Presentation on theme: "Progress on the software developed under E-STAT Bill Browne and Chris Charlton."— Presentation transcript:
Progress on the software developed under E-STAT Bill Browne and Chris Charlton
Why the slides? To remind me which templates to demonstrate Lets start with simply demonstrating 1 template Template1lev 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 its 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.
Template input code class 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
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
Other Stuff Graphics – show with tutorial (or with output u_1 and tau_u) TemplateXYlabel TemplateHistogram And then show the python code! Finally large numbers of model templates – some still to do depends on Bruces algebra system.
Classification as index notation Tutorial dataset and Template2levindex Set up model then fire up Bruces 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.