Description Usage Arguments Details Value Author(s) References Examples
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  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)

Y 
input outcome for different networks. Y can either be (i). list of sociomatrices for (ii). list of data frame with columns (iii). a dataframe with columns named as follows: 
edgeCov 
data frame to specify edge level covariates with (i). a column for network id named (ii). a column for sender node named (iii). a column for receiver nodes named (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 (ii.) a column (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 (ii). a column (iii). the rest for respective node level covariates. 
FullX 
list of numeric arrays of dimension 
initialVals 
an optional list of values to initialize the chain. If For fixed effect model For random effect model

priors 
an optional list to specify the hyperparameters for the prior distribution of the paramters.
If priors =

tune 
an optional list of tuning parameters for tuning the chain. If tune =

tuneIn 
a logical to indicate whether tuning is needed in the MCMC sampling. Default is 
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 
burnin 
numeric value to burn the chain while extracting results from the 'HLSM'object. Default is 
thin 
numeric value by which the chain is to be thinned while extracting results from the 'HLSM' object. Default is 
The HLSMfixedEF
and HLSMrandomEF
functions will not automatically assess thinning and burnin. To ensure appropriate inference, see HLSMdiag
.
See also LSM
for fitting network data from a single network.
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 
Sam Adhikari & Tracy Sweet
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  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)

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