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

In mixOmicsTeam/mixOmics: Omics Data Integration Project

Estimate the parameters of mint.plsda method

Description

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

Usage

tune.mint.plsda(
  X,
  Y,
  ncomp = 1,
  study,
  scale = TRUE,
  tol = 1e-06,
  max.iter = 100,
  near.zero.var = FALSE,
  signif.threshold = 0.01,
  dist = c("max.dist", "centroids.dist", "mahalanobis.dist"),
  auc = FALSE,
  progressBar = 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
scale	Logical. 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	Logical, see the internal nearZeroVar function (should be set to TRUE in particular for data with many zero values). Default value is FALSE
signif.threshold	numeric between 0 and 1 indicating the significance threshold required for improvement in error rate of the components. Default to 0.01.
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.
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.
light.output	if set to FALSE, the prediction/classification of each sample for each of test.keepX and each comp is returned.

Details

This function performs a Leave-One-Group-Out-Cross-Validation (LOGOCV), where each of study is left out once.

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).

Value

The returned value is a list with components:

study.specific.error	A list that gives BER, overall error rate and error rate per class, for each study
global.error	A list that gives BER, overall error rate and error rate per class for all samples
predict	A list of length ncomp that produces the predicted values of each sample for each class
class	A list which gives the predicted class of each sample for each dist and each of the ncomp components. Directly obtained from the predict output.
auc	AUC values
auc.study	AUC values for each study in mint models

.

Author(s)

Florian Rohart, Al J Abadi

References

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.

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

See Also

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

Examples

# set up data
data(stemcells)
data <- stemcells$gene
type.id <- stemcells$celltype
exp <- stemcells$study

# tune number of components
tune_res <- tune.mint.plsda(X = data,Y = type.id, ncomp=5, 
                             near.zero.var=FALSE,
                             study=exp)

plot(tune_res)
tune_res$choice.ncomp # 1 component

mixOmicsTeam/mixOmics documentation built on Dec. 3, 2024, 11:15 p.m.