mixOmics (version 6.3.2)

tune.mint.splsda: Estimate the parameters of mint.splsda method

Description

Computes Leave-One-Group-Out-Cross-Validation (LOGOCV) scores on a user-input grid to determine optimal values for the sparsity parameters in mint.splsda.

Usage

tune.mint.splsda(X, Y, ncomp = 1, study, test.keepX = c(5, 10, 15), already.tested.X,
dist = "max.dist", measure = "BER", auc = FALSE, progressBar = TRUE,
scale = TRUE, tol = 1e-06, max.iter = 100, near.zero.var = FALSE, light.output = TRUE )

Arguments

X

numeric matrix of predictors. NAs are allowed.

Y

Outcome. Numeric vector or matrix of responses (for multi-response models)

ncomp

Number of components to include in the model (see Details). Default to 1

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 \(X\) data set

already.tested.X

if ncomp > 1 Numeric vector indicating the number of variables to select from the \(X\) data set on the firsts components

dist

only applies to an object inheriting from "plsda" or "splsda" to evaluate the classification performance of the model. Should be a subset of "max.dist", "centroids.dist", "mahalanobis.dist". Default is "all". See predict.

measure

Two misclassification measure are available: overall misclassification error overall or the Balanced Error Rate BER

auc

if TRUE calculate the Area Under the Curve (AUC) performance of the model.

progressBar

by default set to TRUE to output the progress bar of the computation.

scale

boleean. If scale = TRUE, each block is standardized to zero means and unit variances (default: TRUE)

tol

Convergence stopping value.

max.iter

integer, the maximum number of iterations.

near.zero.var

boolean, see the internal nearZeroVar function (should be set to TRUE in particular for data with many zero values). Default value is FALSE

light.output

if set to FALSE, the prediction/classification of each sample for each of test.keepX and each comp is returned.

Value

The returned value is a list with components:

error.rate

returns the prediction error for each test.keepX on each component, averaged across all repeats and subsampling folds. Standard deviation is also output. All error rates are also available as a list.

choice.keepX

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

choice.ncomp

returns the optimal number of components for the model fitted with $choice.keepX

error.rate.class

returns the error rate for each level of Y and for each component computed with the optimal keepX

predict

Prediction values for each sample, each test.keepX and each comp.

class

Predicted class for each sample, each test.keepX and each comp.

Details

This function performs a Leave-One-Group-Out-Cross-Validation (LOGOCV), where each of study is left out once. It returns a list of variables of X that were selected on each of the ncomp components. Then, a mint.splsda can be performed with keepX set as the output choice.keepX.

All component \(1:\code{ncomp}\) are tuned, except the first ones for which a already.tested.X is provided. See examples below.

The function outputs the optimal number of components that achieve the best performance based on the overall error rate or BER. The assessment is data-driven and similar to the process detailed in (Rohart et al., 2016), where one-sided t-tests assess whether there is a gain in performance when adding a component to the model. Our experience has shown that in most case, the optimal number of components is the number of categories in Y - 1, but it is worth tuning a few extra components to check (see our website and case studies for more details).

BER is appropriate in case of an unbalanced number of samples per class as it calculates the average proportion of wrongly classified samples in each class, weighted by the number of samples in each class. BER is less biased towards majority classes during the performance assessment.

More details about the prediction distances in ?predict and the supplemental material of the mixOmics article (Rohart et al. 2017).

References

Rohart F, Eslami A, Matigian, N, Bougeard S, L<U+00EA> Cao K-A (2017). MINT: A multivariate integrative approach to identify a reproducible biomarker signature across multiple experiments and platforms. BMC Bioinformatics 18:128.

mixOmics article:

Rohart F, Gautier B, Singh A, L<U+00EA> Cao K-A. mixOmics: an R package for 'omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752

See Also

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

Examples

# NOT RUN {
data(stemcells)
data = stemcells$gene
type.id = stemcells$celltype
exp = stemcells$study

res = mint.splsda(X=data,Y=type.id,ncomp=3,keepX=c(10,5,15),study=exp)
out = tune.mint.splsda(X=data,Y=type.id,ncomp=2,near.zero.var=FALSE,
study=exp,test.keepX=seq(1,10,1))

out$choice.ncomp
out$choice.keepX

# }
# NOT RUN {
out = tune.mint.splsda(X=data,Y=type.id,ncomp=2,near.zero.var=FALSE,
    study=exp,test.keepX=seq(1,10,1))
out$choice.keepX

## only tune component 2 and keeping 10 genes on comp1
out = tune.mint.splsda(X=data,Y=type.id,ncomp=2, study=exp,
already.tested.X = c(10),
test.keepX=seq(1,10,1))
out$choice.keepX

# }
# NOT RUN {
# }