LLPLS: Concentrated Loglikelihood Function for alpha in PLS model
In FGN: Fractional Gaussian Noise and power law decay time series model fitting
Description
Usage
Arguments
Value
Author(s)
See Also
Examples
Description
The concentrated loglikelihood, that is, the loglikelihood function
maximized over the innovation variance parameter, is computed.
Usage
1
LLPLS(alpha, z)
Arguments
alpha
decay parameter
z
time series
Value
the value of the loglikelihood
Author(s)
A. I. McLeod
See Also
Examples
1
2
3
4
5
6
7
#compute loglikelihood for NileFlowCMS with
# H=0.9 and with alpha=2-2*H=0.2
data(NileFlowCMS)
z<-NileFlowCMS
z<-z-mean(z)
LLFGN(0.9, z)
LLPLS(0.2, z)
FGN documentation built on May 30, 2017, 7:19 a.m.
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Description Usage Arguments Value Author(s) See Also Examples
Description
The concentrated loglikelihood, that is, the loglikelihood function maximized over the innovation variance parameter, is computed.
Usage
1 | LLPLS(alpha, z)
|
Arguments
alpha |
decay parameter |
z |
time series |
Value
the value of the loglikelihood
Author(s)
A. I. McLeod
See Also
Examples
1 2 3 4 5 6 7 | #compute loglikelihood for NileFlowCMS with
# H=0.9 and with alpha=2-2*H=0.2
data(NileFlowCMS)
z<-NileFlowCMS
z<-z-mean(z)
LLFGN(0.9, z)
LLPLS(0.2, z)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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