A PSD S3 object representing initial guess of the PSD parameters
r
Input log-return series
hist
Input histogram
trace
TRUE/FALSE: turn trace on/off
iter
Maximum number of iterations
plotqq
TRUE/FALSE: Plot intermediate charts oor not
weights
Specify the weights of each component in the nonlinear fit, defaults are 1.
merge_tails
Specify the numbers of data points to merge in both tails when processing histogram
Value
dist
A PSD S3 object representing best nonlinear fit
psdout
The output of optmix/psg function. This is for debugging purpose only.
See Also
LIHNPSD_standardfit_fn
Examples
# Load the daily log-return data of DJIA
data(szd_logr)
# Prepare the input data set
merge_tails <- c(1,3)
dt <- LIHNPSD_prepare_data(szd_logr, breaks=68, merge_tails=merge_tails)
# Prepare the input PSD
dist <- list( sigma= 0.0036, alpha= 0.9, gamma= 0.0, beta= -0.014 )
class(dist) <- "LIHNPSD"
dist <- rawmean(dist)
dist$location <- 0.00014
# Invoke the nonlinear fit (This will take some time!)
#fit <- standardfit(dist, dt$logr, dt$h, trace=1, iter=10,
# plotqq=1, weights=list(m3=5,m4=1,qq_df=4), merge_tails=merge_tails )
# The final PSD
#dist <- fit$dist
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(LIHNPSD)
Loading required package: sn
Loading required package: stats4
Attaching package: 'sn'
The following object is masked from 'package:stats':
sd
Loading required package: moments
Loading required package: BB
Loading required package: Bolstad2
Loading required package: optimx
Loading required package: Rmpfr
Loading required package: gmp
Attaching package: 'gmp'
The following objects are masked from 'package:base':
%*%, apply, crossprod, matrix, tcrossprod
C code of R package 'Rmpfr': GMP using 64 bits per limb
Attaching package: 'Rmpfr'
The following object is masked from 'package:sn':
zeta
The following objects are masked from 'package:stats':
dbinom, dnorm, dpois, pnorm
The following objects are masked from 'package:base':
cbind, pmax, pmin, rbind
Attaching package: 'LIHNPSD'
The following object is masked from 'package:stats':
density
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/LIHNPSD/standardfit.Rd_%03d_medium.png", width=480, height=480)
> ### Name: standardfit
> ### Title: Standard utility to perform nonlinear PSD fit
> ### Aliases: standardfit
>
> ### ** Examples
>
> # Load the daily log-return data of DJIA
> data(szd_logr)
>
> # Prepare the input data set
> merge_tails <- c(1,3)
> dt <- LIHNPSD_prepare_data(szd_logr, breaks=68, merge_tails=merge_tails)
>
> # Prepare the input PSD
> dist <- list( sigma= 0.0036, alpha= 0.9, gamma= 0.0, beta= -0.014 )
> class(dist) <- "LIHNPSD"
> dist <- rawmean(dist)
> dist$location <- 0.00014
>
> # Invoke the nonlinear fit (This will take some time!)
> #fit <- standardfit(dist, dt$logr, dt$h, trace=1, iter=10,
> # plotqq=1, weights=list(m3=5,m4=1,qq_df=4), merge_tails=merge_tails )
>
> # The final PSD
> #dist <- fit$dist
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.