HLSMrandomEF {HLSM} | R Documentation |
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 (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 hyper-parameters 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 |
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)