View source: R/bothsidesmodel.mle.R
bothsidesmodel.mle | R Documentation |
This function fits the model using maximum likelihood. It takes an optional
pattern matrix P
as in (6.51), which specifies which \beta _{ij}
's
are zero.
bothsidesmodel.mle(x, y, z = diag(qq), pattern = matrix(1, nrow = p, ncol = l))
x |
An |
y |
The |
z |
A |
pattern |
An optional |
A list with the following components:
The least-squares estimate of \beta
.
The P \times L
matrix with the ij
th element
being the standard error of \hat{\beta}_{ij}
.
The P \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 \times Q
matrix \hat{\Sigma}_z
.
The Q \times Q
residual sum of squares and
crossproducts matrix.
The dimension of the model, counting the nonzero
\beta _{ij}
's and components of \Sigma _z
.
Mallow's C_p
Statistic.
The dimension of the model, counting the nonzero
\beta _{ij}
's and components of \Sigma_z
The corrected AIC criterion from (9.87) and (aic19)
The BIC criterion from (9.56).
bothsidesmodel.chisquare
, bothsidesmodel.df
,
bothsidesmodel.hotelling
, bothsidesmodel.lrt
,
and bothsidesmodel
.
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))
bothsidesmodel.mle(x, y, z, cbind(c(1, 1), 1, 0, 0))
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