# cv.eppls: Cross validation for peplos In Renvlp: Computing Envelope Estimators

 cv.eppls R Documentation

## Cross validation for peplos

### Description

Compute the prediction error for the Envelope-based Partial Partial Least Squares estimator using m-fold cross validation.

### Usage

``````cv.eppls(X1, X2, Y, u, m, nperm)
``````

### Arguments

 `X1` Predictors of main interest. An n by p1 matrix, n is the number of observations, and p1 is the number of main predictors. The predictors can be univariate or multivariate, discrete or continuous. `X2` Covariates, or predictors not of main interest. An n by p2 matrix, p2 is the number of covariates. `Y` Multivariate responses. An n by r matrix, r is the number of responses and n is number of observations. The responses must be continuous variables. `u` Dimension of the envelope. An integer between 0 and r. `m` A positive integer that is used to indicate m-fold cross validation. `nperm` A positive integer indicating number of permutations of the observations, m-fold cross validation is run on each permutation.

### Details

This function computes prediction errors using m-fold cross validation. For a fixed dimension u, the data is randomly partitioned into m parts, each part is in turn used for testing for the prediction performance while the rest m-1 parts are used for training. This process is repeated for `nperm` times, and average prediction error is reported. As Y is multivariate, the identity inner product is used for computing the prediction errors.

### Value

The output is a real nonnegative number.

 `cvPE` The prediction error estimated by m-fold cross validation.

### Examples

``````data(amitriptyline)

Y <- amitriptyline[ , 1:2]
X1 <- amitriptyline[ , 4:7]
X2 <- amitriptyline[ , 3]

m <- 5
nperm <- 50
## Not run: cvPE <- cv.eppls(X1, X2, Y, 2, m, nperm)
## Not run: cvPE
``````

Renvlp documentation built on Oct. 11, 2023, 1:06 a.m.