ade4 (version 1.2-2)

dudi.fca: Fuzzy Correspondence Analysis

Description

This function analyses a table of fuzzy variables. A fuzzy variable takes values of type $a=(a_1,\dots,a_k)$ giving the importance of k categories. A missing data is denoted (0,...,0). Only the profile a/sum(a) is used, and missing data are replaced by the mean profile of the others in the function prep.fuzzy.var. See ref. for details.

Usage

prep.fuzzy.var (df, col.blocks, row.w = rep(1, nrow(df)))
dudi.fca(df, scannf = TRUE, nf = 2)

Arguments

df

a data frame containing positive or null values

col.blocks

a vector containing the number of categories for each fuzzy variable

row.w

a vector of row weights

scannf

a logical value indicating whether the eigenvalues bar plot should be displayed

nf

if scannf FALSE, an integer indicating the number of kept axes

Value

• The function prep.fuzzy.var returns a data frame with the attribute col.blocks. The function dudi.fca returns a list of class fca and dudi (see dudi) containing also
• cr
  a data frame which rows are the blocs, columns are the kept axes, and values are the correlation ratios.

References

Chevenet, F., Dol�dec, S. and Chessel, D. (1994) A fuzzy coding approach for the analysis of long-term ecological data. Freshwater Biology, 31, 295--309.

Examples

w1 <- matrix(c(1,0,0,2,1,1,0,2,2,0,1,0,1,1,1,0,1,3,1,0), 4, 5)
w1 <- data.frame(w1)
w2 <- prep.fuzzy.var(w1, c(2,3))
w1
w2
attributes(w2)

data(bsetal97)
w <- prep.fuzzy.var(bsetal97$biol, bsetal97$biol.blo)
scatter(dudi.fca(w, scann = FALSE, nf = 3), csub = 3, clab.moda = 1.5)

w1 <- prep.fuzzy.var(bsetal97$biol, bsetal97$biol.blo)
w2 <- prep.fuzzy.var(bsetal97$ecol, bsetal97$ecol.blo)
d1 <- dudi.fca(w1, scann = FALSE, nf = 3)
d2 <- dudi.fca(w2, scann = FALSE, nf = 3)
plot(coinertia(d1, d2, scann = FALSE))