Infer parameters and hidden data using the EM algorithm of netprioR

1 2 3 4 | ```
learn(Yobs, X, G, l, u, a = 0.1, b = 0.1, sigma2 = 1, tau2 = 10,
eps = 1e-11, max.iter = 500, thresh = 0.001, use.cg = TRUE,
thresh.cg = 1e-05, nrestarts = 5, max.cores = detectCores(),
verbose = FALSE)
``` |

`Yobs` |
Observed labels (NA, if not observed) |

`X` |
Phenotypes |

`G` |
Graph Laplacians |

`l` |
Indices of labelled instances |

`u` |
Indices of unlabelled instances |

`a` |
Shape parameter of Gamma prior for W |

`b` |
Scale parameter of Gamma prior for W |

`sigma2` |
Cariance for Gaussian labels |

`tau2` |
Variance for Gaussian prior for beta |

`eps` |
Small value added to diagonal of Q in order to make it non-singular |

`max.iter` |
Maximum number of iterations for EM |

`thresh` |
Threshold for termination of EM with respect to change in parameters |

`use.cg` |
Flag whether to use conjugate gradient instead of exact computation of expectations |

`thresh.cg` |
Threshold for the termination of the conjugate gradient solver |

`nrestarts` |
Number of restarts for EM |

`max.cores` |
Maximum number of cores to use for parallel computation |

`verbose` |
Print verbose output |

List containing: Predicted labels Yhat and inferred parameters W and beta

Fabian Schmich

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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