dudi.fca {ade4}R Documentation

Fuzzy Correspondence Analysis and Fuzzy Principal Components Analysis

Description

Theses functions analyse a table of fuzzy variables.

A fuzzy variable takes values of type a=(a1,\dots,ak) 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)
dudi.fpca(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.
The function dudi.fpca returns a list of class pca and dudi (see dudi) containing also

  1. cent
  2. norm
  3. blo
  4. indica
  5. FST
  6. inertia

Author(s)

Daniel Chessel
Anne B Dufour dufour@biomserv.univ-lyon1.fr

References

Chevenet, F., Dol├ędec, 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)
scatter(dudi.fpca(w, scann = FALSE, nf = 3), csub = 3, clab.moda = 1.5)

## Not run: 
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))

## End(Not run)

Worked out examples


> library(ade4)
> ### Name: dudi.fca
> ### Title: Fuzzy Correspondence Analysis and Fuzzy Principal Components
> ###   Analysis
> ### Aliases: dudi.fca dudi.fpca prep.fuzzy.var
> ### Keywords: multivariate
> 
> ### ** 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))
1 missing data found in block 1 
1 missing data found in block 2 
> w1
  X1 X2 X3 X4 X5
1  1  1  2  1  1
2  0  1  0  1  3
3  0  0  1  1  1
4  2  2  0  0  0
> w2 
         X1        X2        X3        X4        X5
1 0.5000000 0.5000000 0.5000000 0.2500000 0.2500000
2 0.0000000 1.0000000 0.0000000 0.2500000 0.7500000
3 0.3333333 0.6666667 0.3333333 0.3333333 0.3333333
4 0.5000000 0.5000000 0.2777778 0.2777778 0.4444444
> attributes(w2)
$names
[1] "X1" "X2" "X3" "X4" "X5"

$row.names
[1] 1 2 3 4

$class
[1] "data.frame"

$col.blocks
FV1 FV2 
  2   3 

$row.w
[1] 0.25 0.25 0.25 0.25

$col.freq
[1] 0.3333333 0.6666667 0.2777778 0.2777778 0.4444444

$col.num
[1] 1 1 2 2 2
Levels: 1 2

> 
> data(bsetal97)
> w <- prep.fuzzy.var(bsetal97$biol, bsetal97$biol.blo)
17 missing data found in block 1 
14 missing data found in block 2 
28 missing data found in block 3 
8 missing data found in block 4 
5 missing data found in block 5 
19 missing data found in block 6 
10 missing data found in block 7 
5 missing data found in block 8 
2 missing data found in block 9 
12 missing data found in block 10 
> scatter(dudi.fca(w, scann = FALSE, nf = 3), csub = 3, clab.moda = 1.5)
> scatter(dudi.fpca(w, scann = FALSE, nf = 3), csub = 3, clab.moda = 1.5)
> 
> w1 <- prep.fuzzy.var(bsetal97$biol, bsetal97$biol.blo)
17 missing data found in block 1 
14 missing data found in block 2 
28 missing data found in block 3 
8 missing data found in block 4 
5 missing data found in block 5 
19 missing data found in block 6 
10 missing data found in block 7 
5 missing data found in block 8 
2 missing data found in block 9 
12 missing data found in block 10 
> w2 <- prep.fuzzy.var(bsetal97$ecol, bsetal97$ecol.blo)
6 missing data found in block 1 
16 missing data found in block 2 
5 missing data found in block 3 
9 missing data found in block 4 
15 missing data found in block 5 
47 missing data found in block 6 
6 missing data found in block 7 
> d1 <- dudi.fca(w1, scann = FALSE, nf = 3)
> d2 <- dudi.fca(w2, scann = FALSE, nf = 3)
> plot(coinertia(d1, d2, scann = FALSE))
> 
> 
> 
> 
> 
> 

[Package ade4 Index]