View source: R/tuneCluster.spls.R

tuneCluster.spls | R Documentation |

This function identify the number of feautures to keep per component and thus by cluster in `mixOmics::spls`

by optimizing the silhouette coefficient, which assesses the quality of clustering.

```
tuneCluster.spls(
X,
Y,
ncomp = 2,
test.keepX = rep(ncol(X), ncomp),
test.keepY = rep(ncol(Y), ncomp),
...
)
```

`X` |
numeric matrix (or data.frame) with features in columns and samples in rows |

`Y` |
numeric matrix (or data.frame) with features in columns and samples in rows (same rows as |

`ncomp` |
integer, number of component to include in the model |

`test.keepX` |
vector of integer containing the different value of keepX to test for block |

`test.keepY` |
vector of integer containing the different value of keepY to test for block |

`...` |
other parameters to be included in the spls model (see |

For each component and for each keepX/keepY value, a spls is done from these parameters. Then the clustering is performed and the silhouette coefficient is calculated for this clustering.

We then calculate "slopes" where keepX/keepY are the coordinates and the silhouette is the intensity. A z-score is assigned to each slope. We then identify the most significant slope which indicates a drop in the silhouette coefficient and thus a deterioration of the clustering.

`silhouette` |
silhouette coef. computed for every combinasion of keepX/keepY |

`ncomp` |
number of component included in the model |

`test.keepX` |
list of tested keepX |

`test.keepY` |
list of tested keepY |

`block` |
names of blocks |

`slopes` |
"slopes" computed from the silhouette coef. for each keepX and keepY, used to determine the best keepX and keepY |

`choice.keepX` |
best |

`choice.keepY` |
best |

`spls`

, `getCluster`

, `plotLong`

```
demo <- suppressWarnings(get_demo_cluster())
X <- demo$X
Y <- demo$Y
# tuning
tune.spls <- tuneCluster.spls(X, Y, ncomp= 2, test.keepX= c(5,10,15,20), test.keepY= c(2,4,6))
keepX <- tune.spls$choice.keepX
keepY <- tune.spls$choice.keepY
# final model
spls.res <- mixOmics::spls(X, Y, ncomp= 2, keepX= keepX, keepY= keepY)
# get clusters and plot longitudinal profile by cluster
spls.cluster <- getCluster(spls.res)
plotLong(spls.res)
```

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