mixOmics (version 5.1.2)

tune.multilevel: Tuning functions for multilevel analyses

Description

These functions were implemented to help tuning the variable selection parameters in the multilevel analyses.

Usage

tune.multilevel(X, 
                Y = NULL, 
                design , 
                ncomp = 1, 
                test.keepX = c(5, 10, 15), 
                test.keepY = NULL, 
                already.tested.X = NULL, 
                already.tested.Y = NULL, 
                method, mode, dist, 
                validation = c("Mfold", "loo"), 
                folds = 10)

Arguments

X

numeric matrix of predictors. NAs are allowed.

Y

if(method = 'spls') numeric vector or matrix of continuous responses (for multi-response models) NAs are allowed.

design

a numeric matrix or data frame of 2 columns for a one-factor discrete outcome, or of 3 columns for two-factor discrete outcome. The first column indicates the repeated measures on each individual, i.e. the individuals ID.

ncomp

the number of components to include in the model.

test.keepX

numeric vector for the different number of variables to test from the $X$ data set

test.keepY

If method = 'spls', numeric vector for the different number of variables to test from the $Y$ data set

already.tested.X

if ncomp > 1 numeric vector indicating the number of variables to select rom the $X$ data set on the previous ncomp-1 components

already.tested.Y

if method = 'spls' and if(ncomp > 1) numeric vector indicating the number of variables to select rom the $Y$ data set on the previous ncomp-1 components

method

character string. Which multivariate method and type of analysis to choose, matching one of 'splsda' (Discriminant Analysis) or 'spls' (unsupervised integrative analysis). See Details.

mode

In the case of method = 'spls', should be set to 'canonical'. See details.

dist

distance metric to use for splsda to estimate the classification error rate, should be a subset of "centroids.dist", "mahalanobis.dist" or "max.dist" (see Details).

validation

character. What kind of (internal) validation to use, matching one of "Mfold" or "loo" (see below). Default is "Mfold".

folds

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

Value

• Depending on the type of analysis performed, a list that contains:
• error
  cross-validation overall error rate when one-factor sPLS-DA analysis is performed.
• prediction.all
  cross-validation prediction for all samples and for the LAST keepX tested parameter when one-factor sPLS-DA analysis is performed.
• cor.value
  compute the correlation between latent variables for two-factor sPLS-DA analysis or sPLS.

encoding

latin1

Details

This tuning function should be used to tune the parameters in the multilevel function.

If method = 'splsda', a distance metric must be used, see help(predict.splsda) for details about the distances.

For a sPLS-DA one-factor analysis, M-fold cross-validation is performed, internally the training data is decomposed into within-subject variation.

For a sPLS-DA two-factor analysis, the correlation between components from the within-subject variation of X and the cond matrix is computed on the whole data set. The reason why we cannot obtain a corss-validation error rate as for the spls-DA one-factor analysis is because of the dififculty to decompose and predict the within matrices within each fold.

For a sPLS two-factor analysis a sPLS canonical mode is run, and the correlation between components from the within-subject variation of X and Y is computed on the whole data set.

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. By default folds is set to the number of unique individuals.

References

On multilevel analysis: Liquet, B., Le Cao, K.-A., Hocini, H. and Thiebaut, R. (2012) A novel approach for biomarker selection and the integration of repeated measures experiments from two platforms. BMC Bioinformatics 13:325.

Westerhuis, J. A., van Velzen, E. J., Hoefsloot, H. C., and Smilde, A. K. (2010). Multivariate paired data analysis: multilevel PLSDA versus OPLSDA. Metabolomics, 6(1), 119-128.

See Also

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

Examples

## First example: one-factor analysis with sPLS-DA
data(vac18.simulated) # simulated data
  design <- data.frame(sample = vac18.simulated$sample,
                       stimu = vac18.simulated$stimulation)
  
    result.ex1 = tune.multilevel(vac18.simulated$genes,
                               design = design,
                               ncomp=2,
                               test.keepX=c(5, 10, 15), 
                               already.tested.X = c(50),
                               method = 'splsda',
                               dist = 'mahalanobis.dist',
                               validation = 'loo') 
  
  # error rate for the tested parameters est.keepX=c(5, 10, 15)
  result.ex1$error
  # prediction for ncomp = 2 and keepX = c(50, 15) (15 is the last tested parameter)
  result.ex1$prediction.all
  table(vac18.simulated$stimulation, result.ex1$prediction.all)



## Second example: two-factor analysis with sPLS-DA
data(liver.toxicity)
  dose <- as.factor(liver.toxicity$treatment$Dose.Group)
  time <- as.factor(liver.toxicity$treatment$Time.Group)
  # note: we made up those data, pretending they are repeated measurements
  repeat.indiv <- c(1, 2, 1, 2, 1, 2, 1, 2, 3, 3, 4, 3, 4, 3, 4, 4, 5, 6, 5, 5,
                    6, 5, 6, 7, 7, 8, 6, 7, 8, 7, 8, 8, 9, 10, 9, 10, 11, 9, 9,
                    10, 11, 12, 12, 10, 11, 12, 11, 12, 13, 14, 13, 14, 13, 14,
                    13, 14, 15, 16, 15, 16, 15, 16, 15, 16)
  summary(as.factor(repeat.indiv)) # 16 rats, 4 measurements each
  
  design <- data.frame(sample = repeat.indiv,
                       dose = dose,
                       time = time)
  
  result.ex2 = tune.multilevel(liver.toxicity$gene,
                                design = design, 
                                ncomp=2,
                                test.keepX=c(5, 10, 15), 
                                already.tested.X = c(50),
                                method = 'splsda',
                                dist = 'mahalanobis.dist') 
  result.ex2

## Third example: one-factor integrative analysis with sPLS
data(liver.toxicity)
  # note: we made up those data, pretending they are repeated measurements
  repeat.indiv <- c(1, 2, 1, 2, 1, 2, 1, 2, 3, 3, 4, 3, 4, 3, 4, 4, 5, 6, 5, 5,
                    6, 5, 6, 7, 7, 8, 6, 7, 8, 7, 8, 8, 9, 10, 9, 10, 11, 9, 9,
                    10, 11, 12, 12, 10, 11, 12, 11, 12, 13, 14, 13, 14, 13, 14,
                    13, 14, 15, 16, 15, 16, 15, 16, 15, 16)
  summary(as.factor(repeat.indiv)) # 16 rats, 4 measurements each
  
  # here we are only interested in a one level variation split since spls is an unsupervised method
  design <- data.frame(sample = repeat.indiv)
  
  result.ex3 = tune.multilevel(X = liver.toxicity$gene, Y = liver.toxicity$clinic, 
                                design = design,
                                mode = 'canonical',
                                ncomp=2,
                                test.keepX=c(5, 10, 15), 
                                test.keepY=c(2,3), 
                                already.tested.X = c(50), already.tested.Y = c(5),
                                method = 'spls') 
  
  result.ex3