Description Usage Arguments Details Value Author(s)

Usage of CID Gibbs Samplers.

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 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | ```
CID.Gibbs (input,
outcome,
node.names,
components,
class.outcome = NULL,
fill.in.missing.edges = missing(outcome),
new.chain = FALSE,
draws = 100,
burnin = -1,
thin = 10,
report = 100,
auto.converge = FALSE,
extend.max=10,
extend.count=100,
verbose=2,
...)
## S3 method for class 'CID.Gibbs'
print(x, ...)
## S3 method for class 'CID.Gibbs'
summary(object, ...)
## S3 method for class 'CID.Gibbs'
plot(x, ...)
## S3 method for class 'summary.CID.Gibbs'
print(x, ...)
likelihood.plot(x, ...)
intercept.plot(x, mode = c("standard","trace"), ...)
COV.plot(x, mode = c("standard","trace","scatterplot"), ...)
LSM.plot(x, ...)
SBM.plot(x, ...)
MMSBM.plot(x, ...)
SR.plot(x, ...)
network.plot (x, fitted.values=FALSE, ...)
sociogram.plot (x, component.color=0, vertexcolor, add.labels = TRUE, ...)
n.nodes(object)
edge.list(object)
is.net.directed(object)
net.density(object)
outcome(object)
node.names(object)
inDegree(object)
outDegree(object)
socio(object)
value.mat(CID.Gibbs.object, prob = TRUE)
value.mat.mean(object, prob = TRUE)
switcheroo(CID.Gibbs.object)
``` |

`input` |
An object containing information about the edges in a network. Must be one of the following classes: Matrix, CIDnetwork, or CID.Gibbs. If input is a square matrix, it is assumed to be a sociomatrix. Otherwise a matrix with 2 columns and a number of rows equal to the number of edges is required. Providing a CIDnetwork will use the associated edge.list. Providing a CID.Gibbs object will continue the MCMC chain from the last draw. |

`outcome` |
If an edgelist is provided as input and outcome is missing, the edges provided are assumed to be the ones in a binary sociomatrix. Otherwise, an outcome value must be specified for each edge in the edgelist, and any edges not provided are assumed to have no data. |

`node.names` |
Names labeling each node in the network. |

`CID.Gibbs.object` |
If desired, an existing CID.Gibbs output object can be loaded instead of a new network specification. |

`components` |
A list of sub-components, including (COV, HBM, LSM, LVM, MMSBM, SBM, SR). |

`class.outcome` |
One of "ordinal" (default, values from 0 to higher integers), "binary" (ordinal in 0 and 1) or "gaussian" (unbounded continuous values). Class is auto-detected if NULL remains in place. |

`fill.in.missing.edges` |
If TRUE, the edge list will be augmented with zeroes for all unspecified but possible edges. By default, if an outcome is specified, these edges will not be added. |

`new.chain` |
If a CID.Gibbs object is provided, the default value of FALSE will return both the old and new MCMC chain combined. A value of TRUE will drop the old chain completely. |

`draws` |
Number of draws to return. |

`burnin` |
Number of draws to burnin. A negative value will automatically determine burnin amount. |

`thin` |
Amount of draws to thin the chain by. |

`report` |
Number of draws between reporting total draws so far. |

`auto.converge` |
When true, a Geweke convergence test on log-likleihood to detect convergence. |

`extend.max` |
Maximum number of times chain will be extended until it returns without converging. |

`extend.count` |
Number of draws to extend chain by if convergence test fails |

`verbose` |
Level of output to be displayed while running. A value of 0 will return little or no output. A value of 1 will only notify of warnings of misuse. A value of 2 will report progress of MCMC chain. A value of 3 or higher will report debugging values. |

`...` |
Further arguments to be passed to the Gibbs sampler routine or the plot routine. See details for more. |

`x, object` |
An object outputted from CID.Gibbs. |

`mode` |
Controls which diagnostic plot is made. |

`fitted.values` |
If TRUE, plots the fitted tie strength under the Gibbs sampler. If FALSE, plots the network outcomes as entered. |

`component.color` |
If non-zero, colors the nodes in the sociogram according to the output of the Gibbs sampler. |

`vertexcolor` |
User-passed vertex colors for sociogram.plot . |

`add.labels` |
When true, node labels are included on nodes. |

`trace` |
If selected, displays the Gibbs sampler trace plot for the intercept rather than a point and interval. |

`prob` |
In value.mat, converts the linear predictor value to the probability of a binary edge. |

This is the main routine for running a Gibbs sampler on any of the CID models. See the vignettes for more information.

CID.Gibbs outputs a list containing a CID object, the results of the Gibbs sampler, and the Deviance Information Criterion estimate for the Gibbs.

A.C. Thomas <act@acthomas.ca>

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