Description Usage Arguments Value Author(s) Examples
plotLikelihood( ) plots the likelihood, and plotDiagnostic( ) plots diagnostic-plot of posterior draws of the parameters from MCMC sample. plotHLSM.random.fit( ) and plotHLSM.fixed.fit( ) are functions to plot mean-results from fitted models, and plotHLSM.LS( ) is for plotting the mean latent position estimates.
1 2 3 4 5 | plotLikelihood(object,burnin = 0, thin = 1)
plotDiagnostic(chain)
plotHLSM.random.fit(fitted.model,parameter,burnin=0,thin=1)
plotHLSM.fixed.fit(fitted.model, parameter,burnin=0,thin=1)
plotHLSM.LS(fitted.model,pdfname=NULL,burnin=0,thin=1,...)
|
object |
object of class 'HLSM' obtained as an output from |
fitted.model |
model fit from either HLSMrandomEF() or HLSMfixedEF() |
parameter |
parameter to plot; specified as |
pdfname |
character to specify the name of the pdf to save the plot if desired. Default is NULL |
burnin |
numeric value to burn the chain for plotting the results from the 'HLSM'object |
thin |
a numeric thinning value |
chain |
a numeric vector of posterior draws of parameter of interest. |
... |
other options |
returns plot objects.
Sam Adhikari
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | #using advice seeking network of teachers in 15 schools
#to fit the data
#Random effect model#
priors = NULL
tune = NULL
initialVals = NULL
niter = 10
random.fit = HLSMrandomEF(Y = ps.advice.mat,FullX = ps.edge.vars.mat,
initialVals = initialVals,priors = priors,
tune = tune,tuneIn = FALSE,dd = 2,niter = niter,
intervention = 0)
plotLikelihood(random.fit)
intercept = getIntercept(random.fit)
dim(intercept) ##is an array of dimension niter by 15
plotDiagnostic(intercept[,1])
plotHLSM.LS(random.fit)
plotHLSM.random.fit(random.fit,parameter = 'Beta')
plotHLSM.random.fit(random.fit,parameter = 'Intercept')
##look at the diagnostic plot of intercept for the first school
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