update.RRRR {RRRR} | R Documentation |
Update the RRRR/ORRRR type model with addition data
Description
update.RRRR
will update online robust reduced-rank regression model with class RRRR
(ORRRR
) using newly added data
to achieve online estimation.
Estimation methods:
- SMM
Stochastic Majorisation-Minimisation
- SAA
Sample Average Approximation
Usage
## S3 method for class 'RRRR'
update(
object,
newy,
newx,
newz = NULL,
addon = object$spec$addon,
method = object$method,
SAAmethod = object$SAAmethod,
...,
ProgressBar = requireNamespace("lazybar")
)
Arguments
object |
A model with class |
newy |
Matrix of dimension N*P, the new data y. The matrix for the response variables. See |
newx |
Matrix of dimension N*Q, the new data x. The matrix for the explanatory variables to be projected. See |
newz |
Matrix of dimension N*R, the new data z. The matrix for the explanatory variables not to be projected. See |
addon |
Integer. The number of data points to be added in the algorithm in each iteration after the first. |
method |
Character. The estimation method. Either "SMM" or "SAA". See |
SAAmethod |
Character. The sub solver used in each iteration when the |
... |
Additional arguments to function
|
ProgressBar |
Logical. Indicating if a progress bar is shown during the estimation process.
The progress bar requires package |
Details
The formulation of the reduced-rank regression is as follow:
where for each realization is a vector of dimension
for the
response variables,
is a vector of dimension
for the
explanatory variables that will be projected to
reduce the rank,
is a vector of dimension
for the
explanatory variables
that will not be projected,
is the constant vector of dimension
,
is the innovation vector of dimension
,
is a coefficient matrix for
with dimension
,
is the so called exposure matrix with dimension
, and
is the so called factor matrix with dimension
.
The matrix resulted from
will be a reduced rank coefficient matrix with rank of
.
The function estimates parameters
,
,
,
, and
, the covariance matrix of
the innovation's distribution.
See ?ORRRR
for details about the estimation methods.
Value
A list of the estimated parameters of class ORRRR
.
- method
The estimation method being used
- SAAmethod
If SAA is the major estimation method, what is the sub solver in each iteration.
- spec
The input specifications.
is the sample size.
- history
The path of all the parameters during optimization and the path of the objective value.
- mu
The estimated constant vector. Can be
NULL
.- A
The estimated exposure matrix.
- B
The estimated factor matrix.
- D
The estimated coefficient matrix of
z
.- Sigma
The estimated covariance matrix of the innovation distribution.
- obj
The final objective value.
- data
The data used in estimation.
Author(s)
Yangzhuoran Yang
See Also
ORRRR
, RRRR
, RRR
Examples
set.seed(2222)
data <- RRR_sim()
newdata <- RRR_sim(A = data$spec$A,
B = data$spec$B,
D = data$spec$D)
res <- ORRRR(y=data$y, x=data$x, z = data$z)
res <- update(res, newy=newdata$y, newx=newdata$x, newz=newdata$z)
res