsd |
Parameter link function applied to the standard deviation.
See Links for more choices.
Being a positive quantity, a log link is the default.
|
emean, esd |
List. Extra argument for the links.
See earg in Links for general information.
|
zero |
An integer vector, containing the value 1 or 2. If so, the mean or
standard deviation respectively are modelled as an intercept only.
Usually, setting zero=2 will be used, if used at all.
The default value NULL means both linear/additive predictors
are modelled as functions of the explanatory variables.
|
By default, the mean is the first linear/additive predictor and the log of the standard deviation is the second linear/additive predictor. The Fisher information matrix is diagonal.
An object of class "vglmff" (see vglmff-class).
The object is used by modelling functions such as vglm,
and vgam.
The response should be univariate. Multivariate responses are more
generally handled using gaussianff, however this only handles
the mean and the variance-covariance matrices are assumed known.
T. W. Yee
Evans, M., Hastings, N. and Peacock, B. (2000) Statistical Distributions, New York: Wiley-Interscience, Third edition.
gaussianff,
posnormal1,
tobit,
cnormal1,
dcnormal1,
studentt.
n = 200
x = rnorm(n)
y = rnorm(n, mean=1-3*x, sd=exp(1+0.2*x))
fit = vglm(y ~ x, normal1)
coef(fit, matrix=TRUE)
# Generate a random sample from a N(mu=theta, sigma=theta)
# distribution with theta=10. Then estimate theta.
theta = 10
y = rnorm(100, m=theta, sd=theta)
fit = vglm(y ~ 1, normal1(lsd="identity"),
constraints=list("(Intercept)"=rbind(1,1)))
coef(fit, matrix=TRUE)