bsm.fit {msos}

Helper function to determine \beta estimates for MLE regression with patterning.

Description

Generates \beta estimates for MLE using a conditioning approach with patterning support.

Usage

bsm.fit(x, y, z, pattern)

Arguments

x

An N \times (P + F) design matrix, where F is the number of columns conditioned on. This is equivalent to the multiplication of xyzb.

y

The N \times (Q - F) matrix of observations, where F is the number of columns conditioned on. This is equivalent to the multiplication of Yz_a.

z

A (Q - F) \times L design matrix, where F is the number of columns conditioned on.

pattern

An optional N-F x F matrix of 0's and 1's indicating which elements of \beta are allowed to be nonzero.

Value

A list with the following components:

Beta

The least-squares estimate of \beta.

SE

The (P+F)\times L matrix with the ijth element being the standard error of \hat{\beta}_ij.

T

The (P+F)\times L matrix with the ijth element being the t-statistic based on \hat{\beta}_ij.

Covbeta

The estimated covariance matrix of the \hat{\beta}_ij's.

df

A p-dimensional vector of the degrees of freedom for the t-statistics, where the jth component contains the degrees of freedom for the jth column of \hat{\beta}.

Sigmaz

The (Q - F) \times (Q - F) matrix \hat{\Sigma}_z.

Cx

The Q \times Q residual sum of squares and crossproducts matrix.

See Also

bothsidesmodel.mle and bsm.simple

Examples

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