Class that represents a netprioR model.

1 2 3 4 5 6 7 8 | ```
netprioR(networks, phenotypes, labels, ...)
## S4 method for signature 'list,matrix,factor'
netprioR(networks, phenotypes, labels,
fit.model = FALSE, a = 0.1, b = 0.1, sigma2 = 0.1, tau2 = 100,
eps = 1e-10, max.iter = 500, thresh = 1e-06, use.cg = FALSE,
thresh.cg = 1e-06, nrestarts = 5, max.cores = detectCores(),
verbose = TRUE, ...)
``` |

`networks` |
List of NxN adjacency matrices of gene-gene similarities |

`phenotypes` |
Matrix of dimension NxP containing covariates |

`labels` |
Vector of Nx1 labels for all genes (NA if no label available) |

`...` |
Additional arguments |

`fit.model` |
Indicator whether to fit the model |

`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 |

A `netprioR`

object

`networks`

List of NxN adjacency matrices of gene-gene similarities

`phenotypes`

Matrix of dimension NxP containing covariates

`labels`

Vector of Nx1 labels for all genes. NA if no label available.

`is.fitted`

Flag indicating if model is fitted

`model`

List containing estimated parameters and imputed missing data

Fabian Schmich

1 2 3 4 5 6 7 | ```
# runs long-ish
data(simulation)
np <- netprioR(networks = simulation$networks,
phenotypes = simulation$phenotypes,
labels = simulation$labels.obs,
fit.model = TRUE)
summary(np)
``` |

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

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