fit_line

scikitplot.probscale.fit_line(x, y, xhat=None, fitprobs=None, fitlogs=None, dist=None, estimate_ci=False, niter=10000, alpha=0.05)

Fits a line to x-y data in various forms (linear, log, prob scales).

Parameters:

x, yarray-like

Independent and dependent data, respectively.

xhatarray-like, optional

The values at which yhat should should be estimated. If not provided, falls back to the sorted values of x.

fitprobs, fitlogsstr, optional.

Defines how data should be transformed. Valid values are ‘x’, ‘y’, or ‘both’. If using fitprobs, variables should be expressed as a percentage, i.e., for a probability transform, data will be transformed with lambda x: dist.ppf(x / 100.). For a log transform, lambda x: numpy.log(x). Take care to not pass the same value to both fitlogs and figprobs as both transforms will be applied.

distdistribution, optional

A fully-spec’d scipy.stats distribution-like object such that dist.ppf and dist.cdf can be called. If not provided, defaults to a minimal implementation of scipy.stats.norm.

estimate_cibool, optional (False)

Estimate and draw a confidence band around the best-fit line using a percentile bootstrap.

niterint, optional (default = 10000)

Number of bootstrap iterations if estimate_ci is provided.

alphafloat, optional (default = 0.05)

The confidence level of the bootstrap estimate.

Returns:

xhat, yhatnumpy arrays

Linear model estimates of x and y.

resultsdict

Dictionary of linear fit results. Keys include:

• slope

• intercept

• yhat_lo (lower confidence interval of the estimated y-vals)

• yhat_hi (upper confidence interval of the estimated y-vals)