update.RRRR | R Documentation |
update.RRRR
will update online robust reduced-rank regression model with class RRRR
(ORRRR
) using newly added data
to achieve online estimation.
Estimation methods:
Stochastic Majorisation-Minimisation
Sample Average Approximation
## S3 method for class 'RRRR' update( object, newy, newx, newz = NULL, addon = object$spec$addon, method = object$method, SAAmethod = object$SAAmethod, ..., ProgressBar = requireNamespace("lazybar") )
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 |
The formulation of the reduced-rank regression is as follow:
y = μ +AB' x + D z+innov,
where for each realization y is a vector of dimension P for the P response variables, x is a vector of dimension Q for the Q explanatory variables that will be projected to reduce the rank, z is a vector of dimension R for the R explanatory variables that will not be projected, μ is the constant vector of dimension P, innov is the innovation vector of dimension P, D is a coefficient matrix for z with dimension P*R, A is the so called exposure matrix with dimension P*r, and B is the so called factor matrix with dimension Q*r. The matrix resulted from AB' will be a reduced rank coefficient matrix with rank of r. The function estimates parameters μ, A, B, D, and Sigma, the covariance matrix of the innovation's distribution.
See ?ORRRR
for details about the estimation methods.
A list of the estimated parameters of class ORRRR
.
The estimation method being used
If SAA is the major estimation method, what is the sub solver in each iteration.
The input specifications. N is the sample size.
The path of all the parameters during optimization and the path of the objective value.
The estimated constant vector. Can be NULL
.
The estimated exposure matrix.
The estimated factor matrix.
The estimated coefficient matrix of z
.
The estimated covariance matrix of the innovation distribution.
The final objective value.
The data used in estimation.
Yangzhuoran Yang
ORRRR
, RRRR
, RRR
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
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