Encoding: UTF-8
Type: Package
Title: Hierarchical Latent Space Network Model
Version: 0.9.2
Date: 2025-06-04
Description: Fits latent space models for single networks and hierarchical latent space models for ensembles of networks as described in Sweet, Thomas & Junker (2013).
Depends: R (≥ 4.4.0)
ByteCompile: TRUE
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Imports: MASS, coda, igraph, grDevices, graphics, methods, abind, stats
LazyData: yes
NeedsCompilation: yes
Packaged: 2025-06-04 14:48:27 UTC; tsweet
RoxygenNote: 7.3.2
Author: Samrachana Adhikari [aut], Tracy Sweet [aut, cre]
Maintainer: Tracy Sweet <tsweet@umd.edu>
Repository: CRAN
Date/Publication: 2025-06-04 15:30:09 UTC

Function plot posterior summaries (boxplots) for regression coefficients

Description

Function plot posterior summaries (boxplots) for regression coefficients

Usage


HLSMcovplots(fitted.model, burnin=0, thin=1, verbose=TRUE)

Arguments

fitted.model

Model fit using HLSM fitting function; should be a HLSM or LSM object

burnin

Amount of burnin to remove

thin

Amount to thin each chain

verbose

logical to indicate whether message about order of covariates is included with plots

Details

The plots show posterior means and 50 and 95 percent equal-tailed credible intervals.

Value

No return value, makes a plot in plotting window

Author(s)

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.

Function to conduct diagnostics the MCMC chain from a random effect HLSM (and HLSMfixedEF for fixed effects model)

Description

Function to compute and report diagnostic plots and statistics for a single or multiple HLSM objects.

Usage


HLSMdiag(object, burnin = 0,
         diags = c('psrf', 'raftery', 'traceplot', 'autocorr'),
         col = 1:6, lty = 1)

Arguments

object

object or list of objects of class 'HLSM' returned by HLSMrandomEf() or HLSMfixedEF()

burnin

numeric value to burn the chain while extracting results from the 'HLSM'object. Default is burnin = 0.

diags

a character vector that is a subset of c('psrf', 'raftery', 'traceplot', 'autocorr'). Default returns all diagnostics. If only a single chain is supplied in object, 'psrf' throws a warning if explicitly requested by user.

col

a character or integer vector specifying the colors for the traceplot and autocorr plot

lty

a character or integer vector specifying the linetype for the traceplot and autocorr plot

Value

Returns an object of class "HLSMdiag". It is a list that contains variable-level diagnostic tables from either or both of the raftery diagnostic and psrf diagnostic.

call

the matched call.

raftery

list of matrices of suggested niters, burnin, and thinning for each chain.

psrf

list containing psrf, a matrix of psrf estimates and upper limits for variable, and mpsrf the multivariate psrf estimate.

Author(s)

Christian Meyer

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, verbose=TRUE)

HLSMfixedEF(Y,edgeCov=NULL, receiverCov = NULL, senderCov = NULL,
        FullX = NULL, initialVals = NULL, priors = NULL, tune = NULL,
        tuneIn = TRUE,dd=2, estimate.intercept=FALSE, niter, verbose=TRUE)
        
LSM(Y,edgeCov=NULL, receiverCov = NULL, senderCov = NULL, 
        FullX = NULL,initialVals = NULL, priors = NULL, tune = NULL,
        tuneIn = TRUE,dd=2, estimate.intercept=FALSE, niter, verbose=TRUE)

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

Y

input outcome for different networks. Y can either be

(i). list of sociomatrices for K different networks (Y[[i]] must be a matrix with named rows and columns)

(ii). list of data frame with columns Sender, Receiver and Outcome for K different networks

(iii). a dataframe with columns named as follows: id to identify network, Receiver for receiver nodes, Sender for sender nodes and finally, Outcome for the edge outcome. Note that for LSM, Y must be an adjacency matrix.

edgeCov

data frame to specify edge level covariates with

(i). a column for network id named id,

(ii). a column for sender node named Sender,

(iii). a column for receiver nodes named Receiver, and

(iv). columns for values of each edge level covariates.

receiverCov

a data frame to specify nodal covariates as edge receivers with

(i.) a column for network id named id,

(ii.) a column Node for node names, and

(iii). the rest for respective node level covariates.

senderCov

a data frame to specify nodal covariates as edge senders with

(i). a column for network id named id,

(ii). a column Node for node names, and

(iii). the rest for respective node level covariates.

