envlpaster (version 0.1-2)

get1Dini: get1Dini

Description

Finds appropriate starting values for the 1D-algorithm.

Usage

get1Dini(A,B)

Arguments

A

A $\sqrt{n}$ estimate of an estimator's asymptotic covariance matrix.

B

A $\sqrt{n}$ estimate of the parameter associated with the space we are enveloping. For our purposes this quantity is either the outer product of the MLE of the mean-value submodel parameter vector with itself or the outer product of the MLE of the canonical submodel parameter vector with itself.

Value

w

An appropriate starting value for the 1D-algorithm.

Details

The 1D-algorithm is sensitive to starting values. Maximizers of the 1D-algorithm objective function are close to eigenvectors of A or (A+B). The vector, w, is the eigenvector of A or (A+B) which produces the largest value of the 1D-algorithm objective function.

References

Cook, R.D. and Zhang, X. (2014). Foundations for Envelope Models and Methods. JASA, In Press.

Cook, R.D. and Zhang, X. (2015). Algorithms for Envelope Estimation. Journal of Computational and Graphical Statistics, Published online. \Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("#1")}10.1080/10618600.2015.1029577http://doi.org/10.1080/10618600.2015.1029577doi:\ifelse{latex}{\out{~}}{ }latex~ 10.1080/10618600.2015.1029577 .

Examples

## Not run: library(envlpaster)
# data(simdata30nodes)
# data <- simdata30nodes.asterdata
# nnode <- length(vars)
# xnew <- as.matrix(simdata30nodes[,c(1:nnode)])
# m1 <- aster(xnew, root, pred, fam, modmat)
# avar <- m1$fisher
# beta <- m1$coef
# U <- beta %o% beta
# get1Dini(A = avar, B = U)## End(Not run)