R/aplsm.R

In selenashuowang/jlsm: Joint Latent Space Model for Social Networks with Multivariate Attributes

#' @title The Attribute Person Latent Space model
#'
#' @description Jointly model social network with multivariate attributes
#'
#' @param Niter number of iterations
#' @param Y.i N by N matrix containing the binary social network
#' @param Y.ia N by M matrix containing the binary multivariate attributes
#' @param D number of dimensions in the data
#' @param type character indicating the types of model. It could be "DD", distance by distance model, "DV", distance by vector model,
#' "VV", vector by vector model
#'
#' @return list containing:
#'  \itemize{
#'  \item \code{lsmhEZ.i} (\code{N} x \code{D}) matrix containing the posterior means of the latent person positions
#'  \item \code{lsmhEZ.a} (\code{M} x \code{D}) matrix containing the posterior means of the latent item positions
#'  \item \code{lsmhVZ.0} (\code{D} x \code{D}) matrix containing the posterior variance of the latent person positions
#'  \item \code{lsmhVZ.1} (\code{D} x \code{D}) matrix containing the posterior variance of the latent item positions
#'  \item \code{lsmhAlpha.0} scaler of mean of the posterior distributions of \eqn{\alpha.0}
#'  \item \code{lsmhAlpha.1} scaler of mean of the posterior distributions of \eqn{\alpha.1}
#'  \item \code{lsmhKL} expected log-likelihood
#'  }
#'
#' @export
#' @examples
#' attach(french)
#' a=aplsm(Niter=5,Y.i, Y.ia, D=2, type="DD")


aplsm<-function(Niter,Y.i, Y.ia,D, type){
  M=ncol(Y.ia)
  N=ncol(Y.i)



    Z.a<-cmdscale(dist(t(Y.ia)),eig=TRUE, k=D)$points

    Z.i <- matrix(rnorm(N * D), ncol = D)

    #Z.i <- lsm(Y.i,D)$lsmEZ



    Zs=list("Z.i" = Z.i,"Z.a" = Z.a)
    if(type == "DD"){
      lsmhMat<-DDlsmh(Niter,Y.i,Y.ia,M,N,D,Zs)
    }

    if(type == "DV"){
      lsmhMat<-DVlsmh(Niter,Y.i,Y.ia,M,N,D,Zs)
    }
    if(type == "VV"){
      lsmhMat<-VVlsmh(Niter,Y.i,Y.ia,M,N,D,Zs)
    }


  return(lsmhMat)


}