`summary`

method for class `"tsglm"`

.

1 2 |

`object` |
an object of class |

`B` |
controls the computation of standard errors. Is passed to |

`parallel` |
controls the computation of standard errors. Is passed to |

`level` |
controls the computation of conficence intervals. Is passed to |

`...` |
further arguments are currently ignored. Only for compatibility with generic function. |

Computes and returns a list of summary statistics of the fitted model given in argument `object`

.

A named list with the following elements:

`call` |
see |

`link` |
see |

`distr` |
see |

`residuals` |
see |

`coefficients` |
data frame with estimated parameters, their standard errors and confidence intervals (based on a normal approximation or a parametric bootstrap, see |

`level` |
numerical value giving the coverage rate of the confidence intervals. |

`number.coef` |
number of coefficients. |

`se.type` |
type of standard errors, see |

`se.bootstrapsamples` |
number of bootstrap samples used for estimation of the standard errors, see |

`logLik` |
value of the log-likelihood function evaluated at the (quasi) maximum likelihood estimate. |

`AIC` |
Akaike's information criterion (AIC), see |

`BIC` |
Bayesian information criterion (BIC), see |

`QIC` |
Quasi information criterion (QIC), see |

`pearson.resid` |
Pearson residuals, see |

Tobias Liboschik and Philipp Probst

S3 method `print`

.

`tsglm`

for fitting a GLM for time series of counts.

1 2 3 4 5 6 7 8 | ```
###Road casualties in Great Britain (see help("Seatbelts"))
timeseries <- Seatbelts[, "VanKilled"]
regressors <- cbind(PetrolPrice=Seatbelts[, c("PetrolPrice")],
linearTrend=seq(along=timeseries)/12)
#Logarithmic link function with Poisson distribution:
seatbeltsfit <- tsglm(ts=timeseries, link="log",
model=list(past_obs=c(1, 12)), xreg=regressors, distr="poisson")
summary(seatbeltsfit)
``` |

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