mfa {ade4} | R Documentation

## Multiple Factorial Analysis

### Description

performs a multiple factorial analysis, using an object of class `ktab`.

### Usage

    mfa(X, option = c("lambda1", "inertia", "uniform", "internal"),
        scannf = TRUE, nf = 3)
    plot.mfa (x, xax = 1, yax = 2, option.plot = 1:4, ...)
    print.mfa (x, ...)
    summary.mfa (object, ...)

### Arguments

`X` | K-tables, an object of class `ktab`
`option` | a string of characters for the weighting of arrays options : `lambda1` weighting of group k by the inverse of the first eigenvalue of the k analysis `inertia` weighting of group k by the inverse of the total inertia of the array k `uniform` uniform weighting of groups `internal` weighting included in `X$tabw`
`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
`x, object` | an object of class 'mfa'
`xax, yax` | the numbers of the x-axis and the y-axis
`option.plot` | an integer between 1 and 4, otherwise the 4 components of the plot are displayed
`...` | further arguments passed to or from other methods

### Value

Returns a list including :

`tab` | a data frame with the modified array
`rank` | a vector of ranks for the analyses
`eig` | a numeric vector with the all eigenvalues
`li` | a data frame with the coordinates of rows
`TL` | a data frame with the factors associated to the rows (indicators of table)
`co` | a data frame with the coordinates of columns
`TC` | a data frame with the factors associated to the columns (indicators of table)
`blo` | a vector indicating the number of variables for each table
`lisup` | a data frame with the projections of normalized scores of rows for each table
`cg` | a data frame with the gravity center for the lisup
`link` | a data frame containing the projected inertia and the links between the arrays and the reference array
`corli` | a data frame giving the correlations between the $lisup and the $li

### Author(s)

Daniel Chessel chessel@biomserv.univ-lyon1.fr

### References

Escofier, B. and Pagès, J. (1994) Multiple factor analysis (AFMULT package), Computational Statistics and Data Analysis, 18, 121–140.

### Examples

    data(friday87)
    w1 <- data.frame(scale(friday87$fau, scal = FALSE))
    w2 <- ktab.data.frame(w1, friday87$fau.blo,
        tabnames = friday87$tab.names)
    mfa1 <- mfa(w2, scann = FALSE)
    mfa1
    plot(mfa1)

    data(escopage)
    w <- data.frame(scale(escopage$tab))
    w <- ktab.data.frame(w, escopage$blo, tabnames = escopage$tab.names)
    plot(mfa(w, scann = FALSE))

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