dudi.hillsmith {ade4} | R Documentation

## Ordination of Tables mixing quantitative variables and factors

### Description

performs a multivariate analysis with mixed quantitative variables and factors.

### Usage

    dudi.hillsmith(df, row.w = rep(1, nrow(df))/nrow(df),
       scannf = TRUE, nf = 2)

### Arguments

`df` | a data frame with mixed type variables (quantitative and factor)
`row.w` | a vector of row weights, by default uniform row weights are used
`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

### Details

If `df` contains only quantitative variables, this is equivalent to a normed PCA.
If `df` contains only factors, this is equivalent to an MCA.

This analysis is the Hill and Smith method and is very similar to `dudi.mix` function. The differences are that `dudi.hillsmith` allow to use various row weights, while `dudi.mix` deals with ordered variables.
The principal components of this analysis are centered and normed vectors maximizing the sum of :
squared correlation coefficients with quantitative variables
correlation ratios with factors

### Value

Returns a list of class `mix` and `dudi` (see dudi) containing also

`index` | a factor giving the type of each variable : f = factor, q = quantitative
`assign` | a factor indicating the initial variable for each column of the transformed table
`cr` | a data frame giving for each variable and each score: the squared correlation coefficients if it is a quantitative variable the correlation ratios if it is a factor

### Author(s)

Stephane Dray dray@biomserv.univ-lyon1.fr

### References

Hill, M. O., and A. J. E. Smith. 1976. Principal component analysis of taxonomic data with multi-state discrete characters. Taxon, 25:249-255.

### See Also

`dudi.mix`

### Examples

       data(dunedata)
       attributes(dunedata$envir$use)$class <- "factor"   # use dudi.mix for ordered data
       dd1 <- dudi.hillsmith(dunedata$envir, scann = FALSE)
       scatter.dudi(dd1, clab.r = 1, clab.c = 1.5)

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