| Title: | Multiblock Exploratory and Predictive Data Analysis |
| Version: | 2.1.0 |
| Description: | Exploratory and predictive methods for the analysis of several blocks of variables measured on the same individuals. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Depends: | R (≥ 4.0.0) |
| LazyData: | false |
| Imports: | ggplot2, ggrepel, grDevices, stats, utils |
| NeedsCompilation: | no |
| Packaged: | 2025-07-07 15:43:17 UTC; benjamin.mahieu |
| Author: | Benjamin Mahieu [aut, cre], Essomanda Tchandao Mangamana [aut], Evelyne Vigneau [aut], Veronique Cariou [aut] |
| Maintainer: | Benjamin Mahieu <benjamin.mahieu@oniris-nantes.fr> |
| Repository: | CRAN |
| Date/Publication: | 2025-07-10 10:00:02 UTC |
MBAnalysis: Multiblock Exploratory and Predictive Data Analysis
Description
Exploratory and predictive methods for the analysis of several blocks of variables measured on the same individuals.
Author(s)
Maintainer: Benjamin Mahieu benjamin.mahieu@oniris-nantes.fr
Authors:
Essomanda Tchandao Mangamana tchanesso@yahoo.fr
Evelyne Vigneau evelyne.vigneau@oniris-nantes.fr
Veronique Cariou veronique.cariou@oniris-nantes.fr
References
Tchandao Mangamana, E., Cariou, V., Vigneau, E., Glèlè Kakaï, R. L., & Qannari, E. M. (2019). Unsupervised multiblock data analysis: A unified approach and extensions. Chemometrics and Intelligent Laboratory Systems, 194, 103856.
Tchandao Mangamana, E., Glèlè Kakaï, R., & Qannari, E. M. (2021). A general strategy for setting up supervised methods of multiblock data analysis. Chemometrics and Intelligent Laboratory Systems, 217, 104388.
Common Dimensions analysis (ComDim)
Description
Performs ComDim analysis on a set of quantitative blocks of variables. ComDim can be viewed as a Multiblock Weighted Principal Components Analysis (MBWPCA)
Usage
ComDim(
X,
block,
name.block = NULL,
ncomp = NULL,
scale = TRUE,
scale.block = TRUE,
threshold = 1e-08
)
Arguments
X |
Dataset obtained by horizontally merging all the blocks of variables. |
block |
Vector indicating the number of variables in each block. |
name.block |
names of the blocks of variables (NULL by default). |
ncomp |
Number of dimensions to compute. By default (NULL), all the global components are extracted. |
scale |
Logical, if TRUE (by default) then variables are scaled to unit variance (all variables are centered anyway). |
scale.block |
Logical, if TRUE (by default) each block of variables is divided by the square root of its inertia (Frobenius norm). |
threshold |
Convergence threshold |
Value
Returns a list of the following elements:
optimalcrit |
Numeric vector of the optimal value of the criterion (sum of squared saliences) obtained for each dimension. |
saliences |
Matrix of the specific weights of each block of variables on the global components, for each dimension. |
T.g |
Matrix of normed global components. |
Scor.g |
Matrix of global components (scores of individuals). |
W.g |
Matrix of global weights (normed) associated with deflated X. |
Load.g |
Matrix of global loadings (normed). |
Proj.g |
Matrix of global projection (to compute scores from pretreated X). |
explained.X |
Matrix of percentages of inertia explained in each block of variables. |
cumexplained |
Matrix giving the percentages, and cumulative percentages, of total inertia of X blocks explained by the global components. |
Block |
A list containing block components (T.b) and block weights (W.b) |
References
E.M. Qannari, I. Wakeling, P. Courcoux, J.M. MacFie (2000). Defining the underlying sensory dimensions, Food Quality and Preference, 11: 151-154.
E. Tchandao Mangamana, V. Cariou, E. Vigneau, R. Glèlè Kakaï, E.M. Qannari (2019). Unsupervised multiblock data analysis: A unified approach and extensions, Chemometrics and Intelligent Laboratory Systems, 194, 103856.
See Also
Examples
data(ham)
X=ham$X
block=ham$block
res.comdim <- ComDim(X,block,name.block=names(block))
summary(res.comdim)
plot(res.comdim)
Multiblock Principal Components Analysis (MB-PCA)
Description
Performs MB-PCA on a set of quantitative blocks of variables.
