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

 ```1 2``` ```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

Peter Hoff

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```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) ```

eigenmodel documentation built on May 28, 2019, 5:04 p.m.