Description Details Author(s) References See Also Examples
Exact and Whittle MLE for time series models with hyperbolic decay. Simulation and regression supported for FGN.
Package: | FGN |
Type: | Package |
Version: | 2.0-12 |
Date: | 2014-05-15 |
License: | CC BY-NC-SA 3.0 |
LazyLoad: | yes |
LazyData: | yes |
A. I. McLeod and Justin Veenstra
Maintainer: aimcleod@uwo.ca
Hipel, K.W. and McLeod, A.I., (2005). Time Series Modelling of Water Resources and Environmental Systems. Electronic reprint of our book orginally published in 1994. http://www.stats.uwo.ca/faculty/aim/1994Book/.
McLeod, A.I., Yu, Hao, Krougly, Zinovi L. (2007). Algorithms for Linear Time Series Analysis, Journal of Statistical Software.
McLeod, A.I. and Veenstra, Justin (2012). Hyperbolic Decay Time Series Models (in press).
HurstK
,
FitFGN
,
FitRegressionFGN
,
SimulateFGN
,
print.FitFGN
,
summary.FitFGN
,
predict.FitFGN
,
plot.FitFGN
,
residuals.FitFGN
,
GetFitFGN
,
GetFitFD
,
GetFitPLS
,
GetFitPLA
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | #Example 1
#Compare HurstK and MLE for H
#Hurst K for Nile Minima
data(NileMin)
HurstK(NileMin)
out<-FitFGN(NileMin)
summary(out)
plot(out)
coef(out)
#
#Example 2.
#Compare models
## Not run:
T1 <- proc.time()[3]
ansFD <- GetFitFD(NileMin)
T2 <- proc.time()[3]
ansFGN <- GetFitFGN(NileMin)
T3 <- proc.time()[3]
ansPLS <- GetFitPLS(NileMin)
T4 <- proc.time()[3]
ansPLA <- GetFitPLA(NileMin)
T5 <- proc.time()[3]
tbLLE <- c(ansFD[[2]],ansFGN[[2]],ansPLS[[2]],ansPLA[[2]])
est <- c(ansFD[[3]],ansFGN[[3]],ansPLS[[3]],ansPLA[[3]])
tbLL <- round(tbLLE, 2)
est <- round(est, 3)
T<-c(T2-T1,T3-T2,T4-T3,T5-T4)
m<-matrix(c(est,tbLL, T),nrow=4, ncol=3)
dimnames(m)<-list(list("FD","FGN","PLS","PLA"), list("alpha","logL", "time"))
mE <- m
mE
#
T1 <- proc.time()[3]
ansFD <- GetFitFD(NileMin, algorithm="wmle")
T2 <- proc.time()[3]
ansFGN <- GetFitFGN(NileMin, algorithm="wmle")
T3 <- proc.time()[3]
ansPLA <- GetFitPLS(NileMin, algorithm="wmle")
T4 <- proc.time()[3]
ansPLS <- GetFitPLA(NileMin, algorithm="wmle")
T5 <- proc.time()[3]
#tbLL <- c(ansFD[[2]],ansFGN[[2]],ansPLS[[2]],ansPLA[[2]])
z <- NileMin-mean(NileMin)
tbLLW <- c(LLFD(ansFD[[1]],z), LLFGN(ansFGN[[1]],z), LLPLS(ansPLS[[1]],z), LLPLA(ansPLA[[1]],z))
est <- c(ansFD[[3]],ansFGN[[3]],ansPLS[[3]],ansPLA[[3]])
tbLL <- round(tbLLW, 2)
est <- round(est, 3)
T<-c(T2-T1,T3-T2,T4-T3,T5-T4)
m<-matrix(c(est,tbLL, T),nrow=4, ncol=3)
dimnames(m)<-list(list("FD","FGN","PLS","PLA"), list("alpha","logL", "time"))
mW<-m
mW
m<-cbind(mE,mW)
m
## End(Not run)
|
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