confint.moc {moc} | R Documentation |
confint.moc
computes confidence intervals (CI) of specified
function of the parameters based on crude Wald asymptotics. More
precise CI for the original parameters are obtained through profiling
of the likelihood function (that is evaluation of the likelihood over
a wide range of values in the parameter space). When profiling is
requested the deviance over different parameters' values is also
returned.
profiles.postCI
computes data values for which the empirical
probability of observing such subject values, given mixture group,
lies between the confidence bounds (see details).
density.moc
computes the estimated mixture density at data
points along some factor and optionally plot it.
## S3 method for class 'moc': confint(object, parm = list(), level = 0.95, profiling = c("none","simple","complete"), ...) profiles.postCI(object, data = NULL, level = 0.95, interpolate = TRUE) ## S3 method for class 'moc': density(x, var = NULL, along = NULL, plot = c("none","pp-plot","density","pq-plot"), type = "l", ...)
object, x |
A fitted moc object. |
parm |
A list of formulas beginning with ~ or expressions of the
parameters denoted p1,p2,... for which confidence intervals
are requested.
For example, parm = list(~p1,~exp(p2)/(1+exp(p1+p3))) . |
level |
Alpha level in the interval (0,1) for the confidence
bounds [(1-level)/2,(1+level)/2]. In profiles.postCI , you
can also directly specify the bounds like level = c(0.02,0.98) . |
profiling |
A string that specifies the desired type of likelihood profiling.
This can be one of
|
data |
An optional data.frame , matrix or vector
of length nsubject containing the values for which the
confidence limits are requested. The default is to take the original
response profile. |
interpolate |
A logical value indicating whether interpolation of data values must be performed to achieve the probability limits. When "FALSE", the data points with the probabilities nearest to the given bounds are taken (thus using the corresponding step function). |
var |
A vector of integer values specifying the response variables for density evaluation. |
along |
A factor used to split the density estimator. |
plot |
A string that specifies the kind of desired plot. Allowed
values are
|
type |
The type of lines in the plot, see plot for
details. |
... |
Used in density.moc to pass arguments
directly to the plotting function. In confint.moc iterlim
will be passed to update.moc and offscal will change the
profiling parameters search range. |
The methods included here primarily exploit the empirical estimators of the conditional expectation given mixture group for some appropriately chosen function of the data g(), that is
g_k = Sum_i (wt[i] * post[i,k] * g(y[i])) / Sum_i (wt[i] * post[i,k]).
Profiles confidence intervals and density estimates are defined
by choosing g() as the indicator function over appropriate sets.
See print.moc
and residuals.moc
.
confint.moc
returns a list containing a list of arrays
with likelihood deviance for each parameters configuration of the
requested profiling, a function ellip
corresponding to the
asymptotic elliptic distance
ellip(p) = (p - p_max) S^-1 (p - p_max)
where p_max is the maximum likelihood estimator of the
parameters and S its asymptotic covariance
matrix. It also returns univariate, joint conditional and likelihood
rejection confidence intervals for the parameters (when profiling
has been requested).
profiles.postCI
returns a list of array with upper and lower
bounds on data profiles for each mixture group.
density.moc
returns nothing when a plot is requested, otherwise
an array with mixture density estimate and data points is returned.
Mixture models are powerful tools to capture and describe the
variability present in the data. The methods profiles.postCI
and density.moc
are especially intended to this purpose.
The method confint.moc
is quite different in essence since it
is used to describe parameters' uncertainty that depends on sampling
scheme and size, estimation method and goodness-of-fit of the model.
A model with small parameters' uncertainty can poorly describe data
variability while a model with large parameters' uncertainty can be
very good at describing data variability.
Bernard Boulerice <Bernard.Boulerice@sympatico.ca>
moc
, print.moc
,
residuals.moc
, post.moc
,
loglike.moc
, profilesplot