quanteda (version 1.3.13)

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)

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.

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

coef.textmodel_lsa, ca

Examples

# NOT RUN {
ieDfm <- dfm(data_corpus_irishbudget2010)
wca <- textmodel_ca(ieDfm)
summary(wca)
# }