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Tip revision: 281f55f9f8ae3be814d154729507a7fb494e8a92 authored by Tracy Sweet on 06 December 2021, 12:00:02 UTC
version 0.9.0
Tip revision: 281f55f

\title{Plotting functions for HLSM objects}

 Functions for plotting HLSM/LSM model fits of class 'HLSM'. HSLMcovplots is the most recent function to plot posterior distribution summaries. HLSMplotLikelihood( ) plots the likelihood, HLSMcovplots( ) summarizes posterior draws of the parameters from MCMC sample, and ) is for plotting the mean latent position estimates.

	HLSMplotLikelihood(object, burnin = 0, thin = 1)
	HLSMcovplots(fitted.model, burnin = 0, thin = 1)


	\item{object}{object of class 'HLSM' obtained as an output from \code{LSM}, \code{HLSMrandomEF()} or \code{HLSMfixedEF()}
	\item{fitted.model}{model fit from LSM(), HLSMrandomEF() or HLSMfixedEF()}

\item{burnin}{numeric value to burn the chain for plotting the results from the 'HLSM'object }
	\item{thin}{a numeric thinning value}

 returns plot objects.

\author{Sam Adhikari & Tracy Sweet}

#using advice seeking network of teachers in 15 schools
#to fit the data

#Random effect model#
priors = NULL
tune = NULL
initialVals = NULL
niter = 10 = HLSMrandomEF(Y = ps.advice.mat,FullX = ps.edge.vars.mat,
	initialVals = initialVals,priors = priors,
	tune = tune,tuneIn = FALSE,dd = 2,niter = niter)

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