Variance Matrix of a Nonlinear Estimator Using the Delta Method
Computes the variance of a nonlinear parameter using the delta method.
deltaMethod(derived.pars, est, Sigma, h=1e-05)
derived.pars |
Vector of derived parameters written in R formula framework (see Examples). |
est |
Vector of parameter estimates |
Sigma |
Covariance matrix of parameters |
h |
Numerical differentiation parameter |
coef |
Vector of nonlinear parameters |
vcov |
Covariance matrix of nonlinear parameters |
se |
Vector of standard errors |
A |
First derivative of nonlinear transformation |
univarTest |
Data frame containing univariate summary of nonlinear parameters |
WaldTest |
Multivariate parameter test for nonlinear parameter |
See car::deltaMethod or
msm::deltamethod.
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# EXAMPLE 1: Nonlinear parameter
#############################################################################
#-- parameter estimate
est <- c( 510.67, 102.57)
names(est) <- c("mu", "sigma")
#-- covariance matrix
Sigma <- matrix( c(5.83, 0.45, 0.45, 3.21 ), nrow=2, ncol=2 )
colnames(Sigma) <- rownames(Sigma) <- names(est)
#-- define derived nonlinear parameters
derived.pars <- list( "d"=~ I( ( mu - 508 ) / sigma ),
"dsig"=~ I( sigma / 100 - 1) )
#*** apply delta method
res <- CDM::deltaMethod( derived.pars, est, Sigma )
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