quanteda (version 1.5.2)

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

See Also

coef.textmodel_lsa, ca

Examples

# NOT RUN {
dfmat <- dfm(data_corpus_irishbudget2010)
tmod <- textmodel_ca(dfmat)
summary(tmod)
# }