lsm: Function to run MCMC sampler in the LSM model for a static...
In SAcmu/LLSM: Package to Fit Longitudinal Latent Space Model

lsm runs MCMC sampler in the LSM model for a static network.

1 2	lsm(Y, Y1 = NULL, initialVals = NULL, priors = NULL, tune = NULL, tuneIn = TRUE, dd, niter)

`Y`	A sociomatrix for observed network
`initialVals`	A list of values for initializing the chain for `intercept` and `ZZ`. Default is set to NULL, when random initialization is used.
`priors`	A list of parameters for prior distribution specified as `MuBeta`, `VarBeta`, `VarZ`, `A` and `B` If set to NULL, default priors is used
`tune`	A list of tuning parameters. If set to NULL, default values are used.
`tuneIn`	Logical option to specify whether to auto tune the chain or not. Default is `TRUE`
`dd`	Dimension of the latent space
`niter`	Number of MCMC iterations to run

lsm runs MCMC sampler for the LSM model of Hoff(2001) and returns samples from the posteriors chains of the parameters, the posterior likelihood at the accpeted parameters, a list of acceptance rates from the metropolis hastings sampling, and a list of the tuning values if tuneIn is set to TRUE

SAcmu/LLSM documentation built on May 9, 2019, 11:06 a.m.