View source: R/rgcca_stability.R

rgcca_stability | R Documentation |

This function can be used to identify the most stable variables identified as relevant by SGCCA. A Variable Importance in the Projection (VIP) based criterion is used to identify the most stable variables.

```
rgcca_stability(
rgcca_res,
keep = vapply(rgcca_res$a, function(x) mean(x != 0), FUN.VALUE = 1),
n_boot = 100,
n_cores = 1,
verbose = TRUE,
balanced = TRUE,
keep_all_variables = FALSE
)
```

`rgcca_res` |
A fitted RGCCA object (see |

`keep` |
A numeric vector indicating the proportion of variables per block to select. |

`n_boot` |
The number of bootstrap samples (default: 100). |

`n_cores` |
The number of cores for parallelization. |

`verbose` |
A logical value indicating if the progress of the procedure is reported. |

`balanced` |
A logical value indicating if a balanced bootstrap procedure is performed or not (default is TRUE). |

`keep_all_variables` |
A logical value indicating if all variables have to be kept even when some of them have null variance for at least one bootstrap sample (default is FALSE). |

A rgcca_stability object that can be printed and plotted.

`top` |
A data.frame giving the indicator (VIP) on which the variables are ranked. |

`n_boot` |
The number of bootstrap samples, returned for further use. |

`keepVar` |
The indices of the most stable variables. |

`bootstrap` |
A data.frame with the block weight vectors computed on each bootstrap sample. |

`rgcca_res` |
An RGCCA object fitted on the most stable variables. |

```
## Not run:
###########################
# stability and bootstrap #
###########################
data("ge_cgh_locIGR", package = "gliomaData")
blocks <- ge_cgh_locIGR$multiblocks
Loc <- factor(ge_cgh_locIGR$y)
levels(Loc) <- colnames(ge_cgh_locIGR$multiblocks$y)
blocks[[3]] <- Loc
fit_sgcca <- rgcca(blocks,
sparsity = c(.071, .2, 1),
ncomp = c(1, 1, 1),
scheme = "centroid",
verbose = TRUE, response = 3
)
boot_out <- rgcca_bootstrap(fit_sgcca, n_boot = 100, n_cores = 1)
fit_stab <- rgcca_stability(fit_sgcca,
keep = sapply(fit_sgcca$a, function(x) mean(x != 0)),
n_cores = 1, n_boot = 10,
verbose = TRUE
)
boot_out <- rgcca_bootstrap(
fit_stab, n_boot = 500, n_cores = 1, verbose = TRUE
)
plot(boot_out, block = 1:2, n_mark = 2000, display_order = FALSE)
## End(Not run)
```

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.