Description Usage Arguments Value See Also Examples
View source: R/bothsidesmodel.R
This function fits the model using least squares. It takes an optional pattern matrix P as in (6.51), which specifies which β _{ij}'s are zero.
1 |
x |
An N x P design matrix. |
y |
The N x Q matrix of observations. |
z |
A Q x L design matrix |
pattern |
An optional N x P matrix of 0's and 1's indicating which elements of β are allowed to be nonzero. |
A list with the following components:
The least-squares estimate of β.
The P x L matrix with the ijth element being the standard error of \hat{β}_ij.
The P x L matrix with the ijth element being the t-statistic based on \hat{β}_{ij}.
The estimated covariance matrix of the \hat{β}_{ij}'s.
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{β}.
The Q x Q matrix \hat{Σ}_z.
The Q x Q residual sum of squares and crossproducts matrix.
bothsidesmodel.chisquare
, bothsidesmodel.df
,
bothsidesmodel.hotelling
, bothsidesmodel.lrt
,
and bothsidesmodel.mle
.
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