`survive.nonparametric`.NonparametricEstimator¶

class survive.nonparametric.NonparametricEstimator[source]¶

Abstract base class for nonparametric estimators.

Attributes:	`conf_level` Confidence level of the confidence intervals. `conf_type` Type of confidence intervals to report. `data_` Survival data used to fit the estimator. `random_state` Seed for this model’s random number generator. `summary` Get a summary of this estimator. `tie_break` How to handle tied event times. `var_type` Type of variance estimate to compute.

Methods

`check_fitted`()	Check whether this model is fitted.
`fit`(args, *kwargs)	Fit this model to data.
`plot`(*groups[, ci, ci_style, ci_kwargs, …])	Plot the estimates.
`predict`(time, *[, return_se, return_ci])	Compute estimates.
`to_string`([max_line_length])	String representation of this model.

check_fitted()[source]¶: Check whether this model is fitted. If not, raise an exception.

conf_level¶

Confidence level of the confidence intervals.

Returns:	conf_level : float The confidence level.

conf_type¶

Type of confidence intervals to report.

Returns:	conf_type : str The type of confidence interval.

data_¶

Survival data used to fit the estimator.

This property is only available after fitting.

Returns:	data : SurvivalData The `survive.SurvivalData` instance used to fit the estimator.

fit(*args, **kwargs)[source]¶

Fit this model to data.

Returns:	self : Model The `Model` being fitted.

plot(*groups, ci=True, ci_style='fill', ci_kwargs=None, mark_censor=True, mark_censor_kwargs=None, legend=True, legend_kwargs=None, colors=None, palette=None, ax=None, **kwargs)[source]¶

Plot the estimates.

Parameters:

*groups : list of group labels: Specify the groups whose curves should be plotted. If none are given, the curves for all groups are plotted.
ci : bool, optional: If True, draw pointwise confidence intervals.
ci_style : {“fill”, “lines”}, optional: Specify how to draw the confidence intervals. If ci_style is “fill”, the region between the lower and upper confidence interval curves will be filled. If ci_style is “lines”, only the lower and upper curves will be drawn (this is inspired by the style of confidence intervals drawn by plot.survfit in the R package survival).
ci_kwargs : dict, optional: Additional keyword parameters to pass to fill_between() (if ci_style is “fill”) or step() (if ci_style is “lines”) when plotting the pointwise confidence intervals.
mark_censor : bool, optional: If True, indicate the censored times by markers on the plot.
mark_censor_kwargs : dict, optional: Additional keyword parameters to pass to scatter() when marking censored times.
legend : bool, optional: Indicates whether to display a legend for the plot.
legend_kwargs : dict, optional: Keyword parameters to pass to legend().
colors : list or tuple or dict or str, optional: Colors for each group. This is ignored if palette is provided. This can be a sequence of valid matplotlib colors to cycle through, or a dictionary mapping group labels to matplotlib colors, or the name of a matplotlib colormap.
palette : str, optional: Name of a seaborn color palette. Requires seaborn to be installed. Setting a color palette overrides the colors parameter.
ax : matplotlib.axes.Axes, optional: The axes on which to plot. If this is not specified, the current axes will be used.
**kwargs : keyword arguments: Additional keyword arguments to pass to step() when plotting the estimates.

Returns:

matplotlib.axes.Axes: The Axes on which the plot was drawn.

predict(time, *, return_se=False, return_ci=False)[source]¶

Compute estimates.

Parameters:

time : array-like: One-dimensional array of times at which to make estimates.
return_se : bool, optional: If True, also return standard error estimates.
return_ci : bool, optional: If True, also return confidence intervals.

Returns:

estimate : pandas.DataFrame: DataFrame of estimates. Each columns represents a group, and each row represents an entry of time.
std_err : pandas.DataFrame, optional: Standard errors of the estimates. Same shape as estimate. Returned only if return_se is True.
lower : pandas.DataFrame, optional: Lower confidence interval bounds. Same shape as estimate. Returned only if return_ci is True.
upper : pandas.DataFrame, optional: Upper confidence interval bounds. Same shape as estimate. Returned only if return_ci is True.

random_state¶

Seed for this model’s random number generator. This may not be an numpy.random.RandomState instance. The internal RNG is not a public attribute and should not be used directly.

Returns:	random_state : object The seed for this model’s RNG.

summary¶

Get a summary of this estimator.

Returns:	summary : NonparametricEstimatorSummary The summary of this estimator.

Parameters:	max_line_length : int, optional Specifies the maximum length of a line. If None, everything will be on one line.
Returns:	model_string : str A string representation of this model which should be able to be used to instantiate a new identical model.

survive.nonparametric.NonparametricEstimator¶

`survive.nonparametric`.NonparametricEstimator¶