| Multivariate Pattern Analysis in Python

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This content refers to the previous stable release of PyMVPA. Please visit www.pymvpa.org for the most recent version of PyMVPA and its documentation.

# misc.errorfx

## Module: `misc.errorfx`

Inheritance diagram for `mvpa.misc.errorfx`:

Error functions helpers.

PyMVPA can use arbitrary function which takes 2 arguments: predictions and targets and spits out a scalar value. Functions below are for the convinience, and they confirm the agreement that ‘smaller’ is ‘better’

## Classes

### `AUCErrorFx`

class `mvpa.misc.errorfx.``AUCErrorFx`

  Bases: `mvpa.misc.errorfx._ErrorFx`

  Computes the area under the ROC for the given the target and predicted to make the prediction.

### `MeanMismatchErrorFx`

class `mvpa.misc.errorfx.``MeanMismatchErrorFx`

  Bases: `mvpa.misc.errorfx._ErrorFx`

  Computes the percentage of mismatches between some target and some predicted values.

### `RMSErrorFx`

class `mvpa.misc.errorfx.``RMSErrorFx`

  Bases: `mvpa.misc.errorfx._ErrorFx`

  Computes the root mean squared error of some target and some predicted values.

### `RelativeRMSErrorFx`

class `mvpa.misc.errorfx.``RelativeRMSErrorFx`

  Bases: `mvpa.misc.errorfx._ErrorFx`

  Ratio between RMSE and root mean power of target output.

  So it can be considered as a scaled RMSE – perfect reconstruction has values near 0, while no reconstruction has values around 1.0. Word of caution – it is not commutative, ie exchange of predicted and target might lead to completely different answers

### `Variance1SVFx`

class `mvpa.misc.errorfx.``Variance1SVFx`

  Bases: `mvpa.misc.errorfx._ErrorFx`

  Ratio of variance described by the first singular value component.

  Of limited use – left for the sake of not wasting it

## Functions

`mvpa.misc.errorfx.``meanPowerFx`(data)

  Returns mean power

  Similar to var but without demeaning

`mvpa.misc.errorfx.``rootMeanPowerFx`(data)

  Returns root mean power

  to be comparable against RMSE

### Navigation

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