quanteda.textmodels (version 0.9.9)

textmodel_ca: Correspondence analysis of a document-feature matrix

Description

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

Usage

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

Value

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

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

Author

Kenneth Benoit and Haiyan Wang

Details

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

References

Nenadic, O. & Greenacre, M. (2007). Correspondence Analysis in R, with Two- and Three-dimensional Graphics: The ca package. Journal of Statistical Software, 20(3). tools:::Rd_expr_doi("10.18637/jss.v020.i03")

See Also

coef.textmodel_lsa(), ca

Examples

library("quanteda")
dfmat <- dfm(tokens(data_corpus_irishbudget2010))
tmod <- textmodel_ca(dfmat)
summary(tmod)