FullX

list of numeric arrays of dimension n by n by p of covariates for K different networks. When FullX is provided to the function, edgeCov, receiverCov and senderCov must be specified as NULL.

initialVals

an optional list of values to initialize the chain. If NULL default initialization is used, else initialVals = list(ZZ, beta, intercept, alpha).

For fixed effect model beta is a vector of length p and intercept is a vector of length 1.

For random effect model beta is an array of dimension K by p, and intercept is a vector of length K, where p is the number of covariates and K is the number of network.

ZZ is an array of dimension NN by dd, where NN is the sum of nodes in all K networks.

priors

an optional list to specify the hyper-parameters for the prior distribution of the paramters. If priors = NULL, default value is used. Else,

priors=

list(MuBeta,VarBeta,MuZ,VarZ,PriorA,PriorB)

MuBeta is a numeric vector of length PP + 1 specifying the mean of prior distribution for coefficients and intercept

VarBeta is a numeric vector for the variance of the prior distribution of coefficients and intercept. Its length is same as that of MuBeta.

MuZ is a numeric vector of length same as the dimension of the latent space, specifying the prior mean of the latent positions.

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.

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

tune

an optional list of tuning parameters for tuning the chain. If tune = NULL, default tuning is done. Else,

tune = list(tuneBeta, tuneInt,tuneZ).

tuneBeta and tuneInt have the same structure as beta and intercept in initialVals.

ZZ is a vector of length NN.

tuneIn

a logical to indicate whether tuning is needed in the MCMC sampling. Default is FALSE.

dd

dimension of latent space.

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.

niter

number of iterations for the MCMC chain.

object

object of class 'HLSM' returned by HLSM() or HLSMfixedEF()

burnin

numeric value to burn the chain while extracting results from the 'HLSM'object. Default is burnin = 0.

thin

numeric value by which the chain is to be thinned while extracting results from the 'HLSM' object. Default is thin = 1.

verbose

logical value; TRUE results in messages during MCMC tuning

Details

The HLSMfixedEF and HLSMrandomEF functions will not automatically assess thinning and burn-in. To ensure appropriate inference, see HLSMdiag. See also LSM for fitting network data from a single network.

Value

Returns an object of class "HLSM". It is a list with following components:

draws

list of posterior draws for each parameters.

acc

list of acceptance rates of the parameters.

call

the matched call.

tune

final tuning values

Author(s)

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)
data(schoolsAdviceData)

#Set values for the inputs of the function
priors = NULL
tune = NULL
initialVals = NULL
niter = 10

lsm.fit = LSM(Y=School9Network,edgeCov=School9EdgeCov, 
senderCov=School9NodeCov, receiverCov=School9NodeCov, estimate.intercept=0, niter = niter)

HLSM: Included Data Sets

Description

Data set included with the HLSM package: network variables from Pitts and Spillane (2009).

Usage

	ps.advice.mat
	ps.advice.df
	ps.all.vars.mat
	ps.edge.vars.mat
	ps.edge.df
	ps.school.vars.mat
	ps.teacher.vars.mat
	ps.node.df
	School9Network
	School9NodeCov
	School9EdgeCov

Format

ps.advice.mat: a list of 15 sociomatrices of advice seeking network, one for each school.

ps.advice.df: a data frame of all ties.

ps.all.vars.mat: a list of 15 arrays of all the covariates, one for each school. edge.vars.mat: a list of edge level covariates for 15 different school.

ps.edge.df: a dataframe of all edge covariates.

ps.school.vars.mat: a list of school level covariates for all 15 schools.

ps.teacher.vars.mat: a list of node level covariates for all 15 schools.

ps.node.df: a dataframe of all node covariates.

ps.all.vars.mat: a single list of length 15 containing the covariates mentioned above.

School9Network: a single adjacency matrix from School 9.

School9NodeCov: a dataframe with node covariates

School9EdgeCov: a dataframe with dyad-level covariates.

Author(s)

Sam Adhikari and Tracy Sweet

References

Pitts, V., & Spillane, J. (2009). "Using social network methods to study school leadership".International Journal of Research & Method in Education, 32, 185-207

Sweet, T.M., Thomas, A.C., and Junker, B.W. (2013). "Hierarchical Network Models for Education Research: Hierarchical Latent Space Models". Journal of Educational and Behavorial Statistics.