Description Usage Arguments Details Value Author(s) References See Also Examples
Wrapper of all tuning functions.
1 2 3 4 5 6 7 8 | tune(method, X, Y, multilevel = NULL, ncomp, study, test.keepX = c(5,
10, 15), test.keepY = NULL, already.tested.X, already.tested.Y,
mode = "regression", nrepeat = 1, grid1 = seq(0.001, 1, length =
5), grid2 = seq(0.001, 1, length = 5), validation = "Mfold",
folds = 10, dist = "max.dist", measure = c("BER"), auc = FALSE,
progressBar = TRUE, near.zero.var = FALSE, logratio = "none",
center = TRUE, scale = TRUE, max.iter = 100, tol = 1e-09,
light.output = TRUE)
|
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 multi-response 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 Cross-Validation 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 cross-validation. See Details. |
dist |
distance metric to use for |
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 for the NIPALS algorithm. |
tol |
a positive real, the tolerance used for the NIPALS algorithm. |
light.output |
if set to FALSE, the prediction/classification of each
sample for each of |
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 one-sided t-tests 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
DIABLO:
Singh A., Gautier B., Shannon C., Vacher M., Rohart F., Tebbutt S. and Lê Cao K.A. (2016). DIABLO - multi omics integration for biomarker discovery.
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
MINT:
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.
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 1-11. (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), 422-429.
sparse PLS regression mode:
Lê Cao, K. A., Rossouw D., Robert-Granie, 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.
One-sided t-tests (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 K-A&, 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 24 | ## sPLS-DA
## Not run:
library(mixOmics.data)
X <- breast.tumors$gene.exp
Y <- as.factor(breast.tumors$sample$treatment)
tune= tune(method = "splsda", X, Y, ncomp=1, nrepeat=10, logratio="none",
test.keepX = c(5, 10, 15), folds=10, dist="max.dist", progressBar = TRUE)
plot(tune)
## mint.splsda
data = stemcells$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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