Upload
sara-townsend
View
212
Download
0
Tags:
Embed Size (px)
Citation preview
Progress on the software developed under E-STAT
Bill Browne and Chris Charlton
Why the slides?• To remind me which templates to demonstrate
• Let’s start with simply demonstrating 1 templateTemplate1lev with the tutorial dataset and a
regression model in E-STAT• Run in E-STAT• Show generated C code • Show java script and explain the server
possibilities• Show the template code (maybe discuss input
functions)
Explanation for following screenshots
• We show how to set up the model and run using the E-STAT engine
• The generated C code can be taken away and run on it’s own or modified by an algorithm writer
• The Java script code is similar however offers the opportunity of hosting the software on a server but running the estimation (via Java script) on the local machine.
Setting up first model
Equations for model and model code
Output from the E-STAT engine
Output of generated C code
View of Java Script source
Java Script output
Template input codeclass Template1Lev(Template): invars = '''y = DataVector('response: ')D = Text('specify distribution: ', ['Normal', 'Binomial', 'Poisson'])if D.name == 'Binomial': n = DataVector('denominator: ') link = Text('specify link function: ', ['logit', 'probit', 'cloglog'])if D.name == 'Poisson': link = Text(value = 'ln') offset = Text('Is there an offset: ', ['yes', 'no']) if offset.name == 'yes': n = DataVector('offset: ')if D.name == 'Normal': tau = ParamScalar() sigma = ParamScalar()x = DataMatrix('explanatory variables: ')beta = ParamVector()beta.ncols = len(x.name) '''
This code matches with the inputs in the web interface
Stringing Templates together
• Use rats dataset and aim to fit random intercepts model:
• Use template split and demonstrate the view and summary buttons
• Form the dataset called ratlong• Choose as template2lev and ratlong dataset.• Again use view to look at the dataset created.• Setup the random intercepts model
Viewing a data set via the view button
Summarising the data (like MLwiN names window)
Converting repeated measures data to a single response (Templatesplit)
Viewing the new dataset
Setting up a random intercepts model (with template2lev)
Output from the model
Rats continued
• Fit the model storing results in ratout• Construct the VPC (= (1/tau_u)/((1/tau_u)
+(1/tau)) !!!) using TemplateEvalute storing in ratsout again
• Then view the chain using TemplateColumndiag
• Finally look at the residuals using Templatecaterpillar
Using TemplateEvaluate to construct the VPC
View the file ratout (including VPC)
Using TemplateColumnDiag to look at the VPC
Set up templateCaterpillar for Caterpillar plot
Caterpillar plot
Other Stuff
• Graphics – show with tutorial (or with output u_1 and tau_u)
TemplateXYlabelTemplateHistogramAnd then show the python code!• Finally large numbers of model templates –
some still to do depends on Bruce’s algebra system.
TemplateXYlabel
All code for templateXYlabel – quite a short template!
from EStat.Templating import *from mako.template import Template as MakoTemplateclass TemplateXYLabel(Template): invars = '''yaxis = DataMatrix('Y values: ')xaxis = DataVector('X values: ')
yaxislabel = Text('Y label: ')xaxislabel = Text('X label: ')''‘ def preparedata(self, data): self.data = data return self.data def resultdata(self, m): return self.data def graphdata(self, data): import numpy from matplotlib.figure import Figure import matplotlib.lines as lines from matplotlib.backends.backend_agg import
FigureCanvasAgg
import subprocess import tempfile import os fig = Figure(figsize=(8,8)) ax = fig.add_subplot(100 + 10 + 1, xlabel = str(self.objects['xaxislabel'].name), ylabel = str(self.objects['yaxislabel'].name)) for n in self.objects['yaxis'].name: ax.plot(self.data[self.objects['xaxis'].name], self.data[n], 'x')
canvas = FigureCanvasAgg(fig) directory = tempfile.mkdtemp() + os.sep canvas.print_figure(directory + 'xyplot.png', dpi=80) return directory + 'xyplot.png'
TemplateHistogram
Classification as index notation
• Tutorial dataset and Template2levindex• Set up model then fire up Bruce’s demo (via
notepad)• Next show the C code• Finally run the model• Camille to demo interoperability later.• Going off piste: Any other models ?• Any suggestions for improvements, wish list etc.
Setting up model in E-Stat
Model Code in detailmodel { for (i in 1:length(normexam)) { normexam[i] ~ dnorm(mu[i], tau) mu[i] <- cons[i] * beta0 + standlrt[i] * beta1 + u[school[i]] * cons[i] } for (j in 1:length(u)) { u[j] ~ dnorm(0, tau_u) } # Priors beta0 ~ dflat() beta1 ~ dflat() tau ~ dgamma(0.001000, 0.001000) tau_u ~ dgamma(0.001000, 0.001000) }
Bruce’s Demo algebra system step for u
Generated C code – note the if statements for u
Model executed in E-Stat
Note code takes longer but generalises easily to cross-classified models.
Interoperability with WinBUGS
Output from WinBUGS with multiple chains