perf: Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA,...
In mixOmics: Omics Data Integration Project
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
Function to evaluate the performance of the fitted PLS, sparse PLS,
PLS-DA, sparse PLS-DA, MINT (mint.splsda) and DIABLO (block.splsda) models using various criteria.
Usage
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## S3 method for class 'pls'
perf(object,validation = c("Mfold", "loo"),
folds = 10, progressBar = TRUE, ...)
## S3 method for class 'spls'
perf(object,validation = c("Mfold", "loo"),
folds = 10, progressBar = TRUE, ...)
## S3 method for class 'plsda'
perf(object,
dist = c("all", "max.dist", "centroids.dist", "mahalanobis.dist"),
validation = c("Mfold", "loo"),
folds = 10, nrepeat =1, auc = FALSE, progressBar = TRUE, cpus, ...)
## S3 method for class 'splsda'
perf(object,
dist = c("all", "max.dist", "centroids.dist", "mahalanobis.dist"),
validation = c("Mfold", "loo"),
folds = 10, nrepeat =1, auc = FALSE, progressBar = TRUE, cpus, ...)
## S3 method for class 'mint.splsda'
perf(object,
dist = c("all", "max.dist", "centroids.dist", "mahalanobis.dist"),
auc = FALSE, progressBar = TRUE, ...)
## S3 method for class 'sgccda'
perf(object,
dist = c("all", "max.dist", "centroids.dist", "mahalanobis.dist"),
validation = c("Mfold", "loo"),
folds = 10, nrepeat =1, cpus, ...)
Arguments
object
object of class inheriting from "pls", "plsda",
"spls", "splsda" or "mint.splsda". The function will retrieve some key parameters stored in that object.
dist
only applies to an object inheriting from "plsda", "splsda" or "mint.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.
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.
nrepeat
Number of times the Cross-Validation process is repeated. This is an important argument to ensure the estimation of the performance to be as accurate as possible.
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.
cpus
Number of cpus to use when running the code in parallel.
...
not used
Details
Procedure. The process of evaluating the performance of a fitted model object is similar for all PLS-derived methods; a cross-validation approach is used to fit the method of object on folds-1 subsets of the data and then to predict on the subset left out. Different measures of performance are available depending on the model. Parameters such as logratio, multilevel, keepX or keepY are retrieved from object.
Parameters. If validation = "Mfold", M-fold cross-validation is performed.
folds specifies the number of folds to generate.
The folds also can be supplied as a list of vectors containing the indexes defining each
fold as produced by split. When using validation = "Mfold", make sure that you repeat the process several times (as the results will be highly dependent on the random splits and the sample size).
If validation = "loo", leave-one-out cross-validation is performed (in that case, there is no need to repeat the process).
Measures of performance. For fitted PLS and sPLS regression models, perf estimates the
mean squared error of prediction (MSEP), R^2, and Q^2 to assess the predictive
perfity of the model using M-fold or leave-one-out cross-validation. Note that only the classic, regression and invariant modes can be applied.
For sPLS, the MSEP, R^2, and Q^2 criteria are averaged across all folds.
The Q2.total combines the Q^2 values for each Y variable and provides an overall assessment of fit. According to Tenenhaus (1998) the rule of thumb to decide whether a component contributes significantly to the prediction is when its Q2.total is still greater than 0.0975. However whe dealing with a large number of variables, especially in X, we have often experienced Q2.total values that do not decrease. The sPLS method also may not reach this cutoff, depending on the type of data and problem.
Note that for PLS and sPLS objects, perf is performed on the pre-processed data after log ratio transform and multilevel analysis, when applied.
Sparse methods. The sPLS, sPLS-DA and sgccda functions are run on several and different subsets of data (the cross-folds) and will certainly lead to different subset of selected features. Those are summarised in the output features$stable (see output Value below) to assess how often the variables are selected across all folds.
Note that for PLS-DA and sPLS-DA objects, perf is performed on the original data, i.e. before the pre-processing step of the log ratio transform and multilevel analysis, if any. In addition for these methods, the classification error rate is averaged across all folds.
The mint.sPLS-DA function estimates errors based on Leave-one-group-out cross validation (where each levels of object$study is left out (and predicted) once) and provides study-specific outputs (study.specific.error) as well as global outputs (global.error).
