nLogLike {countHMM} | R Documentation |

## Penalized negative log-likelihood

### Description

Computes the penalized negative log-likelihood using the forward algorithm as described in Adam *et al*. (2019). Not intended to be run by the user (internal function, called by the function `fitMod`

).

### Usage

```
nLogLike(parvect,x,N,stationary,lambda,sup,m,inflation)
```

### Arguments

`parvect` |
Vector of working parameters (as returned by |

`x` |
Vector of observed counts. |

`N` |
Integer, number of states. |

`stationary` |
Logical, determines whether the initial distribution of the Markov chain underlying the observed counts is the stationary distribution. |

`lambda` |
Vector of length |

`sup` |
Integer, determines the upper bound of the support of the state-dependent distributions. If |

`m` |
Integer, order of the difference penalties. |

`inflation` |
Count probabilities to be excluded from penalization (e.g. in the presence of zero-inflation). |

### Value

Numeric, the penalized negative log-likelihood.

### References

Adam, T., Langrock, R., and Weiß, C.H. (2019): Penalized Estimation of Flexible Hidden Markov Models for Time Series of Counts. arXiv:https://arxiv.org/pdf/1901.03275.pdf.

### Examples

```
# importing example data
x = read.table("http://www.hmms-for-time-series.de/second/data/earthquakes.txt")$V2
# computing the penalized negative log-likelihood
parvect = pn2pw(N=2,probs=cbind(dpois(x=0:41,lambda=14),dpois(x=0:41,lambda=26)),
gamma=matrix(c(0.95,0.05,0.05,0.95),ncol=2),delta=NULL,stationary=TRUE)
lambda = rep(10^4,2)
nLogLike(parvect=parvect,x=x,N=2,stationary=TRUE,lambda=lambda,sup=41,m=3,inflation=FALSE)
```

*countHMM*version 0.1.0 Index]