msir.bic {msir} | R Documentation |
BIC-type criterion for dimensionality
Description
BIC-type criterion for selecting the dimensionality of a dimension reduction subspace.
Usage
msir.bic(object, type = 1, plot = FALSE)
bicDimRed(M, x, nslices, type = 1, tol = sqrt(.Machine$double.eps))
Arguments
object |
a |
plot |
if |
M |
the kernel matrix. See details below. |
x |
the predictors data matrix. See details below. |
type |
See details below. |
nslices |
the number of slices. See details below. |
tol |
a tolerance value |
Details
This BIC-type criterion for the determination of the structural dimension selects d
as the maximizer of
G(d) = l(d) - Penalty(p,d,n)
where l(d)
is the log-likelihood for dimensions up to d
, p
is the number of predictors, and n
is the sample size.
The term Penalty(p,d,n)
is the type of penalty to be used:
-
type = 1
:Penalty(p,d,n) = -(p-d) \log(n)
-
type = 2
:Penalty(p,d,n) = 0.5 C d (2p-d+1)
, whereC = (0.5 \log(n) + 0.1 n^(1/3))/2 nslices/n
-
type = 3
:Penalty(p,d,n) = 0.5 C d (2p-d+1)
, whereC = \log(n) nslices/n
-
type = 4
Penalty(p,d,n) = 1/2 d \log(n)
Value
Returns a list with components:
evalues |
eigenvalues |
l |
log-likelihood |
crit |
BIC-type criterion |
d |
selected dimensionality |
The msir.bic
also assign the above information to the corresponding 'msir'
object.
Author(s)
Luca Scrucca luca.scrucca@unipg.it
References
Zhu, Miao and Peng (2006) "Sliced Inverse Regression for CDR Space Estimation", JASA.
Zhu, Zhu (2007) "On kernel method for SAVE", Journal of Multivariate Analysis.
See Also
Examples
# 1-dimensional symmetric response curve
n <- 200
p <- 5
b <- as.matrix(c(1,-1,rep(0,p-2)))
x <- matrix(rnorm(n*p), nrow = n, ncol = p)
y <- (0.5 * x%*%b)^2 + 0.1*rnorm(n)
MSIR <- msir(x, y)
msir.bic(MSIR, plot = TRUE)
summary(MSIR)
msir.bic(MSIR, type = 3, plot = TRUE)
summary(MSIR)