AUROC. For PLS-DA, sPLS-DA, mint.PLS-DA and mint.sPLS-DA methods: if auc=TRUE, Area Under the Curve (AUC) values are calculated from the predicted scores obtained from the predict function applied to the internal test sets in the cross-validation process, either for all samples or for study-specific samples (for mint models). Therefore we minimise the risk of overfitting. See auroc for more details. Our multivariate supervised methods already use a prediction threshold based on distances (see predict) that optimally determine class membership of the samples tested. As such AUC and ROC are not needed to estimate the performance of the model. We provide those outputs as complementary performance measures. See more details in our mixOmics article.
Prediction distances. See details from ?predict, and also our supplemental material in the mixOmics article.
Repeats of the CV-folds. Repeated cross-validation implies that the whole CV process is repeated a number of times (nrepeat) to reduce variability across the different subset partitions. In the case of Leave-One-Out CV (validation = 'loo'), each sample is left out once (folds = N is set internally) and therefore nrepeat is by default 1.
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 PLS modes in ?pls.
Value
For PLS and sPLS models, perf produces a list with the following components:
MSEP
Mean Square Error Prediction for each Y variable, only applies to object inherited from "pls", and "spls".
R2
a matrix of R^2 values of the Y-variables for models
with 1, … ,ncomp components, only applies to object inherited from "pls", and "spls".
Q2
if Y containts one variable, a vector of Q^2 values else a list with
a matrix of Q^2 values for each Y-variable. Note that in the specific case of an sPLS model, it is better to have a look at the Q2.total criterion, only applies to object inherited from "pls", and "spls"
Q2.total
a vector of Q^2-total values for models with 1, … ,ncomp components, only applies to object inherited from "pls", and "spls"
features
a list of features selected across the folds ($stable.X and $stable.Y) for the keepX and keepY parameters from the input object.
error.rate
For PLS-DA and sPLS-DA models, perf produces a matrix of classification error rate estimation.
The dimensions correspond to the components in the model and to the prediction method used, respectively. Note that error rates reported in any component include the performance of the model in earlier components for the specified keepX parameters (e.g. error rate reported for component 3 for keepX = 20 already includes the fitted model on components 1 and 2 for keepX = 20). For more advanced usage of the perf function, see www.mixomics.org/methods/spls-da/ and consider using the predict function.
auc
Averaged AUC values over the nrepeat
For mint.splsda models, perf produces the following outputs:
study.specific.error
A list that gives BER, overall error rate and error rate per class, for each study
global.error
A list that gives BER, overall error rate and error rate per class for all samples
predict
A list of length ncomp that produces the predicted values of each sample for each class
class
A list which gives the predicted class of each sample for each dist and each of the ncomp components. Directly obtained from the predict output.
auc
AUC values
auc.study
AUC values for each study
For sgccda models, perf produces the following outputs:
error.rate
Prediction error rate for each block of object$X and each dist
error.rate.per.class
Prediction error rate for each block of object$X, each dist and each class
predict
Predicted values of each sample for each class, each block and each component
class
Predicted class of each sample for each block, each dist, each component and each nrepeat
features
a list of features selected across the folds ($stable.X and $stable.Y) for the keepX and keepY parameters from the input object.
AveragedPredict.class
if more than one block, returns the average predicted class over the blocks (averaged of the Predict output and prediction using the max.dist distance)
AveragedPredict.error.rate
if more than one block, returns the average predicted error rate over the blocks (using the AveragedPredict.class output)
WeightedPredict.class
if more than one block, returns the weighted predicted class over the blocks (weighted average of the Predict output and prediction using the max.dist distance)
WeightedPredict.error.rate
if more than one block, returns the weighted average predicted error rate over the blocks (using the WeightedPredict.class output)
MajorityVote
if more than one block, returns the majority class over the blocks. NA for a sample means that there is no consensus on the predicted class for this particular sample over the blocks.
MajorityVote.error.rate
if more than one block, returns the error rate of the MajorityVote output
WeightedVote
if more than one block, returns the weighted majority class over the blocks. NA for a sample means that there is no consensus on the predicted class for this particular sample over the blocks.
WeightedVote.error.rate
if more than one block, returns the error rate of the WeightedVote output
weights
Returns the weights of each block used for the weighted predictions, for each nrepeat and each fold
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.
Author(s)
Ignacio González, Amrit Singh, Kim-Anh Lê Cao, Benoit Gautier, Florian Rohart.
