Description Usage Arguments Value Note

Since all three of `tidylda`

,
`refit.tidylda`

, and
`predict.tidylda`

call `fit_lda_c`

,
we need a way to format the resulting posteriors and other user-facing
objects consistently. This function does that.

1 2 3 4 5 6 7 8 9 10 11 12 13 |

`lda` |
list output of |

`dtm` |
a document term matrix or term co-occurrence matrix of class |

`burnin` |
integer number of burnin iterations. |

`is_prediction` |
is this for a prediction (as opposed to initial fitting,
or update)? Defaults to |

`alpha` |
output of |

`eta` |
output of |

`optimize_alpha` |
did you optimize |

`calc_r2` |
did the user want to calculate R-squared when calculating the
the model? If |

`calc_likelihood` |
did you calculate the log likelihood when making a call
to |

`call` |
the result of calling |

`threads` |
number of parallel threads |

Returns an S3 object of class `tidylda`

with the following slots:

`beta`

is a numeric matrix whose rows are the posterior estimates
of P(token|topic)

`theta`

is a numeric matrix whose rows are the posterior estimates of
P(topic|document)

`lambda`

is a numeric matrix whose rows are the posterior estimates of
P(topic|token), calculated using Bayes's rule.
See `calc_lambda`

.

`alpha`

is the prior for topics over documents. If `optimize_alpha`

is `FALSE`

, `alpha`

is what the user passed when calling
`tidylda`

. If `optimize_alpha`

is `TRUE`

,
`alpha`

is a numeric vector returned in the `alpha`

slot from a
call to `fit_lda_c`

.

`eta`

is the prior for tokens over topics. This is what the user passed
when calling `tidylda`

.

`summary`

is the result of a call to `summarize_topics`

`call`

is the result of `match.call`

called at the top
of `tidylda`

`log_likelihood`

is a `tibble`

whose columns are
the iteration and log likelihood at that iteration. This slot is only populated
if `calc_likelihood = TRUE`

`r2`

is a numeric scalar resulting from a call to
`calc_rsquared`

. This slot only populated if
`calc_r2 = TRUE`

In general, the arguments of this function should be what the user passed
when calling `tidylda`

.

`burnin`

is used only to determine whether or not burn in iterations
were used when fitting the model. If `burnin > -1`

then posteriors
are calculated using `lda$Cd_mean`

and `lda$Cv_mean`

respectively.
Otherwise, posteriors are calculated using `lda$Cd_mean`

and
`lda$Cv_mean`

.

The class of `call`

isn't checked. It's just passed through to the
object returned by this function. Might be useful if you are using this
function for troubleshooting or something.

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