rmout: Remove outliers using subject-wise Brier scores

Description Usage Arguments Value References See Also

View source: R/TMS_Classifier.R

Description

Detect and remove outliers from a dataset using per subject Brier scores (Brier, 1950).

Usage

1
rmout(data, status = 10, k = 5, b = 1)

Arguments

data

A matrix or data.frame containing subjects as rows and TMS parameters as columns. A further column specifying subject diagnosis is required to run RFC cross-validation.

status

Numeric value specifying subject diagnosis (default status = 10).

k

Number of cross-validation cycles (default k = 5).

b

Maximum Brier score beyond which a subject is considered as an outlier. By default, b = 1. If b is set to NULL, outliers will be automatically set to Q3 + 1.5*(Q3 - Q1).

Value

The input data.frame without outliers.

References

Brier GW (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1):1-3. https://doi.org/10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2

See Also

evaluation


fernandoPalluzzi/tmsClassifier documentation built on Feb. 3, 2021, 12:31 p.m.