Adds a heatmap representing the correlation coefficients to a MCA cloud of individuals, for a numerical supplementary variable or one category of a categorical supplementary variable.

```
ggadd_corr(p, resmca, var, cat = levels(var)[1], axes = c(1,2),
xbins = 20, ybins = 20, min.n = 1, pal = "RdYlBu", limits = NULL, legend = "right")
```

## Arguments

- p
`ggplot2`

object with the cloud of variables

- resmca
object of class `MCA`

, `speMCA`

, `csMCA`

, `stMCA`

or `multiMCA`

- var
factor or numerical vector. The supplementary variable used for the heatmap.

- cat
character string. The category of `var`

to plot (by default, the first level of `var`

is plotted). Only used if var is a factor.

- axes
numeric vector of length 2, specifying the components (axes) to plot. Default is c(1,2).

- xbins
integer. Number of bins in the x axis. Default is 20.

- ybins
integer. Number of bins in the y axis. Default is 20.

- min.n
integer. Minimal number of points for a tile to be drawn. By default, every tiles are drawn.

- pal
character string. Name of a (preferably diverging) palette from the `RColorBrewer`

package. Default is "RdYlBu".

- limits
numerical vector of length 2. Lower and upper limits of the correlation coefficients for the color scale. Should be centered around 0 for a better view of under/over-representations (for example c(-0.2,0.2)). By default, the maximal absolute value of the correlation coefficients is used.

- legend
the position of legends ("none", "left", "right", "bottom", "top", or two-element numeric vector). Default is right.

## Details

For each tile of the heatmap, a correlation coefficient is computed between the supplementary variable and the fact of belonging to the tile. This gives a view of the under/over-representation of the supplementary variable according to the position in the cloud of individuals.

## References

Le Roux B. and Rouanet H., *Multiple Correspondence Analysis*, SAGE, Series: Quantitative Applications in the Social Sciences, Volume 163, CA:Thousand Oaks (2010).

Le Roux B. and Rouanet H., *Geometric Data Analysis: From Correspondence Analysis to Stuctured Data Analysis*, Kluwer Academic Publishers, Dordrecht (June 2004).

## Examples

```
# specific MCA of Taste example data set
data(Taste)
junk <- c("FrenchPop.NA", "Rap.NA", "Rock.NA", "Jazz.NA", "Classical.NA",
"Comedy.NA", "Crime.NA", "Animation.NA", "SciFi.NA", "Love.NA",
"Musical.NA")
mca <- speMCA(Taste[,1:11], excl = junk)
# correlation heatmap for Age = 50+
p <- ggcloud_indiv(mca, col = "lightgrey")
ggadd_corr(p, mca, var = Taste$Age, cat = "50+", xbins = 10, ybins = 10)
```