API References
Compute the log-rank P-value and survival curves based on kaplan meier.
# Name : kaplanmeier.py # Author : E.Taskesen # Date : July. 2018
- kaplanmeier.kaplanmeier.compute_coord(survtimes)
Compute coordinates.
- kaplanmeier.kaplanmeier.example_data()
Create example data.
- kaplanmeier.kaplanmeier.fit(time_event, censoring, labx, verbose=3)
Compute the log-rank P-value and survival curves based on kaplan meier.
- Parameters
time_event (Float.) – Numpy array with survival-time in years/months/days (not a datetime!)
censoring (array-like) – numpy array with censoring (1=event, 0=ongoing). At the time you want to make inferences about durations, it is possible, likely true, that not all the death events have occured yet. In case of patients, you would like to put 1=death as an event.
labx (array-like of type string or int) – Class labels. Each class label is seperately plotted.
verbose (int, (default: 3)) – Verbosity messages.
- Returns
logrank_P (float) : P-value
logrank_Z (float) : Z-score
logrank (float) : fitted logrank_test model
labx (list) : Class labels
uilabx (list) : Unique Class labels
time_event (float) : Time to event
censoring (bool) : Censored or not
- Return type
dict()
Examples
>>> # Import library >>> import kaplanmeier as km >>> >>> # Example data >>> df = km.example_data() >>> >>> # Fit >>> results = km.fit(df['time'], df['Died'], df['group']) >>> >>> # Plot >>> km.plot(results) >>> >>> km.plot(results, cmap='Set1', cii_lines=True, cii_alpha=0.05) >>> km.plot(results, cmap=[(1, 0, 0),(0, 0, 1)]) >>> km.plot(results, cmap='Set1', methodtype='custom') >>> >>> results['logrank_P'] >>> results['logrank_Z']
- kaplanmeier.kaplanmeier.loop(newsurv, y, h_coords, v_coords, lost)
Part of computing coordinates (custom implementation).
- kaplanmeier.kaplanmeier.make_class_color_names(data, labx, uilabx, cmap)
Create class colors.
- kaplanmeier.kaplanmeier.plot(out, fontsize=12, savepath='', width=10, height=6, cmap='Set1', cii_alpha=0.05, cii_lines='dense', methodtype='lifeline', title=None, full_ylim=False, y_percentage=False)
Make plot.
- Parameters
out (dict) – Results from the fit function.
fontsize (int (default: 12)) – Font size for the graph.
savepath (String (default: '')) – Path to store the figure.
width (int (default: 10)) – Width of the figure.
height (int (default: 10)) – height of the figure.
cmap (str (default: 'Set1')) – Specify your own colors for each class-label or use a colormap: https://matplotlib.org/examples/color/colormaps_reference.html. [(1, 0, 0),(0, 0, 1),(..)] ‘Set1’ (default) ‘Set2’ Discrete colors ‘Pastel1’ Discrete colors ‘Paired’ Discrete colors ‘rainbow’ ‘bwr’ Blue-white-red ‘binary’ or ‘binary_r’ ‘seismic’ Blue-white-red ‘Blues’ white-to-blue ‘Reds’ white-to-red
cii_alpha (float (default: 0.05)) – Confidence interval (works only when methodtype=’lifelines’).
cii_lines (String (default: 'dense')) – Confidence lines (works only when methodtype=’lifelines’). ‘dense’ (default) ‘lifelines’ ‘custom’ or None
methodtype (str (default: 'lifeline')) –
Implementation type. ‘dense’ (dense/filled lines) ‘line’
None (no lines)
title (str (default: None)) – In case of None, the logrank P-values is shown. Title of the plot.
- Return type
None.
Examples
>>> # Import library >>> import kaplanmeier as km >>> >>> # Example data >>> df = km.example_data() >>> >>> # Fit >>> results = km.fit(df['time'], df['Died'], df['group']) >>> >>> # Plot >>> km.plot(results) >>> >>> km.plot(results, cmap='Set1', cii_lines=True, cii_alpha=0.05) >>> km.plot(results, cmap=[(1, 0, 0),(0, 0, 1)]) >>> km.plot(results, cmap='Set1', methodtype='custom')