\name{HLSMrandomEF}
\alias{HLSMrandomEF}
\alias{HLSMfixedEF}
\alias{LSM}
\alias{print.HLSM}
\alias{print.summary.HLSM}
\alias{summary.HLSM}
\alias{getIntercept}
\alias{getLS}
\alias{getLikelihood}
\alias{getBeta}
\title{Function to run the MCMC sampler in random effects latent space model, HLSMfixedEF for fixed effects model, or LSM for single network latent space model)
}
\description{
Function to run the MCMC sampler to draw from the posterior distribution of intercept, slopes, and latent positions. HLSMrandomEF( ) fits random effects model; HLSMfixedEF( ) fits fixed effects model; LSM( ) fits single network model.
}
\usage{
HLSMrandomEF(Y,edgeCov=NULL, receiverCov = NULL, senderCov = NULL,
FullX = NULL,initialVals = NULL, priors = NULL, tune = NULL,
tuneIn = TRUE,dd=2, niter)
HLSMfixedEF(Y,edgeCov=NULL, receiverCov = NULL, senderCov = NULL,
FullX = NULL, initialVals = NULL, priors = NULL, tune = NULL,
tuneIn = TRUE,dd=2, niter)
LSM(Y,edgeCov=NULL, receiverCov = NULL, senderCov = NULL,
FullX = NULL,initialVals = NULL, priors = NULL, tune = NULL,
tuneIn = TRUE,dd=2, estimate.intercept=FALSE, niter)
getBeta(object, burnin = 0, thin = 1)
getIntercept(object, burnin = 0, thin = 1)
getLS(object, burnin = 0, thin = 1)
getLikelihood(object, burnin = 0, thin = 1)
}
\arguments{
\item{Y}{
input outcome for different networks. Y can either be
(i). list of sociomatrices for \code{K} different networks (Y[[i]] must be a matrix with named rows and columns)
(ii). list of data frame with columns \code{Sender}, \code{Receiver} and \code{Outcome} for \code{K} different networks
(iii). a dataframe with columns named as follows: \code{id} to identify network, \code{Receiver} for receiver nodes, \code{Sender} for sender nodes and finally, \code{Outcome} for the edge outcome.
}
\item{edgeCov}{
data frame to specify edge level covariates with
(i). a column for network id named \code{id},
(ii). a column for sender node named \code{Sender},
(iii). a column for receiver nodes named \code{Receiver}, and
(iv). columns for values of each edge level covariates.
}
\item{receiverCov}{
a data frame to specify nodal covariates as edge receivers with
(i.) a column for network id named \code{id},
(ii.) a column \code{Node} for node names, and
(iii). the rest for respective node level covariates.
}
\item{senderCov}{
a data frame to specify nodal covariates as edge senders with
(i). a column for network id named \code{id},
(ii). a column \code{Node} for node names, and
(iii). the rest for respective node level covariates.
}
\item{FullX}{
list of numeric arrays of dimension \code{n} by \code{n} by \code{p} of covariates for K different networks. When FullX is provided to the function, edgeCov, receiverCov and senderCov must be specified as NULL.
}
\item{initialVals}{
an optional list of values to initialize the chain. If \code{NULL} default initialization is used, else
\code{initialVals = list(ZZ, beta, intercept, alpha)}.
For fixed effect model \code{beta} is a vector of length \code{p} and \code{intercept} is a vector of length 1.
For random effect model \code{beta} is an array of dimension \code{K} by \code{p}, and \code{intercept} is a vector of length \code{K}, where \code{p} is the number of covariates and \code{K} is the number of network.
\code{ZZ} is an array of dimension \code{NN} by \code{dd}, where \code{NN} is the sum of nodes in all \code{K} networks.
}
\item{priors}{
an optional list to specify the hyper-parameters for the prior distribution of the paramters.
If priors = \code{NULL}, default value is used. Else,
\code{priors=}
\code{list(MuBeta,VarBeta,MuZ,VarZ,PriorA,PriorB)}
\code{MuBeta} is a numeric vector of length PP + 1 specifying the mean of prior distribution for coefficients and intercept
\code{VarBeta} is a numeric vector for the variance of the prior distribution of coefficients and intercept. Its length is same as that of MuBeta.
\code{MuZ} is a numeric vector of length same as the dimension of the latent space, specifying the prior mean of the latent positions.
\code{VarZ} is a numeric vector of length same as the dimension of the latent space, specifying diagonal of the variance covariance matrix of the prior of latent positions.
\code{PriorA, PriorB} is a numeric variable to indicate the rate and scale parameters for the inverse gamma prior distribution of the hyper parameter of variance of slope and intercept
}
\item{tune}{
an optional list of tuning parameters for tuning the chain. If tune = \code{NULL}, default tuning is done. Else,
\code{tune = list(tuneBeta, tuneInt,tuneZ)}.
\code{tuneBeta} and \code{tuneInt} have the same structure as \code{beta} and \code{intercept} in \code{initialVals}.
\code{ZZ} is a vector of length \code{NN}.
}
\item{tuneIn}{
a logical to indicate whether tuning is needed in the MCMC sampling. Default is \code{FALSE}.
}
\item{dd}{
dimension of latent space.
}
\item{estimate.intercept}{
When TRUE, the intercept will be estimated. If the variance of the latent positions are of interest, intercept=FALSE will allow users to obtain a unique variance. The intercept can also be inputed by the user.
}
\item{niter}{
number of iterations for the MCMC chain.
}
\item{object}{
object of class 'HLSM' returned by \code{HLSM()} or \code{HLSMfixedEF()}
}
\item{burnin}{
numeric value to burn the chain while extracting results from the 'HLSM'object. Default is \code{burnin = 0}.
}
\item{thin}{
numeric value by which the chain is to be thinned while extracting results from the 'HLSM' object. Default is \code{thin = 1}.
}
}
\value{
Returns an object of class "HLSM". It is a list with following components:
\item{draws}{
list of posterior draws for each parameters.
}
\item{acc}{
list of acceptance rates of the parameters.
}
\item{call}{
the matched call.
}
\item{tune}{
final tuning values
}
}
\details{
The \code{HLSMfixedEF} and \code{HLSMrandomEF} functions will not automatically assess thinning and burn-in. To ensure appropriate inference, see \code{HLSMdiag}.
See also \code{LSM} for fitting network data from a single network.
}
\author{
Sam Adhikari & Tracy Sweet
}
\references{Tracy M. Sweet, Andrew C. Thomas and Brian W. Junker (2013), "Hierarchical Network Models for Education Research: Hierarchical Latent Space Models", Journal of Educational and Behavorial Statistics.
}
\examples{
library(HLSM)
#Set values for the inputs of the function
priors = NULL
tune = NULL
initialVals = NULL
niter = 10
#Fixed effect HLSM on Pitt and Spillane data
fixed.fit = HLSMfixedEF(Y = ps.advice.mat, senderCov=ps.node.df,
initialVals = initialVals,priors = priors,
tune = tune,tuneIn = FALSE,dd = 2,niter = niter)
summary(fixed.fit)
lsm.fit = LSM(Y=School9Network,edgeCov=School9EdgeCov,
senderCov=School9NodeCov, receiverCov=School9NodeCov, niter = niter)
names(lsm.fit)
}
