Description Usage Arguments Details Value See Also Examples

Model types: SB (SingleBatchModel): hierarchical model with mixture component-specific means and variances; MB (MultiBatchModel): hierarchical model with mixture component- and batch-specific means and variances; SBP (SingleBatchPooled): similar to SB model but with a pooled MBP (MultiBatchPooled): similar to MB model but with a pooled estimate of the variance (across mixture components) for each batch.

1 2 3 |

`model` |
a character vector indicating which models to fit (any combination of 'SB', 'MB', 'SBP', and 'MBP') |

`dat` |
numeric vector of the summary copy number data for each sample at a single CNP (e.g., the median log R ratio for each sample) |

`mp` |
an object of class |

`hp.list` |
a list of hyperparameters for each of the different models. If missing, this list will be generated automatically with default hyperparameters that work well for copy number data |

`batches` |
an integer vector of the same length as |

`k_range` |
a length-two numeric vector providing the minimum and maximum number of components to model. For example, c(1, 3) will fit mixture models with 1, 2, and 3 components. |

`max_burnin` |
the maximum number of burnin iterations. See details. |

`min_GR` |
length-1 numeric vector specifying the maximum Gelman-Rubin (GR) statistic. If the GR statistic is above this value, the marginal likelihood will not be estimated. |

`top` |
a length-one numeric vector indicating how many of the top models to return. |

`df` |
length-1 numeric vector for t-distribution degrees of freedom |

`min_effsize` |
length-1 numeric vector specifying the minimum effective size of the MCMC simulations. If below this value, the marginal likelihood will not be estimated. |

For each model specified, a Gibbs sampler will be initiated

The number of mixture models fit depends on `k_range`

and

A list of models of length `top`

sorted by decreasing

`gelman.diag`

`effectiveSize`

`marginalLikelihood`

See `ggChains`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ```
set.seed(100)
nbatch <- 3
k <- 3
means <- matrix(c(-2.1, -2, -1.95, -0.41, -0.4, -0.395, -0.1,
0, 0.05), nbatch, k, byrow = FALSE)
sds <- matrix(0.15, nbatch, k)
sds[, 1] <- 0.3
N <- 1000
truth <- simulateBatchData(N = N, batch = rep(letters[1:3],
length.out = N),
p = c(1/10, 1/5, 1 - 0.1 - 0.2),
theta = means,
sds = sds)
## clearly not enough simulations
mp <- McmcParams(iter=10, burnin=5)
## this should be much higher > 1,000
max_burnin <- 10
## suppress the warnings from the short chains
suppressWarnings(gibbs(model=c("SB", "SBP", "MB", "MBP"),
dat=y(truth),
mp=mp,
batches=batch(truth),
k_range=c(1, 4),
max_burnin=max_burnin,
min_GR=1.2,
top=2,
df=100))
``` |

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