LogNormalFitter¶

class lifelines.fitters.log_normal_fitter.LogNormalFitter(*args, **kwargs)¶

This class implements an Log Normal model for univariate data. The model has parameterized form:

\[S(t) = 1 - \Phi\left(\frac{\log(t) - \mu}{\sigma}\right), \;\; \sigma >0\]

where \(\Phi\) is the CDF of a standard normal random variable. This implies the cumulative hazard rate is

\[H(t) = -\log\left(1 - \Phi\left(\frac{\log(t) - \mu}{\sigma}\right)\right)\]

For inference, our null hypothesis is that mu=0.0, and sigma=1.0.

After calling the .fit method, you have access to properties like: survival_function_, mu_, sigma_. A summary of the fit is available with the method print_summary()

Parameters:: alpha (float, optional (default=0.05)) – the level in the confidence intervals.

cumulative_hazard_¶

The estimated cumulative hazard (with custom timeline if provided)

Type:: DataFrame

hazard_¶

The estimated hazard (with custom timeline if provided)

Type:: DataFrame

survival_function_¶

The estimated survival function (with custom timeline if provided)

Type:: DataFrame

cumulative_density_¶

The estimated cumulative density function (with custom timeline if provided)

Type:: DataFrame

density_¶

The estimated density function (PDF) (with custom timeline if provided)

Type:: DataFrame

variance_matrix_¶

The variance matrix of the coefficients

Type:: DataFrame

median_survival_time_¶

The median time to event

Type:: float

mu_¶

The fitted parameter in the model

Type:: float

sigma_¶

The fitted parameter in the model

Type:: float

durations¶

The durations provided

Type:: array

event_observed¶

The event_observed variable provided

Type:: array

timeline¶

The time line to use for plotting and indexing

Type:: array

entry¶

The entry array provided, or None

Type:: array or None

property median_survival_time_: float¶: Return the unique time point, t, such that S(t) = 0.5. This is the “half-life” of the population, and a robust summary statistic for the population, if it exists.

mu_: float¶

percentile(p) → float¶

Return the unique time point, t, such that S(t) = p.

Parameters:: p (float)

sigma_: float¶