fcm-class: Virtual class "fcm" for a feature co-occurrence matrix

In quanteda: Quantitative Analysis of Textual Data

Virtual class "fcm" for a feature co-occurrence matrix

Description

The fcm class of object is a special type of fcm object with additional slots, described below.

Usage

## S4 method for signature 'fcm'
t(x)

## S4 method for signature 'fcm,numeric'
Arith(e1, e2)

## S4 method for signature 'numeric,fcm'
Arith(e1, e2)

## S4 method for signature 'fcm,index,index,missing'
x[i, j, ..., drop = TRUE]

## S4 method for signature 'fcm,index,index,logical'
x[i, j, ..., drop = TRUE]

## S4 method for signature 'fcm,missing,missing,missing'
x[i, j, ..., drop = TRUE]

## S4 method for signature 'fcm,missing,missing,logical'
x[i, j, ..., drop = TRUE]

## S4 method for signature 'fcm,index,missing,missing'
x[i, j, ..., drop = TRUE]

## S4 method for signature 'fcm,index,missing,logical'
x[i, j, ..., drop = TRUE]

## S4 method for signature 'fcm,missing,index,missing'
x[i, j, ..., drop = TRUE]

## S4 method for signature 'fcm,missing,index,logical'
x[i, j, ..., drop = TRUE]

Arguments

x	the fcm object
e1	first quantity in "+" operation for fcm
e2	second quantity in "+" operation for fcm
i	index for features
j	index for features
...	additional arguments not used here
drop	always set to FALSE

Slots

context

the context definition

window

the size of the window, if context = "window"

count

how co-occurrences are counted

weights

context weighting for distance from target feature, equal in length to window

margin

frequencies of features in the original dfm or tokens

tri

whether the lower triangle of the symmetric V \times V matrix is recorded

ordered

whether a term appears before or after the target feature are counted separately

See Also

fcm

Examples

# fcm subsetting
fcmat <- fcm(tokens(c("this contains lots of stopwords",
                  "no if, and, or but about it: lots"),
                remove_punct = TRUE))
fcmat[1:3, ]
fcmat[4:5, 1:5]

quanteda documentation built on Sept. 11, 2024, 6:08 p.m.