def plot_survival_function(self, **kwargs): """Alias of ``plot``""" return _plot_estimate( self, estimate=self.survival_function_, confidence_intervals=self.confidence_interval_survival_function_, **kwargs)
def plot_hazard(self, bandwidth=None, **kwargs): if bandwidth is None: raise ValueError( "Must specify a bandwidth parameter in the call to plot_hazard, e.g. `plot_hazard(bandwidth=1.0)`" ) estimate = self.smoothed_hazard_(bandwidth) confidence_intervals = self.smoothed_hazard_confidence_intervals_(bandwidth, estimate.values[:, 0]) return _plot_estimate(self, estimate, confidence_intervals=confidence_intervals, **kwargs)
def plot_survival_function(self, **kwargs): """Alias of ``plot``""" return _plot_estimate( self, estimate=self.survival_function_, confidence_intervals=self.confidence_interval_survival_function_, **kwargs )
def plot_survival_function(self, **kwargs): set_kwargs_drawstyle(kwargs, "default") return _plot_estimate( self, estimate=getattr(self, "survival_function_"), confidence_intervals=self.confidence_interval_survival_function_, **kwargs )
def plot_survival_function(self, **kwargs): """Alias of ``plot``""" if not CensoringType.is_interval_censoring(self): return _plot_estimate(self, estimate="survival_function_", **kwargs) else: # hack for now. color = coalesce(kwargs.get("c"), kwargs.get("color"), "k") self.survival_function_.plot(drawstyle="steps-pre", color=color, **kwargs)
def plot_cumulative_density(self, **kwargs): """ Plots a pretty figure of the cumulative density function. Matplotlib plot arguments can be passed in inside the kwargs. Parameters ----------- show_censors: bool place markers at censorship events. Default: False censor_styles: bool If show_censors, this dictionary will be passed into the plot call. ci_alpha: bool the transparency level of the confidence interval. Default: 0.3 ci_force_lines: bool force the confidence intervals to be line plots (versus default shaded areas). Default: False ci_show: bool show confidence intervals. Default: True ci_legend: bool if ci_force_lines is True, this is a boolean flag to add the lines' labels to the legend. Default: False at_risk_counts: bool show group sizes at time points. See function ``add_at_risk_counts`` for details. Default: False loc: slice specify a time-based subsection of the curves to plot, ex: >>> model.plot(loc=slice(0.,10.)) will plot the time values between t=0. and t=10. iloc: slice specify a location-based subsection of the curves to plot, ex: >>> model.plot(iloc=slice(0,10)) will plot the first 10 time points. Returns ------- ax: a pyplot axis object """ if not CensoringType.is_interval_censoring(self): return _plot_estimate(self, estimate="cumulative_density_", **kwargs) else: # hack for now. color = coalesce(kwargs.get("c"), kwargs.get("color"), "k") self.cumulative_density_.plot(drawstyle="steps", color=color, **kwargs)
def plot_survival_function(self, **kwargs): """Alias of ``plot``""" if not CensoringType.is_interval_censoring(self): return _plot_estimate(self, estimate="survival_function_", **kwargs) else: # hack for now. def safe_pop(dict, key): if key in dict: return dict.pop(key) else: return None color = coalesce(safe_pop(kwargs, "c"), safe_pop(kwargs, "color"), "k") self.survival_function_.plot(drawstyle="steps-pre", color=color, **kwargs)
def plot_cumulative_density(self, **kwargs): """ Plots a pretty figure of {0}.{1} Matplotlib plot arguments can be passed in inside the kwargs, plus Parameters ----------- show_censors: bool place markers at censorship events. Default: False censor_styles: bool If show_censors, this dictionary will be passed into the plot call. ci_alpha: bool the transparency level of the confidence interval. Default: 0.3 ci_force_lines: bool force the confidence intervals to be line plots (versus default shaded areas). Default: False ci_show: bool show confidence intervals. Default: True ci_legend: bool if ci_force_lines is True, this is a boolean flag to add the lines' labels to the legend. Default: False at_risk_counts: bool show group sizes at time points. See function ``add_at_risk_counts`` for details. Default: False loc: slice specify a time-based subsection of the curves to plot, ex: >>> model.plot(loc=slice(0.,10.)) will plot the time values between t=0. and t=10. iloc: slice specify a location-based subsection of the curves to plot, ex: >>> model.plot(iloc=slice(0,10)) will plot the first 10 time points. invert_y_axis: bool boolean to invert the y-axis, useful to show cumulative graphs instead of survival graphs. (Deprecated, use ``plot_cumulative_density()``) Returns ------- ax: a pyplot axis object """ return _plot_estimate( self, estimate=self.cumulative_density_, confidence_intervals=self.confidence_interval_cumulative_density_, **kwargs )
def plot_cumulative_density(self, **kwargs): """ Plots a pretty figure of {0}.{1} Matplotlib plot arguments can be passed in inside the kwargs, plus Parameters ----------- show_censors: bool place markers at censorship events. Default: False censor_styles: bool If show_censors, this dictionary will be passed into the plot call. ci_alpha: bool the transparency level of the confidence interval. Default: 0.3 ci_force_lines: bool force the confidence intervals to be line plots (versus default shaded areas). Default: False ci_show: bool show confidence intervals. Default: True ci_legend: bool if ci_force_lines is True, this is a boolean flag to add the lines' labels to the legend. Default: False at_risk_counts: bool show group sizes at time points. See function ``add_at_risk_counts`` for details. Default: False loc: slice specify a time-based subsection of the curves to plot, ex: >>> model.plot(loc=slice(0.,10.)) will plot the time values between t=0. and t=10. iloc: slice specify a location-based subsection of the curves to plot, ex: >>> model.plot(iloc=slice(0,10)) will plot the first 10 time points. invert_y_axis: bool boolean to invert the y-axis, useful to show cumulative graphs instead of survival graphs. (Deprecated, use ``plot_cumulative_density()``) Returns ------- ax: a pyplot axis object """ return _plot_estimate( self, estimate=self.cumulative_density_, confidence_intervals=self.confidence_interval_cumulative_density_, **kwargs )
def plot_survival_function(self, **kwargs): """Alias of ``plot``""" return _plot_estimate(self, estimate="survival_function_", **kwargs)
def plot_hazard(self, **kwargs): return _plot_estimate( self, estimate=getattr(self, "hazard_"), confidence_intervals=self.confidence_interval_hazard_, **kwargs)
def plot_survival_function(self, **kwargs): return _plot_estimate( self, estimate=getattr(self, "survival_function_"), confidence_intervals=self.confidence_interval_survival_function_, **kwargs)
def plot(self, **kwargs): return _plot_estimate(self, estimate=getattr(self, self._estimate_name), confidence_intervals=self.confidence_interval_, **kwargs)
def plot_hazard(self, **kwargs): set_kwargs_drawstyle(kwargs, "default") return _plot_estimate( self, estimate=getattr(self, "hazard_"), confidence_intervals=self.confidence_interval_hazard_, **kwargs )
def plot(self, **kwargs): set_kwargs_drawstyle(kwargs, "default") return _plot_estimate( self, estimate=getattr(self, self._estimate_name), confidence_intervals=self.confidence_interval_, **kwargs )