

In [1]:

    
%load_ext autoreload
%autoreload 2
%matplotlib inline
import pandas as pd
import numpy as np









    



The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload



In [2]:

    
import plotly
import plotly.plotly as py
import plotly.graph_objs as go
plotly.offline.init_notebook_mode(connected=True)

load pp_survival data from a random survival model



In [3]:

    
ppsurv = pd.read_csv('plotly_example_data.csv')



In [4]:

    
ppsurv.head()









    Out[4]:






  
    
      
      Unnamed: 0
      iter
      model_cohort
      sex
      level_3
      event_time
      survival
    
  
  
    
      0
      0
      0
      test model
      female
      0
      0.000000
      1.000000
    
    
      1
      1
      0
      test model
      female
      1
      2.615961
      1.000000
    
    
      2
      2
      0
      test model
      female
      2
      3.584694
      0.977162
    
    
      3
      3
      0
      test model
      female
      3
      4.379338
      0.952078
    
    
      4
      4
      0
      test model
      female
      4
      6.253546
      0.945339

plot posterior predicted survival time by sex



In [5]:

    
ppsummary = ppsurv.groupby(['sex','event_time'])['survival'].agg({
        '95_lower': lambda x: np.percentile(x, 2.5),
        '95_upper': lambda x: np.percentile(x, 97.5),
        '50_lower': lambda x: np.percentile(x, 25),
        '50_upper': lambda x: np.percentile(x, 75),
        'median': lambda x: np.percentile(x, 50),
    }).reset_index()



In [6]:

    
ppsummary[ppsummary['sex']=='female'].tail()



In [7]:

    
shade_colors = dict(male='rgba(0, 128, 128, {})', female='rgba(214, 12, 140, {})')
line_colors = dict(male='rgb(0, 128, 128)', female='rgb(214, 12, 140)')
ppsummary.sort_values(['sex', 'event_time'], inplace=True)



In [8]:

    
data5 = list()
for grp, grp_df in ppsummary.groupby('sex'):
    x = list(grp_df['event_time'].values)
    x_rev = x[::-1]
    y_upper = list(grp_df['50_upper'].values)
    y_lower = list(grp_df['50_lower'].values)
    y_lower = y_lower[::-1]
    y2_upper = list(grp_df['95_upper'].values)
    y2_lower = list(grp_df['95_lower'].values)
    y2_lower = y2_lower[::-1]
    y = list(grp_df['median'].values)
    my_shading50 = go.Scatter(
        x = x + x_rev,
        y = y_upper + y_lower,
        fill = 'tozerox',
        fillcolor = shade_colors[grp].format(0.3),
        line = go.Line(color = 'transparent'),
        showlegend = True,
        name = '{} - 50% CI'.format(grp),
    )
    my_shading95 = go.Scatter(
        x = x + x_rev,
        y = y2_upper + y2_lower,
        fill = 'tozerox',
        fillcolor = shade_colors[grp].format(0.1),
        line = go.Line(color = 'transparent'),
        showlegend = True,
        name = '{} - 95% CI'.format(grp),
    )
    my_line = go.Scatter(
        x = x,
        y = y,
        line = go.Line(color=line_colors[grp]),
        mode = 'lines',
        name = grp,
    )
    data5.append(my_line)    
    data5.append(my_shading50)
    data5.append(my_shading95)



In [9]:

    
layout5 = go.Layout(
    yaxis=dict(
        title='Survival (%)',
        #zeroline=False,
        tickformat='.0%',
    ),
    xaxis=dict(title='Days since enrollment')
)



In [10]:

    
py.iplot(go.Figure(data=data5, layout=layout5), filename='survivalstan/posterior-predicted-values')









    Out[10]:



In [ ]:

	sex	event_time	median	50_lower	95_lower	95_upper	50_upper
72	female	18.543842	0.338076	0.283789	0.182441	0.519674	0.405841
73	female	18.656898	0.331524	0.275271	0.176058	0.507488	0.398727
74	female	18.932325	0.320479	0.269188	0.176320	0.492980	0.384687
75	female	19.811832	0.306252	0.251404	0.157844	0.471677	0.367278
76	female	20.000000	0.284725	0.229034	0.134397	0.467671	0.344815

	Unnamed: 0	model_cohort	sex	level_3	event_time	survival
0	0	test model	female	0	0.000000	1.000000
1	1	test model	female	1	2.615961	1.000000
2	2	test model	female	2	3.584694	0.977162
3	3	test model	female	3	4.379338	0.952078
4	4	test model	female	4	6.253546	0.945339