References
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.
See Also
predict, nipals, plot.perf, auroc and www.mixOmics.org for more details.
Examples
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## Not run:
## validation for objects of class 'pls' (regression)
# ----------------------------------------
data(liver.toxicity)
X <- liver.toxicity$gene
Y <- liver.toxicity$clinic
# try tune the number of component to choose
# ---------------------
# first learn the full model
liver.pls <- pls(X, Y, ncomp = 10)
# with 5-fold cross validation: we use the same parameters as in model above
# but we perform cross validation to compute the MSEP, Q2 and R2 criteria
# ---------------------------
liver.val <- perf(liver.pls, validation = "Mfold", folds = 5)
# Q2 total should decrease until it reaches a threshold
liver.val$Q2.total
# ncomp = 2 is enough
plot(liver.val$Q2.total, type = 'l', col = 'red', ylim = c(-0.5, 0.5),
xlab = 'PLS components', ylab = 'Q2 total')
abline(h = 0.0975, col = 'darkgreen')
legend('topright', col = c('red', 'darkgreen'),
legend = c('Q2 total', 'threshold 0.0975'), lty = 1)
title('Liver toxicity PLS 5-fold, Q2 total values')
#have a look at the other criteria
# ----------------------
# R2
liver.val$R2
matplot(t(liver.val$R2), type = 'l', xlab = 'PLS components', ylab = 'R2 for each variable')
title('Liver toxicity PLS 5-fold, R2 values')
# MSEP
liver.val$MSEP
matplot(t(liver.val$MSEP), type = 'l', xlab = 'PLS components', ylab = 'MSEP for each variable')
title('Liver toxicity PLS 5-fold, MSEP values')
## validation for objects of class 'spls' (regression)
# ----------------------------------------
ncomp = 7
# first, learn the model on the whole data set
model.spls = spls(X, Y, ncomp = ncomp, mode = 'regression',
keepX = c(rep(10, ncomp)), keepY = c(rep(4,ncomp)))
# with leave-one-out cross validation
##set.seed(45)
model.spls.val <- perf(model.spls, validation = "Mfold", folds = 5 )#validation = "loo")
#Q2 total
model.spls.val$Q2.total
# R2:we can see how the performance degrades when ncomp increases
model.spls.val$R2
plot(model.spls.val, criterion="R2", type = 'l')
plot(model.spls.val, criterion="Q2", type = 'l')
## validation for objects of class 'splsda' (classification)
# ----------------------------------------
data(srbct)
X <- srbct$gene
Y <- srbct$class
ncomp = 2
srbct.splsda <- splsda(X, Y, ncomp = ncomp, keepX = rep(10, ncomp))
# with Mfold
# ---------
set.seed(45)
error <- perf(srbct.splsda, validation = "Mfold", folds = 8,
dist = "all", auc = TRUE)
error
error$auc
plot(error)
# parallel code
set.seed(45)
error <- perf(srbct.splsda, validation = "Mfold", folds = 8,
dist = "all", auc = TRUE, cpus =2)
\dontrun{
# with 5 components and nrepeat =5, to get a $choice.ncomp
ncomp = 5
srbct.splsda <- splsda(X, Y, ncomp = ncomp, keepX = rep(10, ncomp))
set.seed(45)
error <- perf(srbct.splsda, validation = "Mfold", folds = 8,
dist = "all", nrepeat =5)
error
plot(error)
# parallel code
set.seed(45)
error <- perf(srbct.splsda, validation = "Mfold", folds = 8,
dist = "all", auc = TRUE, cpus =2)
}
## validation for objects of class 'mint.splsda' (classification)
# ----------------------------------------
data(stemcells)
res = mint.splsda(X = stemcells$gene, Y = stemcells$celltype, ncomp = 3, keepX = c(10, 5, 15),
study = stemcells$study)
out = perf(res, auc = TRUE)
out
out$auc
out$auc.study
## validation for objects of class 'sgccda' (classification)
# ----------------------------------------
data(nutrimouse)
Y = nutrimouse$diet
data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid)
design = matrix(c(0,1,1,1,0,1,1,1,0), ncol = 3, nrow = 3, byrow = TRUE)
nutrimouse.sgccda <- block.splsda(X=data,
Y = Y,
design = design,
keepX = list(gene=c(10,10), lipid=c(15,15)),
ncomp = 2,
scheme = "horst")
perf = perf(nutrimouse.sgccda)
perf
\dontrun{
#with 5 components and nrepeat=5 to get $choice.ncomp
nutrimouse.sgccda <- block.splsda(X=data,
Y = Y,
design = design,
keepX = list(gene=c(10,10), lipid=c(15,15)),
ncomp = 5,
scheme = "horst")
perf = perf(nutrimouse.sgccda, folds = 5, nrepeat = 5)
perf
perf$choice.ncomp
}
## End(Not run)
mixOmics documentation built on June 1, 2018, 5:06 p.m.
