hmm.discnp (version 0.2-4)

colifCount: Coliform counts in sea-water samples

Description

Transformed counts of faecal coliform bacteria in sea water samples collected at seven locations near Sydney NSW, Australia. There were four “controls”: Longreef, Bondi East, Port Hacking “50”, and Port Hacking “100” and three “outfalls”: Bondi Offshore, Malabar Offshore and North Head Offshore. At each location measurements were made at four depths: 0, 20, 40, and 60 meters. A large fraction of the counts are missing values.

Usage

colifCount

Arguments

Format

A data frame with 5432 observations on the following 6 variables.

y

Transformed measures of the number of faecal coliform count bacteria. The original measures were obtained by a repeated dilution process.

locn

a factor with levels Longreef, Bondi East, Port Hacking 50, Port Hacking 100, Bondi Offshore, Malabar Offshore and North Head Offshore.

depth

a factor with levels 0 (0 metres), 20 (20 metres), 40 (40 metres), 60 (60 metres).

ma.com

A factor with levels no and yes, indicating whether the Malabar sewage outfall had been commissioned.

nh.com

A factor with levels no and yes, indicating whether the North Head sewage outfall had been commissioned.

bo.com

A factor with levels no and yes, indicating whether the Bondi Offshore sewage outfall had been commissioned.

Modelling

The hidden Markov models applied to these data by Turner et al. (1998) and by Turner (2008) were much more complex and elaborate than those fitted in the examples in this package. See the references for details.

Details

The observations corresponding to each location-depth combination constitute a time series. The sampling interval is ostensibly 1 week; distinct time series are ostensibly synchronous. The measurements were made over a 194 week period. Due to exigencies of weather, the unreliabitity of boats and other factors the collection times were actually highly irregular and have been rounded to the neares week. Often no sample was obtained at a given site within a week of the putative collection time, in which the observed count is given as a missing value. In fact over 75% of the counts are missing. See Turner et al. (1998) for more detail.

References

T. Rolf Turner, Murray A. Cameron, and Peter J. Thomson. Hidden Markov chains in generalized linear models. Canadian J. Statist. 26 (1998) 107 -- 125.

Rolf Turner. Direct maximization of the likelihood of a hidden Markov model. Comp. Statist. Data Anal. 52 (2008) 4147--4160.