aldvmm.cv {aldvmm}  R Documentation 
Numerical Approximation of Covariance Matrix
Description
aldvmm.cv
performs
a numerical approximation of the covariance matrix of parameter estimates.
Usage
aldvmm.cv(ll, par, X, y, dist, psi, ncmp, lcoef, lcpar, lcmp, optim.method)
Arguments
ll 
a function returning the negative loglikelihood of the adjusted
limited dependent variable mixture model as a scalar result
( 
par 
a named numeric vector of parameter values. 
X 
a list of design matrices returned by

y 
a numeric vector of observed outcomes from complete observations in

dist 
an optional character value of the distribution used in the
components. In this release, only the normal distribution is
available, and the default value is set to 
psi 
a numeric vector of minimum and maximum possible utility values
smaller than or equal to 1 (e.g. 
ncmp 
a numeric value of the number of components that are mixed. The
default value is 2. A value of 1 represents a tobit model with a gap
between 1 and the maximum value in 
lcoef 
a character vector of length 2 with labels of objects including
regression coefficients of component distributions (default 
lcpar 
a character vector with the labels of objects including
constant parameters of component distributions (e.g. the standard
deviation of the normal distribution). The length of 
lcmp 
a character value representing a stub (default 
optim.method 
an optional character value of one of the following

Details
aldvmm.cv
uses
hessian
to calculate the hessian matrix of the loglikelihood function supplied to
'll'
at parameter values supplied to 'par'
.
Value
aldvmm.cv
returns a list with the following objects.
hessian 
a numeric matrix with secondorder partial
derivatives of the likelihood function 
cv 
a
numeric matrix with covariances/variances of parameters in 
se 
a numeric vector of standard errors of parameters in

z 
a numeric vector of zvalues of parameters
in 
p 
a numeric vector of pvalues of parameter estimates. 
upper 
a numeric vector of upper 95%
confidence limits of parameter estimates in 
lower 
a numeric vector of lower 95% confidence limits of
parameter estimates in 