plsda: Partial Least Squares Discriminant Analysis (PLS-DA).
In mixOmics: Omics Data Integration Project

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

Function to perform standard Partial Least Squares regression to classify samples.

plsda(
  X,
  Y,
  ncomp = 2,
  scale = TRUE,
  tol = 1e-06,
  max.iter = 100,
  near.zero.var = FALSE,
  logratio = c("none", "CLR"),
  multilevel = NULL,
  all.outputs = TRUE
)

`X`	numeric matrix of predictors with the rows as individual observations. missing values (`NA`s) are allowed.
`Y`	a factor or a class vector for the discrete outcome.
`ncomp`	Positive Integer. The number of components to include in the model. Default to 2.
`scale`	Logical. If scale = TRUE, each block is standardized to zero means and unit variances (default: TRUE)
`tol`	Positive numeric used as convergence criteria/tolerance during the iterative process. Default to `1e-06`.
`max.iter`	Integer, the maximum number of iterations. Default to 100.
`near.zero.var`	Logical, see the internal `nearZeroVar` function (should be set to TRUE in particular for data with many zero values). Setting this argument to FALSE (when appropriate) will speed up the computations. Default value is FALSE.
`logratio`	Character, one of ('none','CLR') specifies the log ratio transformation to deal with compositional values that may arise from specific normalisation in sequencing data. Default to 'none'. See `?logratio.transfo` for details.
`multilevel`	sample information for multilevel decomposition for repeated measurements. A numeric matrix or data frame indicating the repeated measures on each individual, i.e. the individuals ID. See examples in `?splsda`.
`all.outputs`	Logical. Computation can be faster when some specific (and non-essential) outputs are not calculated. Default = `TRUE`.

plsda function fit PLS models with 1,...,ncomp components to the factor or class vector Y. The appropriate indicator matrix is created.

Logratio transformation and multilevel analysis are performed sequentially as internal pre-processing step, through logratio.transfo and withinVariation respectively. Logratio can only be applied if the data do not contain any 0 value (for count data, we thus advise the normalise raw data with a 1 offset).

The type of deflation used is 'regression' for discriminant algorithms. i.e. no deflation is performed on Y.

plsda returns an object of class "plsda", a list that contains the following components:

`X`	the centered and standardized original predictor matrix.
`Y`	the centered and standardized indicator response vector or matrix.
`ind.mat`	the indicator matrix.
`ncomp`	the number of components included in the model.
`variates`	list containing the `X` and `Y` variates.
`loadings`	list containing the estimated loadings associated to each component/variate. The loading weights multiplied with the deflated (residual) matrix gives the variate.
`loadings.stars`	list containing the estimated loadings associated to each component/variate. The loading weights are projected so that when multiplied with the original matrix we obtain the variate.
`names`	list containing the names to be used for individuals and variables.
`nzv`	list containing the zero- or near-zero predictors information.
`tol`	the tolerance used in the iterative algorithm, used for subsequent S3 methods
`max.iter`	the maximum number of iterations, used for subsequent S3 methods
`iter`	Number of iterations of the algorithm for each component
`prop_expl_var`	The proportion of the variance explained by each variate / component divided by the total variance in the `data` (after removing the possible missing values) using the definition of 'redundancy'. Note that contrary to `PCA`, this amount may not decrease in the following components as the aim of the method is not to maximise the variance, but the covariance between data sets (including the dummy matrix representation of the outcome variable in case of the supervised approaches).
`mat.c`	matrix of coefficients from the regression of X / residual matrices X on the X-variates, to be used internally by `predict`.
`defl.matrix`	residual matrices X for each dimension.

Ignacio González, Kim-Anh Lê Cao, Florian Rohart, Al J Abadi

On PLSDA: Barker M and Rayens W (2003). Partial least squares for discrimination. Journal of Chemometrics 17(3), 166-173. Perez-Enciso, M. and Tenenhaus, M. (2003). Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach. Human Genetics 112, 581-592. Nguyen, D. V. and Rocke, D. M. (2002). Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 18, 39-50. On log ratio transformation: Filzmoser, P., Hron, K., Reimann, C.: Principal component analysis for compositional data with outliers. Environmetrics 20(6), 621-632 (2009) Lê Cao K.-A., Costello ME, Lakis VA, Bartolo, F,Chua XY, Brazeilles R, Rondeau P. MixMC: Multivariate insights into Microbial Communities. PLoS ONE, 11(8): e0160169 (2016). On multilevel decomposition: Westerhuis, J.A., van Velzen, E.J., Hoefsloot, H.C., Smilde, A.K.: Multivariate paired data analysis: multilevel plsda versus oplsda. Metabolomics 6(1), 119-128 (2010) Liquet, B., Lê Cao K.-A., Hocini, H., Thiebaut, R.: A novel approach for biomarker selection and the integration of repeated measures experiments from two assays. BMC bioinformatics 13(1), 325 (2012)

splsda, summary, plotIndiv, plotVar, predict, perf, mint.block.plsda, block.plsda and http://mixOmics.org for more details.

## First example
data(breast.tumors)
X <- breast.tumors$gene.exp
Y <- breast.tumors$sample$treatment

plsda.breast <- plsda(X, Y, ncomp = 2)
plotIndiv(plsda.breast, ind.names = TRUE, ellipse = TRUE, legend = TRUE)


## Not run: 
## Second example
data(liver.toxicity)
X <- liver.toxicity$gene
Y <- liver.toxicity$treatment[, 4]

plsda.liver <- plsda(X, Y, ncomp = 2)
plotIndiv(plsda.liver, ind.names = Y, ellipse = TRUE, legend =TRUE)

## End(Not run)

Loading required package: MASS
Loading required package: lattice
Loading required package: ggplot2

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