quanteda (version 0.99.22)

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, and returns a special class of ca object.

Usage

textmodel_ca(x, smooth = 0, nd = NA, sparse = FALSE, threads = 1,
  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

threads

the number of threads to be used; set to 1 to use a serial version of the function; only applicable when sparse = TRUE

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

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/.

Examples

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