R/fcm-classes.R, R/fcm-subsetting.R
fcm-class.RdThe fcm class of object is a special type of fcm object with additional slots, described below.
# S4 method for fcm
t(x)
# S4 method for fcm,numeric
Arith(e1, e2)
# S4 method for numeric,fcm
Arith(e1, e2)
# S4 method for fcm,index,index,missing
[(x, i, j, ..., drop = TRUE)
# S4 method for fcm,index,index,logical
[(x, i, j, ..., drop = TRUE)
# S4 method for fcm,missing,missing,missing
[(x, i, j, ..., drop = TRUE)
# S4 method for fcm,missing,missing,logical
[(x, i, j, ..., drop = TRUE)
# S4 method for fcm,index,missing,missing
[(x, i, j, ..., drop = TRUE)
# S4 method for fcm,index,missing,logical
[(x, i, j, ..., drop = TRUE)
# S4 method for fcm,missing,index,missing
[(x, i, j, ..., drop = TRUE)
# S4 method for fcm,missing,index,logical
[(x, i, j, ..., drop = TRUE)the fcm object
first quantity in "+" operation for fcm
second quantity in "+" operation for fcm
index for features
index for features
additional arguments not used here
always set to FALSE
contextthe context definition
windowthe size of the window, if context = "window"
counthow co-occurrences are counted
weightscontext weighting for distance from target feature, equal in length to window
margintriwhether the lower triangle of the symmetric \(V \times V\) matrix is recorded
orderedwhether a term appears before or after the target feature are counted separately
# 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, ]
#> Feature co-occurrence matrix of: 3 by 12 features.
#> features
#> features this contains lots of stopwords no if and or but
#> this 0 1 1 1 1 0 0 0 0 0
#> contains 0 0 1 1 1 0 0 0 0 0
#> lots 0 0 0 1 1 1 1 1 1 1
#> [ reached max_nfeat ... 2 more features ]
fcmat[4:5, 1:5]
#> Feature co-occurrence matrix of: 2 by 5 features.
#> features
#> features this contains lots of stopwords
#> of 0 0 0 0 1
#> stopwords 0 0 0 0 0