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 0.6248818The 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.