R/dfm-classes.R
, R/dfm-subsetting.R
dfm-class.Rd
The dfm class of object is a type of Matrix-class object with
additional slots, described below. quanteda uses two subclasses of the
dfm
class, depending on whether the object can be represented by a
sparse matrix, in which case it is a dfm
class object, or if dense,
then a dfmDense
object. See Details.
# S4 method for dfm t(x) # S4 method for dfm colSums(x, na.rm = FALSE, dims = 1, ...) # S4 method for dfm rowSums(x, na.rm = FALSE, dims = 1, ...) # S4 method for dfm colMeans(x, na.rm = FALSE, dims = 1, ...) # S4 method for dfm rowMeans(x, na.rm = FALSE, dims = 1, ...) # S4 method for dfm,numeric Arith(e1, e2) # S4 method for numeric,dfm Arith(e1, e2) # S4 method for dfm,index,index,missing [(x, i, j, ..., drop = TRUE) # S4 method for dfm,index,index,logical [(x, i, j, ..., drop = TRUE) # S4 method for dfm,missing,missing,missing [(x, i, j, ..., drop = TRUE) # S4 method for dfm,missing,missing,logical [(x, i, j, ..., drop = TRUE) # S4 method for dfm,index,missing,missing [(x, i, j, ..., drop = TRUE) # S4 method for dfm,index,missing,logical [(x, i, j, ..., drop = TRUE) # S4 method for dfm,missing,index,missing [(x, i, j, ..., drop = TRUE) # S4 method for dfm,missing,index,logical [(x, i, j, ..., drop = TRUE)
x | the dfm object |
---|---|
na.rm | if |
dims | ignored |
... | additional arguments not used here |
e1 | first quantity in "+" operation for dfm |
e2 | second quantity in "+" operation for dfm |
i | index for documents |
j | index for features |
drop | always set to |
The dfm
class is a virtual class that will contain
dgCMatrix-class.
weightTf
the type of term frequency weighting applied to the dfm. Default is
"frequency"
, indicating that the values in the cells of the dfm are
simple feature counts. To change this, use the dfm_weight()
method.
weightFf
the type of document frequency weighting applied to the dfm. See
docfreq()
.
smooth
a smoothing parameter, defaults to zero. Can be changed using
the dfm_smooth()
method.
Dimnames
These are inherited from Matrix-class but are
named docs
and features
respectively.
# dfm subsetting dfmat <- dfm(tokens(c("this contains lots of stopwords", "no if, and, or but about it: lots", "and a third document is it"), remove_punct = TRUE)) dfmat[1:2, ]#> Document-feature matrix of: 2 documents, 16 features (59.4% sparse). #> features #> docs this contains lots of stopwords no if and or but #> text1 1 1 1 1 1 0 0 0 0 0 #> text2 0 0 1 0 0 1 1 1 1 1 #> [ reached max_nfeat ... 6 more features ]dfmat[1:2, 1:5]#> Document-feature matrix of: 2 documents, 5 features (40.0% sparse). #> features #> docs this contains lots of stopwords #> text1 1 1 1 1 1 #> text2 0 0 1 0 0