Usage
MBPCA(
X,
block,
name.block = NULL,
ncomp = NULL,
scale = TRUE,
scale.block = TRUE
)
Arguments
X |
Dataset obtained by horizontally merging all the blocks of variables. |
block |
Vector indicating the number of variables in each block. |
name.block |
names of the blocks of variables (NULL by default). |
ncomp |
Number of dimensions to compute. By default (NULL), all the global components are extracted. |
scale |
Logical, if TRUE (by default) then variables are scaled to unit variance (all variables are centered anyway). |
scale.block |
Logical, if TRUE (by default) each block of variables is divided by the square root of its inertia (Frobenius norm). |
Value
Returns a list of the following elements:
optimalcrit |
Numeric vector of the optimal value of the criterion (sum of saliences) obtained for each dimension. |
saliences |
Matrix of the specific weights of each block of variables on the global components, for each dimension. |
T.g |
Matrix of normed global components. |
Scor.g |
Matrix of global components (scores of individuals). |
W.g |
Matrix of global weights (normed) associated with deflated X. |
Load.g |
Matrix of global loadings (normed) = W.g in the specific context of MB-PCA. |
Proj.g |
Matrix of global projection (to compute scores from pretreated X) = W.g in the specific context of MB-PCA. |
explained.X |
Matrix of percentages of inertia explained in each block of variables. |
cumexplained |
Matrix giving the percentages, and cumulative percentages, of total inertia of X blocks explained by the global components. |
Block |
A list containing block components (T.b) and block weights (W.b) |
References
S. Wold, S. Hellberg, T. Lundstedt, M. Sjostrom, H. Wold (1987). Hierarchical multiblock PLS and PC models for easier model interpretation and as an alternative to variable
selection, in: Proc. Symp. On PLS Model Building: Theory and Application, Frankfurt am Main.
E. Tchandao Mangamana, V. Cariou, E. Vigneau, R. Glèlè Kakaï, E.M. Qannari (2019). Unsupervised multiblock data analysis: A unified approach and extensions, Chemometrics and Intelligent Laboratory Systems, 194, 103856.
See Also
Examples
data(ham)
X=ham$X
block=ham$block
res.mbpca <- MBPCA(X,block, name.block=names(block))
summary(res.mbpca)
plot(res.mbpca)
Multiblock Partial Least Squares (MB-PLS) regression
Description
MB-PLS regression applied to a set of quantitative blocks of variables.
Usage
MBPLS(
X,
Y,
block,
name.block = NULL,
ncomp = NULL,
scale = TRUE,
scale.block = TRUE,
scale.Y = TRUE
)
Arguments
X |
Dataset obtained by horizontally merging all the predictor blocks of variables. |
Y |
Response block of variables. |
block |
Vector indicating the number of variables in each predictor block. |
name.block |
Names of the predictor blocks of variables (NULL by default). |
ncomp |
Number of dimensions to compute. By default (NULL), all the global components are extracted. |
scale |
Logical, if TRUE (by default) the variables in X are scaled to unit variance (all variables in X are centered anyway). |
scale.block |
Logical, if TRUE (by default) each predictor block of variables is divided by the square root of its inertia (Frobenius norm). |
scale.Y |
Logical, if TRUE (by default) then variables in Y are scaled to unit variance (all variables in Y are centered anyway). |
Value
Returns a list of the following elements:
optimalcrit |
Numeric vector of the optimal value of the criterion (sum of saliences) obtained for each dimension. |
saliences |
Matrix of the specific weights of each predictor block on the global components, for each dimension. |
T.g |
Matrix of normed global components. |
Scor.g |
Matrix of global components (scores of individuals). |
W.g |
Matrix of global weights (normed) associated with deflated X. |
Load.g |
Matrix of global loadings. |
Proj.g |
Matrix of global projection (to compute scores from pretreated X). |
explained.X |
Matrix of percentages of inertia explained in each predictor block. |
cumexplained |
Matrix giving the percentages, and cumulative percentages, of total inertia of X and Y blocks explained by the global components. |
Y |
A list containing un-normed Y components (U), normed Y weights (W.Y) and Y loadings (Load.Y) |
Block |
A list containing block components (T.b) and block weights (W.b) |
References
S. Wold (1984). Three PLS algorithms according to SW. In: Symposium MULDAST (Multivariate Analysis in
Science and Technology), Umea University, Sweden. pp. 26–30.
E. Tchandao Mangamana, R. Glèlè Kakaï, E.M. Qannari (2021). A general strategy for setting up supervised methods of multiblock data analysis. Chemometrics and Intelligent Laboratory Systems, 217, 104388.
