Description Usage Arguments Details Value See Also Examples
Generates β estimates for MLE using a conditioning approach.
1 | bsm.simple(x, y, z)
|
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. |
The technique used to calculate the estimates is described in section 9.3.3.
A list with the following components:
The least-squares estimate of β.
The (P + F) x L matrix with the ijth element being the standard error of \hat{β}_ij.
The (P + F) 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-F x Q-F matrix \hat{Σ}_z.
The Q x Q residual sum of squares and crossproducts matrix.
bothsidesmodel.mle
and bsm.fit
1 2 3 4 5 6 7 8 9 10 | # Taken from section 9.3.3 to show equivalence to methods.
data(mouths)
x <- cbind(1, mouths[, 5])
y <- mouths[, 1:4]
z <- cbind(1, c(-3, -1, 1, 3), c(-1, 1, 1, -1), c(-1, 3, -3, 1))
yz <- y %*% solve(t(z))
yza <- yz[, 1:2]
xyzb <- cbind(x, yz[, 3:4])
lm(yza ~ xyzb - 1)
bsm.simple(xyzb, yza, diag(2))
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.