| dudi.fca {ade4} | R Documentation |
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.
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)
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 |
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. |
dudi.fpca returns a list of class pca and dudi (see dudi) containing also
Daniel Chessel
Anne B Dufour dufour@biomserv.univ-lyon1.fr
Chevenet, F., Dolédec, S. and Chessel, D. (1994) A fuzzy coding approach for the analysis of long-term ecological data. Freshwater Biology, 31, 295–309.
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)
> 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))
> > > > > >