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

`textmodel_ca`

implements correspondence analysis scaling on a
dfm. The method is a fast/sparse version of function ca.

1 | ```
textmodel_ca(x, smooth = 0, nd = NA, sparse = FALSE, residual_floor = 0.1)
``` |

`x` |
the dfm on which the model will be fit |

`smooth` |
a smoothing parameter for word counts; defaults to zero. |

`nd` |
Number of dimensions to be included in output; if |

`sparse` |
retains the sparsity if set to |

`residual_floor` |
specifies the threshold for the residual matrix for
calculating the truncated svd.Larger value will reduce memory and time cost
but might reduce accuracy; only applicable when |

svds in the RSpectra package is applied to enable the fast computation of the SVD.

`textmodel_ca()`

returns a fitted CA textmodel that is a special
class of ca object.

You may need to set `sparse = TRUE`

) and
increase the value of `residual_floor`

to ignore less important
information and hence to reduce the memory cost when you have a very big
dfm.
If your attempt to fit the model fails due to the matrix being too large,
this is probably because of the memory demands of computing the *V
\times V* residual matrix. To avoid this, consider increasing the value of
`residual_floor`

by 0.1, until the model can be fit.

Kenneth Benoit and Haiyan Wang

Nenadic, O. & Greenacre, M. (2007). Correspondence Analysis in R, with Two- and Three-dimensional Graphics: The ca package. *Journal of Statistical Software*, 20(3).

1 2 3 | ```
dfmat <- quanteda::dfm(data_corpus_irishbudget2010)
tmod <- textmodel_ca(dfmat)
summary(tmod)
``` |

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