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
params may include parameter-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.
Package: statistics