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
)
