cvMatrixFrobeniusLoss {cvCovEst} | R Documentation |

## Cross-Validation Function for Matrix Frobenius Loss

### Description

`cvMatrixFrobeniusLoss()`

evaluates the matrix Frobenius
loss over a `fold`

object (from 'origami'
(Coyle and Hejazi 2018)). This loss function is equivalent to that
presented in `cvFrobeniusLoss()`

in terms of estimator
selections, but is more computationally efficient.

### Usage

```
cvMatrixFrobeniusLoss(fold, dat, estimator_funs, estimator_params = NULL)
```

### Arguments

`fold` |
A |

`dat` |
A |

`estimator_funs` |
An |

`estimator_params` |
A named |

### Value

A `tibble`

providing information on estimators,
their hyperparameters (if any), and their matrix Frobenius loss evaluated
on a given `fold`

.

### References

Coyle J, Hejazi N (2018).
“origami: A Generalized Framework for Cross-Validation in R.”
*Journal of Open Source Software*, **3**(21), 512.
doi:10.21105/joss.00512.

### Examples

```
library(MASS)
library(origami)
library(rlang)
# generate 10x10 covariance matrix with unit variances and off-diagonal
# elements equal to 0.5
Sigma <- matrix(0.5, nrow = 10, ncol = 10) + diag(0.5, nrow = 10)
# sample 50 observations from multivariate normal with mean = 0, var = Sigma
dat <- mvrnorm(n = 50, mu = rep(0, 10), Sigma = Sigma)
# generate a single fold using MC-cv
resub <- make_folds(dat,
fold_fun = folds_vfold,
V = 2
)[[1]]
cvMatrixFrobeniusLoss(
fold = resub,
dat = dat,
estimator_funs = rlang::quo(c(
linearShrinkEst, thresholdingEst, sampleCovEst
)),
estimator_params = list(
linearShrinkEst = list(alpha = c(0, 1)),
thresholdingEst = list(gamma = c(0, 1))
)
)
```

*cvCovEst*version 1.2.2 Index]