eigenmodel_mcmc: Approximate the posterior distribution of parameters in an...

In pdhoff/eigenmodel: Semiparametric Factor and Regression Models for Symmetric Relational Data

Description

Construct approximate samples from the posterior distribution of the parameters and latent variables in an eigenmodel for symmetric relational data.

Usage

eigenmodel_mcmc(Y, X = NULL, R = 2, S = 1000, seed = 1, Nss = min(S,
  1000), burn = 100)

Arguments

Y	an n x n symmetric matrix with missing diagonal entries. Off-diagonal missing values are allowed.
X	an n x n x p array of regressors
R	the rank of the approximating factor matrix
S	number of samples from the Markov chain
seed	a random seed
Nss	number of samples to be saved
burn	number of initial scans of the Markov chain to be dropped

Value

a list with the following components:

Z_postmean	posterior mean of the latent variable in the probit specification
ULU_postmean	posterior mean of the reduced-rank approximating matrix
Y_postmean	the original data matrix with missing values replaced by posterior means
L_postsamp	samples of the eigenvalues
b_postsamp	samples of the regression coefficients
Y	original data matrix
X	original regressor array
S	number of scans of the Markov chain

Author(s)

Peter Hoff

Examples

data(YX_Friend)

fit<-eigenmodel_mcmc(Y=YX_Friend$Y,X=YX_Friend$X,R=2,S=50,burn=50)

# in general you  should run the Markov chain longer than 50 scans

plot(fit)

#fit<-eigenmodel_mcmc(Y=Y_Gen,R=3,S=10000)

#fit<-eigenmodel_mcmc(Y=Y_Pro,R=3,S=10000)

pdhoff/eigenmodel documentation built on May 13, 2019, 12:19 p.m.