bsm.fit | R Documentation |
\beta
estimates for MLE regression with
patterning.Generates \beta
estimates for MLE using a conditioning approach with
patterning support.
bsm.fit(x, y, z, pattern)
x |
An |
y |
The |
z |
A |
pattern |
An optional |
A list with the following components:
The least-squares estimate of \beta
.
The (P+F)\times L
matrix with the ij
th
element being the standard error of \hat{\beta}_ij
.
The (P+F)\times L
matrix with the ij
th
element being the t-statistic based on \hat{\beta}_ij
.
The estimated covariance matrix of the \hat{\beta}_ij
's.
A p
-dimensional vector of the degrees of freedom for the
t
-statistics, where the j
th component contains the
degrees of freedom for the j
th column of \hat{\beta}
.
The (Q - F) \times (Q - F)
matrix \hat{\Sigma}_z
.
The Q \times Q
residual sum of squares and
crossproducts matrix.
bothsidesmodel.mle
and bsm.simple
# NA
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.