Usage¶

The usage heavily depends on our application but these are the most probable usecases.

Calculation of local IDR¶

The calculation if the local idr can be done with an sklearn like interface.

To calculate the IDR the IDR needs to be fitted to the data.

idr_obj = pyidr.IDR(mean=0.1, var=0.4, maxiter=100) idr_obj.fit(dataset1, dataset2)

Warning

Significant signals are expected to have large values in both datasets. In case that smaller values represent higher significance (e.g. p-value), a monotonic transformation needs to be applied to reverse the order before using this function, for example,`-log(p-value)`.

During the initialization of the class the default parameters can be changed. Afterwards you can get the local IDR by using:

local_idr = idr_obj.predict(dataset1,dataset2)

or in short:

idr_obj = pyidr.IDR(mean=0.1, var=0.4, maxiter=100)
local_idr = idr_obj.fit_predict(dataset1,dataset2)

Now you can get the proportion of the reproducibility component or decay point:

proportion = idr_obj.proportion

Diagnostic correspondence profiles¶

For diagnostic purposes you can visualize plots as in [LI2011] using the CorrespondenceProfile class:
import pyidr
profile = pyidr.CorrespondenceProfile(rankx, ranky, t)
profile.plot_diagnostics()

Warning

Large values need to be significant signals. If small values represent significant signals, rank the signals reversely (e.g. by ranking negative values) and use this as rankx and ranky.

For for deeper information you can get several diagnostic values by using:

profile.get_psi(scale=scale)
profile.get_dpsi(scale=scale)

scale is here a boolean indicating if the resulting values should be scaled by the number of samples used for calculation.