Description Usage Arguments Details Value Author(s) Examples

View source: R/assign.summary.R

The assign.summary function computes the posterior mean of the model parameters estimated in every iteration during the Gibbs sampling.

1 2 | ```
assign.summary(test, burn_in = 1000, iter = 2000, adaptive_B = TRUE,
adaptive_S = FALSE, mixture_beta = TRUE)
``` |

`test` |
The list object returned from the assign.mcmc function. The list components are the MCMC chains of the B, S, Delta, beta, gamma, and sigma. |

`burn_in` |
The number of burn-in iterations. These iterations are discarded when computing the posterior means of the model parameters. The default is 1000. |

`iter` |
The number of total iterations. The default is 2000. |

`adaptive_B` |
Logicals. If TRUE, the model adapts the baseline/background (B) of genomic measures for the test samples. The default is TRUE. |

`adaptive_S` |
Logicals. If TRUE, the model adapts the signatures (S) of genomic measures for the test samples. The default is FALSE. |

`mixture_beta` |
Logicals. If TRUE, elements of the pathway activation matrix are modeled by a spike-and-slab mixture distribution. The default is TRUE. |

The assign.summary function is suggested to run after the assign.convergence function, which is used to check the convergence of the MCMC chain. If the MCMC chain does not converge to a stationary phase, more iterations are required in the assign.mcmc function. The number of burn-in iterations is usually set to be half of the number of total iterations, meaning that the first half of the MCMC chain is discarded when computing the posterior means.

`beta_pos` |
The N x K matrix of the posterior mean of the pathway activation level in test samples (transposed matrix A). Columns:K pathways; rows: N test samples |

`sigma_pos` |
The G x 1 vector of the posterior mean of the variance of gene. |

`kappa_pos` |
The N x K matrix of posterior mean of pathway activation level in test samples (transposed matrix A) (adjusted beta_pos scaling between 0 and 1). Columns:K pathways; rows: N test samples |

`gamma_pos` |
The N x K matrix of the posterior probability of pathways being activated in test samples. |

`S_pos` |
The G x K matrix of the posterior mean of pathway signature genes. |

`Delta_pos` |
The G x K matrix of the posterior probability of genes being significant in the associated pathways. |

Ying Shen

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
data(trainingData1)
data(testData1)
data(geneList1)
trainingLabel1 <- list(control = list(bcat=1:10, e2f3=1:10, myc=1:10,
ras=1:10, src=1:10),
bcat = 11:19, e2f3 = 20:28, myc= 29:38,
ras = 39:48, src = 49:55)
processed.data <- assign.preprocess(trainingData=trainingData1,
testData=testData1, trainingLabel=trainingLabel1, geneList=geneList1)
mcmc.chain <- assign.mcmc(Y=processed.data$testData_sub,
Bg = processed.data$B_vector,
X=processed.data$S_matrix,
Delta_prior_p = processed.data$Pi_matrix,
iter = 20, adaptive_B=TRUE, adaptive_S=FALSE,
mixture_beta=TRUE)
mcmc.pos.mean <- assign.summary(test=mcmc.chain, burn_in=10, iter=20,
adaptive_B=TRUE, adaptive_S=FALSE,
mixture_beta = TRUE)
``` |

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