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
Function to evaluate the performance of the fitted PLS, sparse PLS, PLS-DA, sparse PLS-DA, MINT (mint.splsda) and DIABLO (block.splsda) models using various criteria.
Usage
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 31 |
## S3 method for class 'pls'
perf(object,validation = c("Mfold", "loo"),
folds = 10, progressBar = TRUE, ...)
## S3 method for class 'spls'
perf(object,validation = c("Mfold", "loo"),
folds = 10, progressBar = TRUE, ...)
## S3 method for class 'plsda'
perf(object,
dist = c("all", "max.dist", "centroids.dist", "mahalanobis.dist"),
validation = c("Mfold", "loo"),
folds = 10, nrepeat =1, auc = FALSE, progressBar = TRUE, cpus, ...)
## S3 method for class 'splsda'
perf(object,
dist = c("all", "max.dist", "centroids.dist", "mahalanobis.dist"),
validation = c("Mfold", "loo"),
folds = 10, nrepeat =1, auc = FALSE, progressBar = TRUE, cpus, ...)
## S3 method for class 'mint.splsda'
perf(object,
dist = c("all", "max.dist", "centroids.dist", "mahalanobis.dist"),
auc = FALSE, progressBar = TRUE, ...)
## S3 method for class 'sgccda'
perf(object,
dist = c("all", "max.dist", "centroids.dist", "mahalanobis.dist"),
validation = c("Mfold", "loo"),
folds = 10, nrepeat =1, cpus, ...)
|
Arguments
object |
object of class inheriting from |
dist |
only applies to an object inheriting from |
validation |
character. What kind of (internal) validation to use, matching one of |
folds |
the folds in the Mfold cross-validation. See Details. |
nrepeat |
Number of times the Cross-Validation process is repeated. This is an important argument to ensure the estimation of the performance to be as accurate as possible. |
auc |
if |
progressBar |
by default set to |
cpus |
Number of cpus to use when running the code in parallel. |
... |
not used |
Details
Procedure. The process of evaluating the performance of a fitted model object is similar for all PLS-derived methods; a cross-validation approach is used to fit the method of object on folds-1 subsets of the data and then to predict on the subset left out. Different measures of performance are available depending on the model. Parameters such as logratio, multilevel, keepX or keepY are retrieved from object.
Parameters. If validation = "Mfold", M-fold cross-validation is performed.
folds specifies the number of folds to generate.
The folds also can be supplied as a list of vectors containing the indexes defining each
fold as produced by split. When using validation = "Mfold", make sure that you repeat the process several times (as the results will be highly dependent on the random splits and the sample size).
If validation = "loo", leave-one-out cross-validation is performed (in that case, there is no need to repeat the process).
Measures of performance. For fitted PLS and sPLS regression models, perf estimates the
mean squared error of prediction (MSEP), R^2, and Q^2 to assess the predictive
perfity of the model using M-fold or leave-one-out cross-validation. Note that only the classic, regression and invariant modes can be applied.
For sPLS, the MSEP, R^2, and Q^2 criteria are averaged across all folds.
The Q2.total combines the Q^2 values for each Y variable and provides an overall assessment of fit. According to Tenenhaus (1998) the rule of thumb to decide whether a component contributes significantly to the prediction is when its Q2.total is still greater than 0.0975. However whe dealing with a large number of variables, especially in X, we have often experienced Q2.total values that do not decrease. The sPLS method also may not reach this cutoff, depending on the type of data and problem.
Note that for PLS and sPLS objects, perf is performed on the pre-processed data after log ratio transform and multilevel analysis, when applied.
Sparse methods. The sPLS, sPLS-DA and sgccda functions are run on several and different subsets of data (the cross-folds) and will certainly lead to different subset of selected features. Those are summarised in the output features$stable (see output Value below) to assess how often the variables are selected across all folds.