See Also
Examples
data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbpls <- MBPLS(X, Y, block, name.block = names(block))
summary(res.mbpls)
plot(res.mbpls)
Cross-Validation of MBPLS or MBWCov models
Description
Computes MSEP and corresponding standard error based on Leave One Out (LOO) or Out Of Bag (OOB) Cross-Validation (CV) by number of components of a MBPLS or MBWCov model from MBPLS or MBWCov.
Usage
MBValidation(
res,
ncomp.max = min(res$call$ncomp, nrow(res$call$X) - 2, ncol(X)),
method = "LOO",
nboot = 1000,
graph = TRUE,
size.graph = 2.25
)
Arguments
res |
|
ncomp.max |
The maximum number of components to be investigated in the CV procedure. |
method |
Either "LOO" or "OOB". Default is LOO. |
nboot |
Number of bootstrap samples to be generated in case of OOB CV. |
graph |
Logical. Should the results be plotted? Default is TRUE. |
size.graph |
If graph=TRUE, the overall size of labels, points, etc. |
Value
A matrix with two rows (MSEP and std.error) and ncomp.max+1 columns. The +1 column corresponds to the null model (Dim.0) where Y is predicted by its empirical average on the training sample.
See Also
Examples
# With MBPLS
data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbpls <- MBPLS(X, Y, block, name.block = names(block))
MBValidation(res.mbpls)
# With MBWCov
data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbwcov <- MBWCov(X, Y, block, name.block = names(block))
MBValidation(res.mbwcov)
Multiblock Weighted Covariate analysis (MB-WCov)
Description
MB-WCov analysis applied to a set of quantitative blocks of variables.
Usage
MBWCov(
X,
Y,
block,
name.block = NULL,
ncomp = NULL,
scale = TRUE,
scale.block = TRUE,
scale.Y = TRUE,
threshold = 1e-08
)
Arguments
X |
Dataset obtained by horizontally merging all the predictor blocks of variables. |
Y |
Response block of variables. |
block |
Vector indicating the number of variables in each predictor block. |
name.block |
Names of the predictor blocks of variables (NULL by default). |
ncomp |
Number of dimensions to compute. By default (NULL), all the global components are extracted. |
scale |
Logical, if TRUE (by default) the variables in X are scaled to unit variance (all variables in X are centered anyway). |
scale.block |
Logical, if TRUE (by default) each predictor block of variables is divided by the square root of its inertia (Frobenius norm). |
scale.Y |
Logical, if TRUE (by default) then variables in Y are scaled to unit variance (all variables in Y are centered anyway). |
threshold |
Convergence threshold |
Value
optimalcrit |
Numeric vector of the optimal value of the criterion (sum of squared saliences) obtained for each dimension. |
saliences |
Matrix of the specific weights of each predictor block on the global components, for each dimension. |
T.g |
Matrix of normed global components. |
Scor.g |
Matrix of global components (scores of individuals). |
W.g |
Matrix of global weights (normed) associated with deflated X. |
Load.g |
Matrix of global loadings. |
Proj.g |
Matrix of global projection (to compute scores from pretreated X). |
explained.X |
Matrix of percentages of inertia explained in each predictor block. |
cumexplained |
Matrix giving the percentages, and cumulative percentages, of total inertia of X and Y blocks explained by the global components. |
Y |
A list containing un-normed Y components (U), normed Y weights (W.Y) and Y loadings (Load.Y) |
Block |
A list containing block components (T.b) and block weights (W.b) |
References
E. Tchandao Mangamana, R. Glèlè Kakaï, E.M. Qannari (2021). A general strategy for setting up supervised methods of multiblock data analysis. Chemometrics and Intelligent Laboratory Systems, 217, 104388.
See Also
Examples
data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbwcov <- MBWCov(X, Y, block, name.block = names(block))
summary(res.mbwcov)
plot(res.mbwcov)
Customizable plots of blocks related information
Description
Plots blocks related information of MBPCA, ComDim, MBPLS or MBWCov with several options of customization.
Usage
MBplotBlocks(
res,
which = "explained.blocks&Y",
axes = c(1, 2),
blocks.axes = 1:max(axes),
title = NULL,
size = 2.25
)
Arguments
res |
|
which |
Either "explained.blocks&Y", "scree", "structure" or "blocks.axes". See details. |
axes |
Which global dimensions should be plotted? Only useful if which=structure or which=blocks.axes |
blocks.axes |
Which individual blocks dimensions should be correlated with global ones? Only useful if which=blocks.axes |
title |
An optional title to be added to the plot. |
size |
The overall size of labels, points, etc. |
Details
-
explained.blocks&Y: Barplot of the percentages of inertia explained in each block of variables (and Y for
MBPLSorMBWCov) by each global components. -
scree: Barplot of the saliences of each block of variables on each global components.
