weissData | R Documentation |
Data sets from the book “An Introduction to Discrete-Valued Time Series” by Christian H. Weiß.
data(Bovine) data(Cryptosporidiosis) data(Downloads) data(EricssonB_Jul2) data(FattyLiver) data(FattyLiver2) data(goldparticle380) data(Hanta) data(InfantEEGsleepstates) data(IPs) data(LegionnairesDisease) data(OffshoreRigcountsAlaska) data(PriceStability) data(Strikes) data(WoodPeweeSong)
Bovine
A character vector of length 8419.
Cryptosporidiosis
A numeric (integer) vector of length 365.
Downloads
A numeric (integer) vector of length 267.
EricssonB_Jul2
A numeric (integer) vector of length 460.
FattyLiver2
A numeric (integer) vector of length 449.
FattyLiver
A numeric (integer) vector of length 928.
goldparticle380
A numeric (integer) vector of length 380.
Hanta
A numeric (integer) vector of length 52.
InfantEEGsleepstates
A character vector of length 107.
IPs
A numeric (integer) vector of length 241.
LegionnairesDisease
A numeric (integer) vector of length 365.
OffshoreRigcountsAlaska
A numeric (integer) vector of length 417.
PriceStability
A numeric (integer) vector of length 152.
Strikes
A numeric (integer) vector of length 108.
WoodPeweeSong
A numeric (integer) vector of length 1327.
For detailed information about each of these data sets, see the book cited in the References.
Note that the data sets Cryptosporidiosis
and LegionnairesDisease
are actually
called
Cryptosporidiosis_02-08
and
LegionnairesDisease_02-08
in the given reference.
The
“suffixes” were removed since the minus sign causes
problems in a variable name in R
.
These data sets were kindly provided by Prof. Christian
H. Weiß. The package author is also pleased
to acknowledge the kind permission granted by Prof. Kurt
Brännäs (Professor Emeritus of Economics at
Umeå University) to include the Ericsson time series
data set (EricssonB_Jul2
).
Christian H. Weiß (2018). An Introduction to Discrete-Valued Time Series. Chichester: John Wiley & Sons.
## Not run: fit1 <- hmm(WoodPeweeSong,K=2,verbose=TRUE) # EM converges in 6 steps --- suspicious. set.seed(321) fit2 <- hmm(WoodPeweeSong,K=2,verbose=TRUE,rand.start=list(tpm=TRUE,Rho=TRUE)) # 52 steps --- note the huge difference between fit1$log.like and fit2$log.like! set.seed(321) fit3 <- hmm(WoodPeweeSong,K=2,verbose=TRUE,method="bf", rand.start=list(tpm=TRUE,Rho=TRUE)) # log likelihood essentially the same as for fit2 ## End(Not run)
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