cnormal1 {VGAM} | R Documentation |
Maximum likelihood estimation for the normal distribution with left and right censoring.
cnormal1(lmu="identity", lsd="loge", imethod=1, zero=2)
lmu, lsd |
Parameter link functions applied to the mean and
standard deviation parameters.
See Links for more choices.
The standard deviation is a positive quantity, therefore a log link
is the default.
|
imethod |
Initialization method. Either 1 or 2, this specifies
two methods for obtaining initial values for the parameters.
|
zero |
An integer vector, containing the value 1 or 2. If so,
the mean or standard deviation respectively are modelled
as an intercept only.
Setting zero=NULL means both linear/additive predictors
are modelled as functions of the explanatory variables.
|
This function is like normal1
but handles observations
that are left-censored (so that the true value would be less than
the observed value) else right-censored (so that the true value would be
greater than the observed value). To indicate which type of censoring,
input extra = list(leftcensored = vec1, rightcensored = vec2)
where vec1
and vec2
are logical vectors the same length
as the response.
If the two components of this list are missing then
the logical values are taken to be FALSE
.
The fitted object has these two components stored in the extra
slot.
An object of class "vglmff"
(see vglmff-class
).
The object is used by modelling functions such as vglm
,
and vgam
.
This function was adapted from tobit
.
The Tobit model is a special case of this VGAM
family function because the observations have a common
lower censoring point and upper censoring point.
If there are no censored observation then normal1
is recommended instead.
T. W. Yee
n = 1000 x = runif(n) ystar = rnorm(n, mean=100 + 15 * x, sd=exp(3)) # True values ## Not run: hist(ystar) L = runif(n, 80, 90) # Lower censoring points U = runif(n, 130, 140) # Upper censoring points y = pmax(L, ystar) # Left censored y = pmin(U, y) # Right censored ## Not run: hist(y) extra = list(leftcensored = ystar < L, rightcensored = ystar > U) fit = vglm(y ~ x, cnormal1(zero=2), trace=TRUE, extra=extra) coef(fit, matrix=TRUE) Coef(fit) names(fit@extra)