## 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 a 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 stephane.dray@univ-lyon1.fr
Anne B Dufour anne-beatrice.dufour@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.

`dudi.mix`

### Examples

```data(dunedata)
attributes(dunedata\$envir\$use)\$class <- "factor"   # use dudi.mix for ordered data
dd1 <- dudi.hillsmith(dunedata\$envir, scann = FALSE)