Description Usage Arguments Details Value Author(s) References See Also Examples

Function to evaluate the performance of the fitted sparse PLS, group PLS, sparse group PLS, sparse PLS-DA, group PLS-DA and sparse group PLS-DA models using various criteria.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | ```
## S3 method for class 'sPLS'
perf(object,
criterion = c("all", "MSEP", "R2", "Q2"),
validation = c("Mfold", "loo"),
folds = 10, progressBar = TRUE, setseed = 1,...)
## S3 method for class 'gPLS'
perf(object,
criterion = c("all", "MSEP", "R2", "Q2"),
validation = c("Mfold", "loo"),
folds = 10, progressBar = TRUE, setseed = 1, ...)
## S3 method for class 'sgPLS'
perf(object,
criterion = c("all", "MSEP", "R2", "Q2"),
validation = c("Mfold", "loo"),
folds = 10, progressBar = TRUE,setseed = 1, ...)
## S3 method for class 'sPLSda'
perf(object,
method.predict = c("all", "max.dist", "centroids.dist", "mahalanobis.dist"),
validation = c("Mfold", "loo"),
folds = 10, progressBar = TRUE, ...)
## S3 method for class 'gPLSda'
perf(object,
method.predict = c("all", "max.dist", "centroids.dist", "mahalanobis.dist"),
validation = c("Mfold", "loo"),
folds = 10, progressBar = TRUE, ...)
## S3 method for class 'sgPLSda'
perf(object,
method.predict = c("all", "max.dist", "centroids.dist", "mahalanobis.dist"),
validation = c("Mfold", "loo"),
folds = 10, progressBar = TRUE, ...)
``` |

`object` |
Object of class inheriting from |

`criterion` |
The criteria measures to be calculated (see Details). Can be set to either |

`method.predict` |
only applies to an object inheriting from |

`validation` |
Character. What kind of (internal) validation to use, matching one of |

`folds` |
The folds in the Mfold cross-validation. See Details. |

`progressBar` |
By default set to |

`setseed` |
Integer value to specify the random generator state. |

`...` |
Not used at the moment. |

The method `perf`

has been created by Sebastien Dejean, Ignacio Gonzalez, Amrit Singh and Kim-Anh Le Cao for pls and spls models performed by `mixOmics`

package. Similar code has been adapted for sPLS, gPLS and sgPLS in the package `sgPLS`

.

`perf`

estimates the
mean squared error of prediction (MSEP), *R^2*, and *Q^2* to assess the predictive
performance of the model using M-fold or leave-one-out cross-validation. Note that only the `classic`

, `regression`

and `invariant`

modes can be applied.

If `validation = "Mfold"`

, M-fold cross-validation is performed.
How many folds to generate is selected by specifying the number of folds in `folds`

.
The folds also can be supplied as a list of vectors containing the indexes defining each
fold as produced by `split`

.
If `validation = "loo"`

, leave-one-out cross-validation is performed.

For fitted sPLS-DA, gPLS-DA and sgPLS-DA models, `perf`

estimates the classification error rate
using cross-validation.

Note that the `perf`

function will retrieve the `keepX`

and `keepY`

inputs from the previously run object. The sPLS, gPLS, sgPLS, sPLSda, gPLSda or sgPLSda functions will be run again on several and different subsets of data (the cross-folds) and certainly on different subset of selected features. For sPLS, the MSEP, *R^2*, and *Q^2* criteria are averaged across all folds. A feature stability measure is output for the user to assess how often the variables are selected across all folds. For sPLS-DA, the classification erro rate is averaged across all folds.

`perf`

produces a list with the following components:

`MSEP` |
Mean Square Error Prediction for each |

`R2` |
a matrix of |

`Q2` |
if |

`Q2.total` |
a vector of |

`features` |
a list of features selected across the folds ( |

`error.rate` |
For sPLS-DA, gPLS-DA and sgPLS-DA models, |

Benoit Liquet and Pierre Lafaye de Micheaux

Tenenhaus, M. (1998). *La r\'egression PLS: th\'eorie et pratique*. Paris: Editions Technic.

Le Cao, K.-A., Rossouw, D., Robert-Grani\'e, C. and Besse, P. (2008). A sparse PLS for variable
selection when integrating Omics data. *Statistical Applications in Genetics and Molecular
Biology* **7**, article 35.

Mevik, B.-H., Cederkvist, H. R. (2004). Mean Squared Error of Prediction (MSEP) Estimates for Principal Component
Regression (PCR) and Partial Least Squares Regression (PLSR). *Journal of Chemometrics* **18**(9), 422-429.

`predict`

, `plot.perf`

(from package `mixOmics`

)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ```
## validation for objects of class 'sPLS' (regression)
## Example from mixOmics package
# ----------------------------------------
## Not run:
data(liver.toxicity)
X <- liver.toxicity$gene
Y <- liver.toxicity$clinic
## validation for objects of class 'spls' (regression)
# ----------------------------------------
ncomp <- 7
# first, learn the model on the whole data set
model.spls <- sPLS(X, Y, ncomp = ncomp, mode = 'regression',
keepX = c(rep(5, ncomp)), keepY = c(rep(2, ncomp)))
# with leave-one-out cross validation
set.seed(45)
model.spls.loo.val <- perf(model.spls, validation = "loo")
#Q2 total
model.spls.loo.val$Q2.total
# R2:we can see how the performance degrades when ncomp increases
# results are similar to 5-fold
model.spls.loo.val$R2
## End(Not run)
``` |

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