textmodel_ca implements correspondence analysis scaling on a
dfm. The method is a fast/sparse version of function ca.
the dfm on which the model will be fit
a smoothing parameter for word counts; defaults to zero.
Number of dimensions to be included in output; if
retains the sparsity if set to
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
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).
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Package version: 2.1.2 Parallel computing: 1 of 1 threads used. See https://quanteda.io for tutorials and examples. Attaching package: ‘quanteda’ The following object is masked from ‘package:quanteda.textmodels’: data_dfm_lbgexample The following object is masked from ‘package:utils’: View Length Class Mode sv 7 -none- numeric nd 1 -none- numeric rownames 14 -none- character rowmass 14 -none- numeric rowdist 14 -none- numeric rowinertia 14 -none- numeric rowcoord 98 -none- numeric rowsup 0 -none- logical colnames 5141 -none- character colmass 5141 -none- numeric coldist 5141 -none- numeric colinertia 5141 -none- numeric colcoord 35987 -none- numeric colsup 0 -none- logical call 2 -none- call
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