`textmodel_ca`

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

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

## Arguments

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 `NA` (the
default) then the maximum possible dimensions are included. |

sparse |
retains the sparsity if set to `TRUE` ; set it to
`TRUE` if `x` (the dfm) is too big to be allocated after
converting to dense |

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 `sparse = TRUE` |

## Value

`textmodel_ca()`

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

## Details

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

## Note

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.

## References

Nenadic, O. and Greenacre, M. (2007). Correspondence analysis in
R, with two- and three-dimensional graphics: The ca package. *Journal
of Statistical Software*, 20 (3), http://www.jstatsoft.org/v20/i03/.

## See also

## Examples

#> Error in get(".SigLength", envir = env): object '.SigLength' not found

summary(wca)

#> Error in summary(wca): object 'wca' not found