vcov.cusp: Calculate Variance-Covariance Matrix for a Fitted Cusp Model...

In cusp: Cusp-Catastrophe Model Fitting Using Maximum Likelihood

Calculate Variance-Covariance Matrix for a Fitted Cusp Model Object

Description

Returns an estimate of the variance-covariance matrix of the main parameters of a fitted cusp model object.

Usage

## S3 method for class 'cusp'
vcov(object, ...)
## S3 method for class 'cusp'
confint(object, parm, level = 0.95, ...)

Arguments

object	a fitted cusp model object.
parm	a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.
level	the confidence level required.
...	additional arguments for method functions.

Details

The variance-covariance matrix is estimated by the inverse of the Hessian matrix of the log-likelhood at the maximum likelihood estimate (vcov).

Normal theory confidence intervals are computed for all parameters in the cusp model object using vcov to obtain the standard errors (confint).

Value

The variance-covariance matrix (vcov).

A matrix (or vector) with columns giving lower and upper confidence limits for each parameter. These will be labelled as (1-level)/2 and 1 - (1-level)/2 in

Author(s)

Raoul Grasman

References

Seber, Wild (2005) Nonlinear regression. New York: Wiley

See Also

vcov, cusp

Examples

set.seed(123)
x1 = runif(150)
x2 = runif(150)
z = Vectorize(rcusp)(1, 4*x1-2, 4*x2-1)
data <- data.frame(x1, x2, z)
fit <- cusp(y ~ z, alpha ~ x1+x2, beta ~ x1+x2, data)

vcov(fit)

cusp documentation built on Aug. 29, 2022, 9:07 a.m.