r4 {rrpack} | R Documentation |
Robust reduced-rank regression
Description
Perform robust reduced-rank regression.
Usage
r4(
Y,
X,
maxrank = min(dim(Y), dim(X)),
method = c("rowl0", "rowl1", "entrywise"),
Gamma = NULL,
ic.type = c("AIC", "BIC", "PIC"),
modstr = list(),
control = list()
)
Arguments
Y |
a matrix of response (n by q) |
X |
a matrix of covariate (n by p) |
maxrank |
maximum rank for fitting |
method |
outlier detection method, either entrywise or rowwise |
Gamma |
weighting matrix in the loss function |
ic.type |
information criterion, AIC, BIC or PIC |
modstr |
a list of model parameters controlling the model fitting |
control |
a list of parameters for controlling the fitting process |
Details
The model parameters can be controlled through argument modstr
.
The available elements include
nlam: parameter in the augmented Lagrangian function.
adaptive: if
TRUE
, use leverage values for adaptive penalization. The default value isFALSE
.weights: user supplied weights for adaptive penalization.
minlam: maximum proportion of outliers.
maxlam: maximum proportion of good observations.
delid: discarded observation indices for initial estimation.
The model fitting can be controlled through argument control
.
The available elements include
epsilon: convergence tolerance.
maxit: maximum number of iterations.
qr.tol: tolerance for qr decomposition.
tol: tolerance.
Value
a list consisting of
coef.path |
solutuon path of regression coefficients |
s.path |
solutuon path of sparse mean shifts |
s.norm.path |
solutuon path of the norms of sparse mean shifts |
ic.path |
paths of information criteria |
ic.smooth.path |
smoothed paths of information criteria |
lambda.path |
paths of the tuning parameter |
id.solution |
ids of the selected solutions on the path |
ic.best |
lowest values of the information criteria |
rank.best |
rank values of selected solutions |
coef |
estimated regression coefficients |
s |
estimated sparse mean shifts |
rank |
rank estimate |
References
She, Y. and Chen, K. (2017) Robust reduced-rank regression. Biometrika, 104 (3), 633–647.
Examples
## Not run:
library(rrpack)
n <- 100; p <- 500; q <- 50
xrank <- 10; nrank <- 3; rmax <- min(n, p, q, xrank)
nlam <- 100; gamma <- 2
rho_E <- 0.3
rho_X <- 0.5
nlev <- 0
vlev <- 0
vout <- NULL
vlevsd <- NULL
nout <- 0.1 * n
s2n <- 1
voutsd <- 2
simdata <- rrr.sim5(n, p, q, nrank, rx = xrank, s2n = s2n,
rho_X = rho_X, rho_E = rho_E, nout = nout, vout = vout,
voutsd = voutsd,nlev = nlev,vlev=vlev,vlevsd=vlevsd)
Y <- simdata$Y
X <- simdata$X
fit <- r4(Y, X, maxrank = rmax,
method = "rowl0", ic.type= "PIC")
summary(fit)
coef(fit)
which(apply(fit$s,1,function(a)sum(a^2))!=0)
## End(Not run)