-
structure: Blocks coordinates (saliences) on the global selected axes
-
blocks.axes: Correlations of the selected individual blocks.axes with the global selected axes.
Value
The required plot.
See Also
plot.MBPCA plot.ComDim plot.MBPLS plot.MBWCov
Examples
# Unsupervised example
data(ham)
X=ham$X
block=ham$block
res.mbpca <- MBPCA(X,block, name.block=names(block))
MBplotBlocks(res.mbpca,which="explained.blocks&Y")
MBplotBlocks(res.mbpca,which="scree")
MBplotBlocks(res.mbpca,which="structure")
MBplotBlocks(res.mbpca,which="blocks.axes")
# Supervised example
data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbpls <- MBPLS(X, Y, block, name.block=names(block))
MBplotBlocks(res.mbpls,which="explained.blocks&Y")
MBplotBlocks(res.mbpls,which="scree")
MBplotBlocks(res.mbpls,which="structure")
MBplotBlocks(res.mbpls,which="blocks.axes")
Customizable plots of scores related information
Description
Plots scores related information of MBPCA, ComDim, MBPLS or MBWCov with several options of customization.
Usage
MBplotScores(
res,
axes = c(1, 2),
block = 0,
color = NULL,
select = 1:nrow(res$Scor.g),
title = NULL,
size = 2.25
)
Arguments
res |
|
axes |
Which dimensions should be plotted? |
block |
Of which block? Block 0 corresponds to global components. |
color |
Either NULL (default) or a character vector of length select. Controls the color of each individual plotted. Useful if individuals pertain to different a priori known groups. By default individuals are colored in black for global components and in the block color (the same as in |
select |
A numeric or integer vector to select which individuals should be plotted. By default, all individuals are plotted. |
title |
An optional title to be added to the plot. |
size |
The overall size of labels, points, etc. |
Value
The required plot.
See Also
plot.MBPCA plot.ComDim plot.MBPLS plot.MBWCov
Examples
# Unsupervised example
data(ham)
X=ham$X
block=ham$block
res.mbpca <- MBPCA(X,block, name.block=names(block))
MBplotScores(res.mbpca)
# Supervised example
data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbpls <- MBPLS(X, Y, block, name.block=names(block))
MBplotScores(res.mbpls)
Customizable plots of variables related information
Description
Plots variables related information of MBPCA, ComDim, MBPLS or MBWCov with several options of customization.
Usage
MBplotVars(
res,
axes = c(1, 2),
which = ifelse(res$call$scale, "correlation", "loading"),
block = 0,
select = 0,
title = NULL,
size = 2.25
)
Arguments
res |
|
axes |
Which dimensions should be plotted? |
which |
Either "correlation" or "loading". |
block |
Selection of variables by blocks. A number or integer, possibly a vector, corresponding to the index of the blocks from which the variables should be plotted. For |
select |
Selection of variables by index. A number or integer, possibly a vector, corresponding to the index of the variables that should be plotted. For |
title |
An optional title to be added to the plot. |
size |
The overall size of labels, points, etc. |
Value
The required plot.
See Also
plot.MBPCA plot.ComDim plot.MBPLS plot.MBWCov
Examples
# Unsupervised example
data(ham)
X=ham$X
block=ham$block
res.mbpca <- MBPCA(X,block, name.block=names(block))
MBplotVars(res.mbpca)
# Supervised example
data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbpls <- MBPLS(X, Y, block, name.block=names(block))
MBplotVars(res.mbpls)
Regression coefficients of MBPLS models
Description
Computes regression coefficients from MBPLS.
Usage
## S3 method for class 'MBPLS'
coef(object, ncomp = object$call$ncomp, ...)
Arguments
object |
An object resulting from |
ncomp |
The number of components to be considered in the model. By default, all components computed in |
... |
further arguments passed to or from other methods. |
Value
A matrix of regression coefficients where each row corresponds to a variable in X and each column corresponds to a variable in Y.
See Also
Examples
data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbpls <- MBPLS(X, Y, block, name.block = names(block))
coef(res.mbpls)
Regression coefficients of MBWCov models
Description
Computes regression coefficients from MBWCov.
Usage
## S3 method for class 'MBWCov'
coef(object, ncomp = object$call$ncomp, ...)
Arguments
object |
An object resulting from |
ncomp |
The number of components to be considered in the model. By default, all components computed in |
... |
further arguments passed to or from other methods. |
Value
A matrix of regression coefficients where each row corresponds to a variable in X and each column corresponds to a variable in Y.
