Description Usage Arguments Details Value Author(s) References See Also Examples

Estimates a Wordfish model using Conditional Maximum Likelihood.

1 2 |

`wfm` |
a word frequency matrix |

`dir` |
set global identification by forcing |

`control` |
list of estimation options |

`verbose` |
produce a running commentary |

Fits a Wordfish model with document ideal points constrained to mean zero and unit standard deviation.

The `control`

list specifies options for the estimation process.
These are: `tol`

, the proportional change in log likelihood
sufficient to halt estimatioe, `sigma`

the standard deviation
for the beta prior in poisson form, and `startparams`

a
previously fitted wordfish model. `verbose`

generates
a running commentary during estimation

The model has two equivalent forms: a poisson model with two sets of document and two sets of word parameters, and a multinomial with two sets of word parameters and document ideal points. The first form is used for estimation, the second for summarizing and prediction.

The model is regularized by assuming a prior on beta with mean zero and standard deviation sigma (in poisson form). If you don't want to regularize, set beta to a large number.

An object of class wordfish. This is a list containing:

`dir` |
global identification of the dimension |

`theta` |
document positions |

`alpha` |
document fixed effects |

`beta` |
word slope parameters |

`psi` |
word fixed effects |

`docs` |
names of the documents |

`words` |
names of words |

`sigma` |
regularization parameter for betas in poisson form |

`ll` |
final log likelihood |

`se.theta` |
standard errors for document position |

`data` |
the original data |

Will Lowe

Slapin and Proksch (2008) 'A Scaling Model for Estimating Time-Series Party Positions from Texts.' American Journal of Political Science 52(3):705-772.

`plot.wordfish`

, `summary.wordfish`

,
`coef.wordfish`

, `fitted.wordfish`

,
`predict.wordfish`

, `sim.wordfish`

1 2 3 | ```
dd <- sim.wordfish()
wf <- wordfish(dd$Y)
summary(wf)
``` |

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