Merge pull request #29 from hammerlab/develop

Develop
hammerlab · Nov 8, 2016 · f059a71 · f059a71
2 parents 34fe50b + 4ad283b
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diff --git a/README.md b/README.md
@@ -12,6 +12,20 @@ Overview
 
 Library of Stan Models for Survival Analysis
 
+Features:
+
+* Variety of standard survival models
+	- Weibull, Exponential, and Gamma parameterization
+	- PEM models with variety of baseline hazards
+	- PEM model with varying-coefficients (by group)
+	- PEM model with time-varying-effects
+* Extensible framework - bring your own Stan code, or edit the models above
+* Uses [pandas](http://pandas.pydata.org) data frames & [patsy](https://pypi.python.org/pypi/patsy) formulas
+* Graphical posterior predictive checking (currently PEM models only)
+* Plot posterior estimates of key parameters using [seaborn](https://pypi.python.org/pypi/seaborn)
+* Annotate posterior draws of parameter estimates, format as [pandas](http://pandas.pydata.org) dataframes
+* Works with extensions to [pystan](https://pystan.readthedocs.io/en/latest/), such as [stancache](http://github.com/jburos/stancache) or [pystan-cache](https://github.com/paulkernfeld/pystan-cache)
+
 Installation / Usage
 --------------------
 
@@ -20,19 +34,25 @@ Once we push this repo to pypi, you will be able to install using pip, as:
     $ pip install survivalstan
 
 
-For now, please clone the repo:
+Or, you can clone the repo:
 
     $ git clone https://github.com/hammerlab/survivalstan.git
     $ pip install .
 
 Contributing
 ------------
 
-TBD
+Details to come. For now, please do not hesitate to contribute if you would like. 
 
 Example
 -------
 
+There are several examples included in the [example-notebooks](), roughly one corresponding to each model.
+
+If you are not sure where to start, [Test pem_survival_model with simulated data.ipynb](https://github.com/hammerlab/survivalstan/blob/master/example-notebooks/Test%20pem_survival_model%20with%20simulated%20data.ipynb) contains the most explanatory text. Many of the other notebooks are sparse on explanation, but do illustrate variations on the different models.
+
+For basic usage:
+
 ```
 import survivalstan
 import stanity
@@ -61,6 +81,9 @@ testfit_wei = survivalstan.fit_stan_survival_model(
 ## coefplot for Weibull coefficient estimates
 sb.boxplot(x = 'value', y = 'variable', data = testfit_wei['coefs'])
 
+## or, use plot_coefs
+survivalstan.utils.plot_coefs([testfit_wei])
+
 ## print summary of MCMC draws from posterior for each parameter
 print(testfit_wei['fit'])
 
@@ -85,13 +108,23 @@ print(testfit_pem['fit'])
 ## coefplot for PEM model results
 sb.boxplot(x = 'value', y = 'variable', data = testfit_pem['coefs'])
 
+## plot baseline hazard (only PEM models)
+survivalstan.utils.plot_coefs([testfit_pem], element='baseline')
+
+## posterior-predictive checking (only PEM models)
+survivalstan.utils.plot_pp_survival([testfit_pem])
+
 ## e.g. compare models using PSIS-LOO
 stanity.loo_compare(testfit_wei['loo'], testfit_pem['loo'])
 
 ## compare coefplots 
 sb.boxplot(x = 'value', y = 'variable', hue = 'model_cohort',
     data = testfit_pem['coefs'].append(testfit_wei['coefs']))
 plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
+
+## (or, use survivalstan.utils.plot_coefs)
+survivalstan.utils.plot_coefs([testfit_wei, testfit_pem])
+
 ```
 
 
diff --git a/example-notebooks/Test pem_survival_model_gamma with simulated data.ipynb b/example-notebooks/Test pem_survival_model_gamma with simulated data.ipynb
diff --git a/example-notebooks/Test pem_survival_model_randomwalk with simulated data.ipynb b/example-notebooks/Test pem_survival_model_randomwalk with simulated data.ipynb
diff --git a/example-notebooks/Test pem_survival_model_timevarying with simulated data.ipynb b/example-notebooks/Test pem_survival_model_timevarying with simulated data.ipynb