RMark (version 2.1.1)

var.components: Variance components estimation

Description

Computes estimated effects, standard errors and process variance for a set of estimates

Usage

var.components(theta, design, vcv, LAPACK = TRUE)

Arguments

theta

vector of parameter estimates

design

design matrix for combining parameter estimates

vcv

estimated variance-covariance matrix for parameters

LAPACK

argument passed to call to qr for qr decomposition and inversion

Value

• A list with the following elements
• sigma
  process variance estimate
• beta
  dataframe with estimates and standard errors of betas for design
• vcv.beta
  variance-covariance matrix for beta

Details

Computes estimated effects, standard errors and process variance for a set of estimates using the method of moments estimator described by Burnham and White (2002). The design matrix specifies the manner in which the estimates (theta) are combined. The number of rows of the design matrix must match the length of theta. To get a mean estimate use a column matrix of 1's (e.g., design=matrix(1,ncol=1,nrow=length(theta)). The function returns a list with the estimates of the coefficients for the design matrix (beta) with one value per column in the design matrix and the variance-covariance matrix (vcv.beta) for the beta estimates. The process variance is returned as sigma.

References

BURNHAM, K. P. and G. C. WHITE. 2002. Evaluation of some random effects methodology applicable to bird ringing data. Journal of Applied Statistics 29: 245-264.

Examples

data(dipper)
md=mark(dipper,model.parameters=list(Phi=list(formula=~time)))
zz=get.real(md,"Phi",vcv=TRUE)
z=zz$estimates$estimate[1:6]
vcv=zz$vcv.real
var.components(z,design=matrix(rep(1,length(z)),ncol=1),vcv)