getRobustWeightsMultiBinary | R Documentation |

SmCCNet algorithm with multi-omics data and binary phenotype. This is a stepwise approach (1) use SmCCA to identify relationship between omics (exlude phenotype), (2) within highly connected omics features selected in step 1, identify relationship between these selected omics features and phenotype of interest with sparse PLS. First, it computes PLSDA by assuming outcome is continuous to extract multiple latent factors, then uses latent factors to fit logistic regression, and weight latent factor by regression parameters. Refer to multi-omics vignette for more detail.

```
getRobustWeightsMultiBinary(
X,
Y,
Between_Discriminate_Ratio = c(1, 1),
SubsamplingPercent = NULL,
CCcoef = NULL,
LambdaBetween,
LambdaPheno = NULL,
SubsamplingNum = 1000,
ncomp_pls = 3,
EvalClassifier = FALSE,
testData = NULL
)
```

`X` |
A list of omics data each with n subjects. |

`Y` |
A vector of binary variable, user needs to set the level of this variable to 0 and 1. |

`Between_Discriminate_Ratio` |
A vector with length 2 specifying the relative importance of between-omics relationship and omics-phenotype relationship. For instance a ratio of 1:1 (c(1,1) in the argument) means between-omics relationship and omics-phenotype relationship contribute equally to the canonical weights extraction. |

`SubsamplingPercent` |
A vector with length equal to the number of omics data ( |

`CCcoef` |
A vector of scaling factors only for between-omics relationship (exclude omics-phenotype). This
coefficient vector follows the column order of |

`LambdaBetween` |
A vector of sparsity penalty value for each omics data to run the between-omics SmCCA, each penalty term should be within the range of 0 and 1. |

`LambdaPheno` |
A penalty term when running the sparse PLS with phenotype, penalty term should be within the range of 0 and 1. |

`SubsamplingNum` |
Number of feature subsamples. Default is 1000. Larger number leads to more accurate results, but at a higher computational cost, default is set to 1000. |

`ncomp_pls` |
Number of latent components for PLS, default set to 3. |

`EvalClassifier` |
If |

`testData` |
A list of testing omics data matrix, should have the exact same order as data list X, only used when EvalClassifier is set to |

If `EvalClassifier`

is set to `FALSE`

, a canonical correlation weight matrix is returned with combined omics data. Each
column is the canonical correlation weights based on subsampled X features. The number of columns is `SubsamplingNum`

. If `EvalClassifier`

is set to `TRUE`

, then latent factors from training and testing data will be returned for classifier evaluation.

```
## For illustration, we only subsample 5 times.
set.seed(123)
X1 <- matrix(rnorm(600,0,1), nrow = 60)
X2 <- matrix(rnorm(600,0,1), nrow = 60)
Y_binary <- rbinom(60,1,0.5)
Ws <- getRobustWeightsMultiBinary(list(X1,X2), Y_binary,
SubsamplingPercent = c(0.8,0.8), CCcoef = NULL,
LambdaBetween = c(0.5,0.5), LambdaPheno = 0.1, SubsamplingNum = 10)
```

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.