Compute cosine from Frobenius norm between kernels and display the
corresponding correlation plot.

```
cim.kernel(..., scale = TRUE,
method = c("circle", "square", "number", "shade", "color", "pie"))
```

## Arguments

- ...
list of kernels (called 'blocks') computed on different datasets and
measured on the same samples.

- scale
boleean. If `scale = TRUE`

, each block is standardized to zero
mean and unit variance and cosine normalization is performed on the kernel.
Default: `TRUE`

.

- method
character. The visualization method to be used. Currently, seven
methods are supported (see Details).

## Details

The displayed similarities are the kernel generalization of the
RV-coefficient described in Lavit *et al.*, 1994.

The plot is displayed using the `corrplot`

package. Seven
visualization methods are implemented: `"circle"`

(default),
`"square"`

, `"number"`

, `"pie"`

, `"shade"`

and
`"color"`

. Circle and square areas are proportional to the absolute value
of corresponding similarities coefficients.

## Value

`cim.kernel`

returns a matrix containing the cosine from Frobenius norm
between kernels.

## References

Lavit C., Escoufier Y., Sabatier R. and Traissac P. (1994). The ACT (STATIS
method). *Computational Statistics and Data Analysis*, **18**(1),
97-119.

Mariette J. and Villa-Vialaneix N. (2018). Unsupervised multiple kernel learning
for heterogeneous data integration. *Bioinformatics*, **34**(6), 1009-1015.

## Author

Jerome Mariette <jerome.mariette@inrae.fr>

Nathalie Vialaneix <nathalie.vialaneix@inrae.fr>

## Examples

```
data(TARAoceans)
# compute one kernel per dataset
phychem.kernel <- compute.kernel(TARAoceans$phychem, kernel.func = "linear")
pro.phylo.kernel <- compute.kernel(TARAoceans$pro.phylo, kernel.func = "abundance")
pro.NOGs.kernel <- compute.kernel(TARAoceans$pro.NOGs, kernel.func = "abundance")
# display similarities between kernels
cim.kernel(phychem = phychem.kernel,
pro.phylo = pro.phylo.kernel,
pro.NOGs = pro.NOGs.kernel,
method = "square")
```