\name{HLSMrandomEF}
\alias{HLSMrandomEF}
\alias{HLSMfixedEF}
\alias{print.HLSM}
\alias{print.summary.HLSM}
\alias{summary.HLSM}
\alias{getIntercept}
\alias{getAlpha}
\alias{getLS}
\alias{getLikelihood}
\alias{getBeta}

\title{Function to run the MCMC sampler in random effect model (and HLSMfixedEF for fixed effect model)
}

\description{
    Function to run the MCMC sampler to draw from the posterior distribution of intercept, slopes, latent positions, and intervention effect (if applicable). HLSMrandomEF( ) fits randome effect model; HLSMfixedEF( ) fits fixed effect model. 
}

\usage{

HLSMrandomEF(Y,edgeCov=NULL, receiverCov = NULL, senderCov = NULL, 
	FullX = NULL,initialVals = NULL, priors = NULL, tune = NULL,
	tuneIn = TRUE,TT = NULL,dd, niter,intervention)

HLSMfixedEF(Y,edgeCov=NULL, receiverCov = NULL, senderCov = NULL,
	FullX = NULL, initialVals = NULL, priors = NULL, tune = NULL,
        tuneIn = TRUE, TT = NULL,dd, niter,intervention)

getBeta(object, burnin = 0, thin = 1)
getIntercept(object, burnin = 0, thin = 1)
getAlpha(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 socio-matrix for \code{K} different networks
		ii. list of data frame with columns \code{Sender}, \code{Receiver} and \code{Outcome} for \code{K} different networks
		iii. a dataframe with columns \code{id} to identify network, \code{Receiver}, \code{Sender} and \code{Outcome} for receiver nodes, sender nodes and the edge outcome respectively       
}

	\item{edgeCov}{a data frame to specify edge level covariates with a column for network id named \code{id}, a column for sender node named \code{Sender}, a column for receiver nodes named \code{Receiver} and columns for values of each edge level covariates.
}

	\item{receiverCov}{a data frame to specify nodal covariates as edge receivers, with a column for network id, named \code{id}, a column \code{Node} for node names, and the rest for respective node level covariates.
}

	\item{senderCov}{a data frame to specify nodal covariates as edge senders, with a column for network id, named \code{id}, a column \code{Node} for node names, and 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.

	\code{alpha} is a numeric variable and is 0 for no-intervention model.	
}

    \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,MuAlpha,VarAlpha,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{MuAlpha} is a numeric variable specifying the mean of prior distribution of intervention effect. Default is 0.

    \code{VarAlpha} is a numeric variable for the variance of the prior distribution of intervention effect. Default is 100.

    \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(tuneAlpha, tuneBeta, tuneInt,tuneZ)}.

	\code{tuneAlpha}, \code{tuneBeta} and \code{tuneInt} have the same structure as \code{beta}, \code{alpha} 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{TT}{
     a vector of binaries to indicate treatmeant and control networks. If there is no intervention effect, TT = \code{NULL} (default).
}

    \item{dd}{
    dimension of latent space.
}

    \item{niter}{
    number of iterations for the MCMC chain.
}
    \item{intervention}{
    binary variable indicating whether the posterior distribution of the intervention effect is to be estimated.
}

    \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 
}

    \item{thin}{
	numeric value by which the chain is to be thinned while extracting results from the 'HLSM' object 
} 
}


\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
}	

}

\author{
    Sam Adhikari
}

\references{Tracy M. Sweet, Andrew C. Thomas and Brian W. Junker (2012), "Hierarchical Network Models for Education Research: Hierarchical Latent Space Models", Journal of Educational and Behavorial Statistics.
}


\examples{

library(HLSM)

#Set up the parameters of the function
priors = NULL
tune = NULL
initialVals = NULL
niter = 10

#Random effect HLSM on Pitt and Spillane data
random.fit = HLSMrandomEF(Y = ps.advice.mat,FullX = ps.edge.vars.mat,
	initialVals = initialVals,priors = priors,
	tune = tune,tuneIn = FALSE,dd = 2,niter = niter,
	intervention = 0)


summary(random.fit)
names(random.fit)

#extract results without burning and thinning
getBeta(random.fit)
getIntercept(random.fit)
getLS(random.fit)
getLikelihood(random.fit)

##Same can be done for fixed effect model

#Fixed effect HLSM on Pitt and Spillane data 

fixed.fit = HLSMfixedEF(Y = ps.advice.mat,FullX = ps.edge.vars.mat,
	initialVals = initialVals,priors = priors,
	tune = tune,tuneIn = FALSE,dd = 2,niter = niter,
	intervention = 0)

summary(fixed.fit)
names(fixed.fit)

}