FrozenLeftTruncatedModel

class relife.lifetime_model.FrozenLeftTruncatedModel(model, args_nb_assets, a0, *args)

Frozen left truncated model.

Parameters:

modelLeftTruncatedModel

Any left truncated model.

args_nb_assetsint

Number of assets given in frozen arguments. It is automatically computed by freeze function.

a0float or np.ndarray

Conditional age values to be frozen.

*argsfloat or np.ndarray

Additional arguments needed by the model to be frozen.

Attributes:

unfrozen_modelLeftTruncatedModel

The unfrozen left truncated model.

frozen_argstuple of float or np.ndarray

All the frozen arguments given and necessary to compute model functions.

args_nb_assetsint

Number of assets passed in frozen arguments. The data is mainly used to control numpy broadcasting and may not interest an user.

Warning

This class is documented for the purpose of clarity and mainly address contributors or advance users. Actually, the recommanded way to instanciate a FrozenLeftTruncatedModel is use to freeze factory function.

Methods

cdf	The cumulative distribution function.
chf	The cumulative hazard function.
hf	The hazard function.
ichf	Inverse cumulative hazard function.
isf	Inverse survival function.
ls_integrate	Lebesgue-Stieltjes integration.
mean	The mean.
median	The median.
moment	n-th order moment
mrl	The mean residual life function.
pdf	The probability density function.
ppf	The percent point function.
rvs	Random variable sampling.
sf	The survival function.
unfreeze
var	The variance.

cdf(time)

The cumulative distribution function.

Parameters:

timefloat or np.ndarray

Elapsed time value(s) at which to compute the function. If ndarray, allowed shapes are (), (n,) or (m, n).

Returns:

np.float64 or np.ndarray

Function values at each given time(s).

chf(time)

The cumulative hazard function.

Parameters:

timefloat or np.ndarray

Elapsed time value(s) at which to compute the function. If ndarray, allowed shapes are (), (n,) or (m, n).

Returns:

np.float64 or np.ndarray

Function values at each given time(s).

hf(time)

The hazard function.

Parameters:

timefloat or np.ndarray

Elapsed time value(s) at which to compute the function. If ndarray, allowed shapes are (), (n,) or (m, n).

Returns:

np.float64 or np.ndarray

Function values at each given time(s).

ichf(cumulative_hazard_rate)

Inverse cumulative hazard function.

Parameters:

cumulative_hazard_ratefloat or np.ndarray

Cumulative hazard rate value(s) at which to compute the function. If ndarray, allowed shapes are (), (n,) or (m, n).

Returns:

np.float64 or np.ndarray

Function values at each given cumulative hazard rate(s).

isf(probability)

Inverse survival function.

Parameters:

probabilityfloat or np.ndarray

Probability value(s) at which to compute the function. If ndarray, allowed shapes are (), (n,) or (m, n).

Returns:

np.float64 or np.ndarray

Function values at each given probability value(s).

ls_integrate(func, a, b, deg=10)

Lebesgue-Stieltjes integration.

Parameters:

funccallable (in1 ndarray , out1 ndarray)

The callable must have only one ndarray object as argument and one ndarray object as output

andarray (maximum number of dimension is 2)

Lower bound(s) of integration.

bndarray (maximum number of dimension is 2)

Upper bound(s) of integration. If lower bound(s) is infinite, use np.inf as value.)

degint, default 10

Degree of the polynomials interpolation

Returns:

np.ndarray

Lebesgue-Stieltjes integral of func from a to b.

mean()

The mean.

Returns:

np.float64

The mean value.

median()

The median.

Returns:

np.float64

The median value.

moment(n)

n-th order moment

Parameters:

norder of the moment, at least 1.

Returns:

np.float64

n-th order moment.

mrl(time)

The mean residual life function.

Parameters:

timefloat or np.ndarray

Elapsed time value(s) at which to compute the function. If ndarray, allowed shapes are (), (n,) or (m, n).

Returns:

np.float64 or np.ndarray

Function values at each given time(s).

property nb_params

Number of parameters.

Returns:

int

Number of parameters.

property params

Parameters values.

Returns:

ndarray

Parameters values of the core

Notes

If parameter values are not set, they are encoded as np.nan value.

Parameters can be by manually setting`params` through its setter, fitting the core if fit exists or by specifying all parameters values when the core object is initialized.

property params_names

Parameters names.

Returns:

list of str

Parameters names

Notes

Parameters values can be requested (a.k.a. get) by their name at instance level.

pdf(time)

The probability density function.

Parameters:

timefloat or np.ndarray

Elapsed time value(s) at which to compute the function. If ndarray, allowed shapes are (), (n,) or (m, n).

Returns:

np.float64 or np.ndarray

Function values at each given time(s).

ppf(probability)

The percent point function.

Parameters:

probabilityfloat or np.ndarray

Probability value(s) at which to compute the function. If ndarray, allowed shapes are (), (n,) or (m, n).

Returns:

np.float64 or np.ndarray

Function values at each given probability value(s).

Notes

The ppf is the inverse of cdf().

rvs(size, return_event=False, return_entry=False, seed=None)

Random variable sampling.

Parameters:

sizeint, (int,) or (int, int)

Size of the generated sample. If size is n or (n,), n samples are generated. If size is (m,n), a 2d array of samples is generated.

return_eventbool, default is False

If True, returns event indicators along with the sample time values.

random_entrybool, default is False

If True, returns corresponding entry values of the sample time values.

seedoptional int, default is None

Random seed used to fix random sampling.

Returns:

float, ndarray or tuple of float or ndarray

The sample values. If either return_event or random_entry is True, returns a tuple containing the time values followed by event values, entry values or both.

sf(time)

The survival function.

Parameters:

timefloat or np.ndarray

Elapsed time value(s) at which to compute the function. If ndarray, allowed shapes are (), (n,) or (m, n).

Returns:

np.float64 or np.ndarray

Function values at each given time(s).

var()

The variance.

Returns:

np.float64

The variance value.