Note that for PLS-DA and sPLS-DA objects, perf is performed on the original data, i.e. before the pre-processing step of the log ratio transform and multilevel analysis, if any. In addition for these methods, the classification error rate is averaged across all folds.
The mint.sPLS-DA function estimates errors based on Leave-one-group-out cross validation (where each levels of object$study is left out (and predicted) once) and provides study-specific outputs (study.specific.error) as well as global outputs (global.error).
AUROC. For PLS-DA, sPLS-DA, mint.PLS-DA and mint.sPLS-DA methods: if auc=TRUE, Area Under the Curve (AUC) values are calculated from the predicted scores obtained from the predict function applied to the internal test sets in the cross-validation process, either for all samples or for study-specific samples (for mint models). Therefore we minimise the risk of overfitting. See auroc for more details. Our multivariate supervised methods already use a prediction threshold based on distances (see predict) that optimally determine class membership of the samples tested. As such AUC and ROC are not needed to estimate the performance of the model. We provide those outputs as complementary performance measures. See more details in our mixOmics article.
Prediction distances. See details from ?predict, and also our supplemental material in the mixOmics article.
Repeats of the CV-folds. Repeated cross-validation implies that the whole CV process is repeated a number of times (nrepeat) to reduce variability across the different subset partitions. In the case of Leave-One-Out CV (validation = 'loo'), each sample is left out once (folds = N is set internally) and therefore nrepeat is by default 1.
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 PLS modes in ?pls.
Value
For PLS and sPLS models, perf produces a list with the following components:
MSEP |
Mean Square Error Prediction for each Y variable, only applies to object inherited from |
R2 |
a matrix of R^2 values of the Y-variables for models
with 1, … , |
Q2 |
if Y containts one variable, a vector of Q^2 values else a list with
a matrix of Q^2 values for each Y-variable. Note that in the specific case of an sPLS model, it is better to have a look at the Q2.total criterion, only applies to object inherited from |
Q2.total |
a vector of Q^2-total values for models with 1, … , |
features |
a list of features selected across the folds ( |
error.rate |
For PLS-DA and sPLS-DA models, |
auc |
Averaged AUC values over the |
For mint.splsda models, perf produces the following outputs:
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 |
class |
A list which gives the predicted class of each sample for each |
auc |
AUC values |
auc.study |
AUC values for each study |
For sgccda models, perf produces the following outputs:
error.rate |
Prediction error rate for each block of |
error.rate.per.class |
Prediction error rate for each block of |
predict |
Predicted values of each sample for each class, each block and each component |
class |
Predicted class of each sample for each block, each |
features |
a list of features selected across the folds ( |
AveragedPredict.class |
if more than one block, returns the average predicted class over the blocks (averaged of the |
AveragedPredict.error.rate |
if more than one block, returns the average predicted error rate over the blocks (using the |
WeightedPredict.class |
if more than one block, returns the weighted predicted class over the blocks (weighted average of the |
WeightedPredict.error.rate |
if more than one block, returns the weighted average predicted error rate over the blocks (using the |
MajorityVote |
if more than one block, returns the majority class over the blocks. NA for a sample means that there is no consensus on the predicted class for this particular sample over the blocks. |
MajorityVote.error.rate |
if more than one block, returns the error rate of the |
WeightedVote |
if more than one block, returns the weighted majority class over the blocks. NA for a sample means that there is no consensus on the predicted class for this particular sample over the blocks. |
WeightedVote.error.rate |
if more than one block, returns the error rate of the |
weights |
Returns the weights of each block used for the weighted predictions, for each nrepeat and each fold |
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. |
Author(s)
Ignacio González, Amrit Singh, Kim-Anh Lê Cao, Benoit Gautier, Florian Rohart.
References
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.
See Also
predict, nipals, plot.perf, auroc and www.mixOmics.org for more details.
