`depmix`

creates an object of class `depmix`

, a dependent
mixture model, otherwise known as hidden Markov model. For a short
description of the package see `depmixS4`

. See the vignette
for an introduction to hidden Markov models and the package.

1 2 3 4 5 |

`response` |
The response to be modeled; either a formula or a list of formulae (in the multivariate case); this interfaces to the glm and other distributions. See 'Details'. |

`data` |
An optional |

`nstates` |
The number of states of the model. |

`transition` |
A one-sided formula specifying the model for the transitions. See 'Details'. |

`family` |
A family argument for the response. This must be a list
of |

`prior` |
A one-sided formula specifying the density for the prior or initial state probabilities. |

`initdata` |
An optional data.frame to interpret the variables
occuring in |

`respstart` |
Starting values for the parameters of the response models. |

`trstart` |
Starting values for the parameters of the transition models. |

`instart` |
Starting values for the parameters of the prior or initial state probability model. |

`ntimes` |
A vector specifying the lengths of individual, i.e.
independent, time series. If not specified, the responses are
assumed to form a single time series, i.e. |

`...` |
Not used currently. |

The function `depmix`

creates an S4 object of class `depmix`

,
which needs to be fitted using `fit`

to optimize the
parameters.

The response model(s) are by default created by call(s) to
`GLMresponse`

using the `formula`

and the `family`

arguments, the latter specifying the error distribution. See
`GLMresponse`

for possible values of the `family`

argument for `glm`

-type responses (ie a subset of the `glm`

family options, and the multinomial). Alternative response
distributions are specified by using the `makeDepmix`

function. Its help page has examples of specifying a model with a
multivariate normal response, as well as an example of adding a
user-defined response model, in this case for the ex-gauss
distribution.

If `response`

is a list of formulae, the `response`

's are
assumed to be independent conditional on the latent state.

The transitions are modeled as a multinomial logistic model for each
state. Hence, the transition matrix can be modeled using time-varying
covariates. The prior density is also modeled as a multinomial
logistic. Both of these models are created by calls to
`transInit`

.

Starting values for the initial, transition, and response models may be
provided by their respective arguments. NB: note that the starting
values for the initial and transition models as well as of the
multinomial logit response models are interpreted as probabilities, and
internally converted to multinomial logit parameters. The order in
which parameters must be provided can be easily studied by using the
`setpars`

and `getpars`

functions.

Linear constraints on parameters can be provided as argument to the
`fit`

function.

The print function prints the formulae for the response, transition and prior models along with their parameter values.

Missing values are allowed in the data, but missing values in the covariates lead to errors.

`depmix`

returns an object of class `depmix`

which has the
following slots:

`response` |
A list of a list of response models; the first index runs over states; the second index runs over the independent responses in case a multivariate response is provided. |

`transition` |
A list of |

`prior` |
A multinomial logistic model for the initial state probabilities. |

`dens,trDens,init` |
See |

`stationary` |
Logical indicating whether the transitions are time-dependent or not; for internal use. |

`ntimes` |
A vector containing the lengths of independent time series. |

`nstates` |
The number of states of the model. |

`nresp` |
The number of independent responses. |

`npars` |
The total number of parameters of the model. Note: this
is |

Models are not fitted; the return value of `depmix`

is a model
specification without optimized parameter values. Use the `fit`

function to optimize parameters, and to specify additional constraints.

Ingmar Visser & Maarten Speekenbrink

Ingmar Visser and Maarten Speekenbrink (2010). depmixS4: An R Package for
Hidden Markov Models. *Journal of Statistical Software, 36(7)*, p. 1-21.

Lawrence R. Rabiner (1989). A tutorial on hidden Markov models and
selected applications in speech recognition. *Proceedings of
IEEE*, 77-2, p. 267-295.

`fit`

, `transInit`

, `GLMresponse`

,
`depmix-methods`

for accessor functions to `depmix`

objects.

For full control see the `makeDepmix`

help page and its
example section for the possibility to add user-defined response
distributions.

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 58 59 60 61 62 63 64 65 | ```
# create a 2 state model with one continuous and one binary response
# ntimes is used to specify the lengths of 3 separate series
data(speed)
mod <- depmix(list(rt~1,corr~1),data=speed,nstates=2,
family=list(gaussian(),multinomial("identity")),ntimes=c(168,134,137))
# print the model, formulae and parameter values
mod
set.seed(1)
# fit the model by calling fit
fm <- fit(mod)
# Volatility of S & P 500 returns
# (thanks to Chen Haibo for providing this example)
data(sp500)
# fit some models
msp <- depmix(logret~1,nstates=2,data=sp500)
set.seed(1)
fmsp <- fit(msp)
# plot posterior state sequence for the 2-state model
plot(ts(posterior(fmsp)[,2], start=c(1950,2),deltat=1/12),ylab="probability",
main="Posterior probability of state 1 (volatile, negative markets).",
frame=FALSE)
## Not run:
# this creates data with a single change point with Poisson data
set.seed(3)
y1 <- rpois(50,1)
y2 <- rpois(50,2)
ydf <- data.frame(y=c(y1,y2))
# fit models with 1 to 3 states
m1 <- depmix(y~1,ns=1,family=poisson(),data=ydf)
set.seed(1)
fm1 <- fit(m1)
m2 <- depmix(y~1,ns=2,family=poisson(),data=ydf)
set.seed(1)
fm2 <- fit(m2)
m3 <- depmix(y~1,ns=3,family=poisson(),data=ydf)
set.seed(1)
fm3 <- fit(m3,em=em.control(maxit=500))
# plot the BICs to select the proper model
plot(1:3,c(BIC(fm1),BIC(fm2),BIC(fm3)),ty="b")
## End(Not run)
## Not run:
# similar to the binomial model, data may also be entered in
# multi-column format where the n for each row can be different
dt <- data.frame(y1=c(0,1,1,2,4,5),y2=c(1,0,1,0,1,0),y3=c(4,4,3,2,1,1))
# specify a mixture model ...
m2 <- mix(cbind(y1,y2,y3)~1,data=dt,ns=2,family=multinomial("identity"))
set.seed(1)
fm2 <- fit(m2)
# ... or dependent mixture model
dm2 <- depmix(cbind(y1,y2,y3)~1,data=dt,ns=2,family=multinomial("identity"))
set.seed(1)
fdm2 <- fit(dm2)
## End(Not run)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.