The goal of inteRmodel is to help you with interaction models using RGCCA to asses the stability of the model and the best model possible given the data provided. The package assumes that the blocks are all connected.

You can apply bootstraping to the models with `search_model`

, then `iterate_model`

or bootstrap the samples with `boot_samples_sgcca`

and `boot_index_sgcca`

.

For further information about the regularized canonical correlations and the interpretation read the RGCCA vignette and the associated articles.

If the CRAN version is too slow you could try my fork which has some more dependencies but is much faster.

## Installation

You can install the released version of inteRmodel from Github with:

`devtools::install_github("llrs/inteRmodel")`

## Example

This is a basic example which shows you how to apply the bootstraping on this analysis:

```
library(inteRmodel)
data("Russett", package = "RGCCA")
X_agric <- as.matrix(Russett[, c("gini", "farm", "rent")])
X_ind <- as.matrix(Russett[, c("gnpr", "labo")])
X_polit <- as.matrix(Russett[ , c("inst", "ecks", "death", "demostab",
"dictator")])
A <- list(X_agric, X_ind, X_polit)
set.seed(879138)
boots <- 10
C <- matrix(c(0, 0, 1, 0, 0, 1, 1, 1, 0), 3, 3)
boot_i <- boot_samples_sgcca(A = A, C = C, c1 = rep(1, 3), nb_boot = boots)
```

We can see the AVE of the bootstraps by using:

```
head(boot_i$AVE)
#> inner outer
#> [1,] 0.3003373 0.6522110
#> [2,] 0.3871677 0.6821278
#> [3,] 0.4648659 0.6416236
#> [4,] 0.4732215 0.7039174
#> [5,] 0.4903151 0.6333550
#> [6,] 0.3474681 0.6248818
```

The AVE scores is for each bootstrap sample, which help to decide which is the stability of the model.

See the vignette for a full example.