bsm.fit: Helper function to determine beta estimates for MLE...
In msos: Data Sets and Functions Used in Multivariate Statistics: Old School by John Marden
Description
Usage
Arguments
Value
See Also
Examples
Description
Generates β estimates for MLE using a conditioning approach with
patterning support.
Usage
1
Arguments
x
An N x (P + F) design matrix, where
F is the number of columns conditioned on. This is
equivalent to the multiplication of xyzb.
y
The N x (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) x 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 β are allowed to be nonzero.
Value
A list with the following components:
- Beta
The least-squares estimate of β.
- SE
The (P + F) x L matrix with the ijth
element being the standard error of \hat{β}_ij.
- T
The (P + F) x L matrix with the ijth
element being the t-statistic based on \hat{β}_ij.
- Covbeta
The estimated covariance matrix of the \hat{β}_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{β}.
- Sigmaz
The (Q - F) x (Q - F)
matrix \hat{Σ}_z.
- Cx
The Q x Q residual sum of squares and
crossproducts matrix.
See Also
bothsidesmodel.mle and bsm.simple
Examples
1
# NA
msos documentation built on Oct. 31, 2020, 9:07 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value See Also Examples
Description
Generates β estimates for MLE using a conditioning approach with patterning support.
Usage
1 |
Arguments
x |
An N x (P + F) design matrix, where F is the number of columns conditioned on. This is equivalent to the multiplication of xyzb. |
y |
The N x (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) x 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 β are allowed to be nonzero. |
Value
A list with the following components:
- Beta
The least-squares estimate of β.
- SE
The (P + F) x L matrix with the ijth element being the standard error of \hat{β}_ij.
- T
The (P + F) x L matrix with the ijth element being the t-statistic based on \hat{β}_ij.
- Covbeta
The estimated covariance matrix of the \hat{β}_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{β}.
- Sigmaz
The (Q - F) x (Q - F) matrix \hat{Σ}_z.
- Cx
The Q x Q residual sum of squares and crossproducts matrix.
See Also
bothsidesmodel.mle and bsm.simple
Examples
1 | # NA
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.