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 29 30  tune(
method,
X,
Y,
multilevel = NULL,
ncomp,
study,
test.keepX = c(5, 10, 15),
test.keepY = NULL,
already.tested.X,
already.tested.Y,
mode = c("regression", "canonical", "invariant", "classic"),
nrepeat = 1,
grid1 = seq(0.001, 1, length = 5),
grid2 = seq(0.001, 1, length = 5),
validation = "Mfold",
folds = 10,
dist = "max.dist",
measure = ifelse(method == "spls", "MSE", "BER"),
auc = FALSE,
progressBar = FALSE,
near.zero.var = FALSE,
logratio = c("none", "CLR"),
center = TRUE,
scale = TRUE,
max.iter = 100,
tol = 1e09,
light.output = TRUE,
cpus = 1
)

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 multiresponse 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 X data set 
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 CrossValidation 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 crossvalidation. See Details. 
dist 
distance metric to estimate the
classification error rate, should be a subset of 
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. 
tol 
Numeric, convergence tolerance criteria. 
light.output 
if set to FALSE, the prediction/classification of each
sample for each of 
cpus 
Integer, number of cores to use for parallel processing.
Currently only available for 
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 onesided ttests 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, Francois Bartolo, KimAnh Lê Cao, Al J Abadi
Singh A., Shannon C., Gautier B., Rohart F., Vacher M., Tebbutt S. and Lê Cao K.A. (2019), DIABLO: an integrative approach for identifying key molecular drivers from multiomics assays, Bioinformatics, Volume 35, Issue 17, 1 September 2019, Pages 3055–3062.
mixOmics article:
Rohart F, Gautier B, Singh A, Lê Cao KA. 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 KA (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, 30 111. (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), 422429.
sparse PLS regression mode:
Lê Cao, K. A., Rossouw D., RobertGranie, 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.
Onesided ttests (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 KA&, 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  ## sPLSDA
data(breast.tumors)
X < breast.tumors$gene.exp
Y < as.factor(breast.tumors$sample$treatment)
tune= tune(method = "splsda", X, Y, ncomp=1, nrepeat=10, logratio="none",
test.keepX = c(5, 10, 15), folds=10, dist="max.dist", progressBar = TRUE)
plot(tune)
## Not run:
## mint.splsda
data(stemcells)
data = stemcells$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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