envlpaster (version 0.1-2)

get1Dderiv: get1Dderiv

Description

The derivative of the objective function for the 1D-algorithm.

Usage

get1Dderiv(w,A,B)

Arguments

w

A vector of length of $p$.

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

dF

The value of the derivative of the objective function for the 1D-algorithm evaluated at w, A, and B.

Details

This function evaluates the derivative of the objective function for the 1D-algorithm at w, A, and B. This is needed in order to reliably find the maximum 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
# get1Dderiv(w = beta, A = avar, B = U)## End(Not run)