hmm.discnp (version 3.0-9)

weissData: Data from “An Introduction to Discrete-Valued Time Series”

Description

Data sets from the book “An Introduction to Discrete-Valued Time Series” by Christian H. Weiß.

Usage

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)

Format

• 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.

Details

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.

References

Christian H. Weiß (2018). An Introduction to Discrete-Valued Time Series. Chichester: John Wiley & Sons.

Examples

if (FALSE) {
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
}