Examples
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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 | ## Not run:
## validation for objects of class 'pls' (regression)
# ----------------------------------------
data(liver.toxicity)
X <- liver.toxicity$gene
Y <- liver.toxicity$clinic
# try tune the number of component to choose
# ---------------------
# first learn the full model
liver.pls <- pls(X, Y, ncomp = 10)
# with 5-fold cross validation: we use the same parameters as in model above
# but we perform cross validation to compute the MSEP, Q2 and R2 criteria
# ---------------------------
liver.val <- perf(liver.pls, validation = "Mfold", folds = 5)
# Q2 total should decrease until it reaches a threshold
liver.val$Q2.total
# ncomp = 2 is enough
plot(liver.val$Q2.total, type = 'l', col = 'red', ylim = c(-0.5, 0.5),
xlab = 'PLS components', ylab = 'Q2 total')
abline(h = 0.0975, col = 'darkgreen')
legend('topright', col = c('red', 'darkgreen'),
legend = c('Q2 total', 'threshold 0.0975'), lty = 1)
title('Liver toxicity PLS 5-fold, Q2 total values')
#have a look at the other criteria
# ----------------------
# R2
liver.val$R2
matplot(t(liver.val$R2), type = 'l', xlab = 'PLS components', ylab = 'R2 for each variable')
title('Liver toxicity PLS 5-fold, R2 values')
# MSEP
liver.val$MSEP
matplot(t(liver.val$MSEP), type = 'l', xlab = 'PLS components', ylab = 'MSEP for each variable')
title('Liver toxicity PLS 5-fold, MSEP values')
## validation for objects of class 'spls' (regression)
# ----------------------------------------
ncomp = 7
# first, learn the model on the whole data set
model.spls = spls(X, Y, ncomp = ncomp, mode = 'regression',
keepX = c(rep(10, ncomp)), keepY = c(rep(4,ncomp)))
# with leave-one-out cross validation
##set.seed(45)
model.spls.val <- perf(model.spls, validation = "Mfold", folds = 5 )#validation = "loo")
#Q2 total
model.spls.val$Q2.total
# R2:we can see how the performance degrades when ncomp increases
model.spls.val$R2
plot(model.spls.val, criterion="R2", type = 'l')
plot(model.spls.val, criterion="Q2", type = 'l')
## validation for objects of class 'splsda' (classification)
# ----------------------------------------
data(srbct)
X <- srbct$gene
Y <- srbct$class
ncomp = 2
srbct.splsda <- splsda(X, Y, ncomp = ncomp, keepX = rep(10, ncomp))
# with Mfold
# ---------
set.seed(45)
error <- perf(srbct.splsda, validation = "Mfold", folds = 8,
dist = "all", auc = TRUE)
error
error$auc
plot(error)
# parallel code
set.seed(45)
error <- perf(srbct.splsda, validation = "Mfold", folds = 8,
dist = "all", auc = TRUE, cpus =2)
\dontrun{
# with 5 components and nrepeat =5, to get a $choice.ncomp
ncomp = 5
srbct.splsda <- splsda(X, Y, ncomp = ncomp, keepX = rep(10, ncomp))
set.seed(45)
error <- perf(srbct.splsda, validation = "Mfold", folds = 8,
dist = "all", nrepeat =5)
error
plot(error)
# parallel code
set.seed(45)
error <- perf(srbct.splsda, validation = "Mfold", folds = 8,
dist = "all", auc = TRUE, cpus =2)
}
## validation for objects of class 'mint.splsda' (classification)
# ----------------------------------------
data(stemcells)
res = mint.splsda(X = stemcells$gene, Y = stemcells$celltype, ncomp = 3, keepX = c(10, 5, 15),
study = stemcells$study)
out = perf(res, auc = TRUE)
out
out$auc
out$auc.study
## validation for objects of class 'sgccda' (classification)
# ----------------------------------------
data(nutrimouse)
Y = nutrimouse$diet
data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid)
design = matrix(c(0,1,1,1,0,1,1,1,0), ncol = 3, nrow = 3, byrow = TRUE)
nutrimouse.sgccda <- block.splsda(X=data,
Y = Y,
design = design,
keepX = list(gene=c(10,10), lipid=c(15,15)),
ncomp = 2,
scheme = "horst")
perf = perf(nutrimouse.sgccda)
perf
\dontrun{
#with 5 components and nrepeat=5 to get $choice.ncomp
nutrimouse.sgccda <- block.splsda(X=data,
Y = Y,
design = design,
keepX = list(gene=c(10,10), lipid=c(15,15)),
ncomp = 5,
scheme = "horst")
perf = perf(nutrimouse.sgccda, folds = 5, nrepeat = 5)
perf
perf$choice.ncomp
}
## End(Not run)
|
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