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

Wrapper of all tuning functions.

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 | ```
tune(method,
X,
Y,
multilevel,
ncomp,
study, # mint.splsda
test.keepX = c(5, 10, 15), # all but pca, rcc
test.keepY = NULL, # rcc, multilevel
already.tested.X, # all but pca, rcc
already.tested.Y, #multilevel
mode = "regression", # multilevel
nrepeat = 1, #multilevel, splsda
grid1 = seq(0.001, 1, length = 5), # rcc
grid2 = seq(0.001, 1, length = 5), # rcc
validation = "Mfold", # all but pca
folds = 10, # all but pca
dist = "max.dist", # all but pca, rcc
measure = c("BER"), # all but pca, rcc
auc = FALSE,
progressBar = TRUE, # all but pca, rcc
near.zero.var = FALSE, # all but pca, rcc
logratio = "none", # all but pca, rcc
center = TRUE, # pca
scale = TRUE, # mint, splsda
max.iter = 100, #pca
tol = 1e-09,
light.output = TRUE # mint, splsda
)
``` |

`method` |
This parameter is used to pass all other argument to the suitable function. |

`X` |
numeric matrix of predictors. |

`Y` |
Either a factor or a class vector for the discrete outcome, or a numeric vector or matrix of continuous responses (for multi-response models). |

`multilevel` |
Design matrix for multilevel anaylis (for repeated measurements) that indicates the repeated measures on each individual, i.e. the individuals ID. See Details. |

`ncomp` |
the number of components to include in the model. |

`study` |
grouping factor indicating which samples are from the same study |

`test.keepX` |
numeric vector for the different number of variables to test from the |

`test.keepY` |
If |

`already.tested.X` |
Optional, if |

`already.tested.Y` |
if |

`mode` |
character string. What type of algorithm to use, (partially) matching
one of |

`nrepeat` |
Number of times the Cross-Validation process is repeated. |

`grid1, grid2` |
vector numeric defining the values of |

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

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

`dist` |
distance metric to use for |

`measure` |
Two misclassification measure are available: overall misclassification error |

`auc` |
if |

`progressBar` |
by default set to |

`near.zero.var` |
boolean, see the internal |

`logratio` |
one of ('none','CLR'). Default to 'none' |

`center` |
a logical value indicating whether the variables should be shifted to be zero centered.
Alternately, a vector of length equal the number of columns of |

`scale` |
a logical value indicating whether the variables should be scaled to have
unit variance before the analysis takes place. The default is |

`max.iter` |
integer, the maximum number of iterations for the NIPALS algorithm. |

`tol` |
a positive real, the tolerance used for the NIPALS algorithm. |

`light.output` |
if set to FALSE, the prediction/classification of each sample for each of |

The `tune`

function called the function `predict`

. more details about most arguments are detailed in `?predict`

.

Also see the help file corresponding to your `method`

, e.g. `tune.splsda`

.
Note that only the arguments used in the tune function corresponding to `method`

are passed on.

Some details on the use of the nrepeat argument are provided in `?perf`

.

More details about the prediction distances in `?predict`

and the supplemental material of the mixOmics article (Rohart et al. 2017). More details about the PLS modes are in `?pls`

.

Depending on the type of analysis performed and the input arguments, a list that may contain:

`error.rate` |
returns the prediction error for each |

`choice.keepX` |
returns the number of variables selected (optimal keepX) on each component. |

`choice.ncomp` |
For supervised models; returns the optimal number of components for the model for each prediction distance using one-sided t-tests that test for a significant difference in the mean error rate (gain in prediction) when components are added to the model. See more details in Rohart et al 2017 Suppl. For more than one block, an optimal ncomp is returned for each prediction framework. |

`error.rate.class` |
returns the error rate for each level of |

`predict` |
Prediction values for each sample, each |

`class` |
Predicted class for each sample, each |

`auc` |
AUC mean and standard deviation if the number of categories in |

`cor.value` |
only if multilevel analysis with 2 factors: correlation between latent variables. |

Florian Rohart

DIABLO:

Singh A., Gautier B., Shannon C., Vacher M., Rohart F., Tebbutt S. and Lê Cao K.A. (2016). DIABLO - multi omics integration for biomarker discovery.

mixOmics article:

Rohart F, Gautier B, Singh A, Lê Cao K-A. mixOmics: an R package for 'omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752

MINT:

Rohart F, Eslami A, Matigian, N, Bougeard S, Lê Cao K-A (2017). MINT: A multivariate integrative approach to identify a reproducible biomarker signature across multiple experiments and platforms. BMC Bioinformatics 18:128.

PLS and PLS citeria for PLS regression:
Tenenhaus, M. (1998). *La regression PLS: theorie et pratique*. Paris: Editions Technic.

Chavent, Marie and Patouille, Brigitte (2003). Calcul des coefficients de regression et du PRESS en regression PLS1. *Modulad n*, **30** 1-11. (this is the formula we use to calculate the Q2 in perf.pls and perf.spls)

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.

sparse PLS regression mode:

Lê Cao, K. A., Rossouw D., Robert-Granie, 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.

One-sided t-tests (suppl material):

Rohart F, Mason EA, Matigian N, Mosbergen R, Korn O, Chen T, Butcher S, Patel J, Atkinson K, Khosrotehrani K, Fisk NM, Lê Cao K-A&, Wells CA& (2016). A Molecular Classification of Human Mesenchymal Stromal Cells. PeerJ 4:e1845.

`tune.rcc`

, `tune.mint.splsda`

,
`tune.pca`

,
`tune.splsda`

, `tune.splslevel`

and http://www.mixOmics.org for more details.

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 | ```
## sPLS-DA
## Not run:
data(breast.tumors)
X <- breast.tumors$gene.exp
Y <- as.factor(breast.tumors$sample$treatment)
tune= tune(method = "splsda", X, Y, ncomp=1, nrepeat=10, logratio="none",
test.keepX = c(5, 10, 15), folds=10, dist="max.dist", progressBar = TRUE)
plot(tune)
## End(Not run)
## mint.splsda
## Not run:
data(stemcells)
data = stemcells$gene
type.id = stemcells$celltype
exp = stemcells$study
out = tune(method="mint.splsda", X=data,Y=type.id, ncomp=2, study=exp, test.keepX=seq(1,10,1))
out$choice.keepX
plot(out)
## End(Not run)
``` |

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