Statistics: crossval

### Function Reference: `crossval`

statistics: results = crossval (f, X, y)
statistics: results = crossval (f, X, y, name, value)

Perform cross validation on given data.

f should be a function that takes 4 inputs xtrain, ytrain, xtest, ytest, fits a model based on xtrain, ytrain, applies the fitted model to xtest, and returns a goodness of fit measure based on comparing the predicted and actual ytest. `crossval` returns an array containing the values returned by f for every cross-validation fold or resampling applied to the given data.

X should be an n by m matrix of predictor values

y should be an n by 1 vector of predicand values

Optional arguments may include name-value pairs as follows:

`"KFold"`

Divide set into k equal-size subsets, using each one successively for validation.

`"HoldOut"`

Divide set into two subsets, training and validation. If the value k is a fraction, that is the fraction of values put in the validation subset (by default k=0.1); if it is a positive integer, that is the number of values in the validation subset.

`"LeaveOut"`

Leave-one-out partition (each element is placed in its own subset). The value is ignored.

`"Partition"`

The value should be a cvpartition object.

`"Given"`

The value should be an n by 1 vector specifying in which partition to put each element.

`"stratify"`

The value should be an n by 1 vector containing class designations for the elements, in which case the `"KFold"` and `"HoldOut"` partitionings attempt to ensure each partition represents the classes proportionately.

`"mcreps"`

The value should be a positive integer specifying the number of times to resample based on different partitionings. Currently only works with the partition type `"HoldOut"`.

Only one of `"KFold"`, `"HoldOut"`, `"LeaveOut"`, `"Given"`, `"Partition"` should be specified. If none is specified, the default is `"KFold"` with k = 10.

See also: cvpartition

Source Code: crossval