logLik.bsrr {bestridge} | R Documentation |
Extract the log-likelihood from a "bsrr.one" object.
Description
This function returns the log-likelihood for the fitted models.
Usage
## S3 method for class 'bsrr'
logLik(object, best.model = TRUE, ...)
Arguments
object |
A " |
best.model |
Whether only return the log-likelihood of the best model. Default is |
... |
additional arguments |
Details
The log-likelihood for the best model chosen by a certain information criterion
or cross-validation corresponding to the call in bsrr
or the best models
with model size and \lambda
in the original s.list
and lambda.list
(or the in the iteration path) can be returned.
For "lm" fits it is assumed that the scale has been estimated
(by maximum likelihood or REML),
and all the constants in the log-likelihood are included.
Value
A matrix or vector containing the log-likelihood for each model is returned.
For bsrr
objects fitted by sequantial
method, values in each row in the
returned matrix corresponding to the model size in s.list
, and each column the shrinkage parameters
in lambda.list
.
For bsrr
objects fitted by gsection
, pgsection
and psequential
, the returned vector
contains log-likelihood for fitted models in each iteration. The coefficients of those model can be extracted
from beta.all
and coef0.all
in the bsrr
object.
Author(s)
Liyuan Hu, Kangkang Jiang, Yanhang Zhang, Jin Zhu, Canhong Wen and Xueqin Wang.
See Also
Examples
# Generate simulated data
n <- 200
p <- 20
k <- 5
rho <- 0.4
SNR <- 10
cortype <- 1
seed <- 10
Tbeta <- rep(0, p)
Tbeta[1:k*floor(p/k):floor(p/k)] <- rep(1, k)
Data <- gen.data(n, p, k, rho, family = "gaussian", cortype = cortype, snr = SNR, seed = seed)
lm.bsrr <- bsrr(Data$x, Data$y, method = "sequential")
logLik(lm.bsrr, best.model = FALSE)