See Also
Examples
data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbwcov <- MBWCov(X, Y, block, name.block = names(block))
coef(res.mbwcov)
Ham data
Description
Case study pertaining to the sensory evaluation of eight American dry-cured ham products, performed by a panel of trained assessors.
Usage
data(ham)
Format
An object of class "list" with 8 products, 3 blocks of X variables (Flavor, Aroma, Texture) and 1 block of Y variables corresponding to hedonic measures:
- X
dataframe of 8 products and 25 variables structured into 3 blocks: Flavor (11 variables), Aroma (8 variables) and Texture (6 variables)
- Y
dataframe of 8 products and 6 vectors of hedonic values corresponding to consumers' segmentation
- block
vector indicating the number of variables per block
References
M.D. Guardia, A.P. Aguiar, A. Claret, J. Arnau & L. Guerrero (2010). Sensory characterization of dry-cured ham using free-choice profiling. Food Quality and Preference, 21(1), 148-155. doi:10.1016/j.foodqual.2009.08.014
Examples
data(ham)
ham$X
ham$Y
ham$block
Default plots for ComDim objects
Description
Successively performs MBplotScores, MBplotVars and MBplotBlocks with the default values of parameters but axes and size.
Usage
## S3 method for class 'ComDim'
plot(x, axes = c(1, 2), size = 2.25, ...)
Arguments
x |
An object resulting from |
axes |
Which dimensions should be plotted? |
size |
The overall size of labels, points, etc. |
... |
further arguments passed to or from other methods. |
Value
The default plots.
See Also
MBplotScores MBplotVars MBplotBlocks
Examples
data(ham)
X=ham$X
block=ham$block
res.comdim <- ComDim(X,block,name.block=names(block))
plot(res.comdim)
Default plots for MBPCA objects
Description
Successively performs MBplotScores, MBplotVars and MBplotBlocks with the default values of parameters but axes and size.
Usage
## S3 method for class 'MBPCA'
plot(x, axes = c(1, 2), size = 2.25, ...)
Arguments
x |
An object resulting from |
axes |
Which dimensions should be plotted? |
size |
The overall size of labels, points, etc. |
... |
further arguments passed to or from other methods. |
Value
The default plots.
See Also
MBplotScores MBplotVars MBplotBlocks
Examples
data(ham)
X=ham$X
block=ham$block
res.mbpca <- MBPCA(X,block, name.block=names(block))
plot(res.mbpca)
Default plots for MBPLS objects
Description
Successively performs MBplotScores, MBplotVars and MBplotBlocks with the default values of parameters but axes and size.
Usage
## S3 method for class 'MBPLS'
plot(x, axes = c(1, 2), size = 2.25, ...)
Arguments
x |
An object resulting from |
axes |
Which dimensions should be plotted? |
size |
The overall size of labels, points, etc. |
... |
further arguments passed to or from other methods. |
Value
The default plots.
See Also
MBplotScores MBplotVars MBplotBlocks
Examples
data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbpls <- MBPLS(X, Y, block, name.block = names(block))
plot(res.mbpls)
Default plots for MBWCov objects
Description
Successively performs MBplotScores, MBplotVars and MBplotBlocks with the default values of parameters but axes and size.
Usage
## S3 method for class 'MBWCov'
plot(x, axes = c(1, 2), size = 2.25, ...)
Arguments
x |
An object resulting from |
axes |
Which dimensions should be plotted? |
size |
The overall size of labels, points, etc. |
... |
further arguments passed to or from other methods. |
Value
The default plots.
See Also
MBplotScores MBplotVars MBplotBlocks
Examples
data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbwcov <- MBWCov(X, Y, block, name.block = names(block))
plot(res.mbwcov)
Prediction from MBPLS models
Description
Computes predictions of Y from MBPLS using calibration X (default) or new X observations.
Usage
## S3 method for class 'MBPLS'
predict(object, newdata = object$call$X, ncomp = object$call$ncomp, ...)
Arguments
object |
An object resulting from |
newdata |
A matrix or data.frame of (new) observations having the same ncol and same colnames as the X of fitting observations. |
ncomp |
The number of components to be considered in the model to perform the predictions. By default, all components computed in |
... |
further arguments passed to or from other methods. |
Value
A matrix of predicted Y values where each row corresponds to an observation and each column corresponds to a Y variable.
See Also
Examples
data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbpls <- MBPLS(X, Y, block, name.block = names(block))
predict(res.mbpls)
Prediction from MBWCov models
Description
Computes predictions of Y from MBWCov using calibration X (default) or new X observations.
Usage
## S3 method for class 'MBWCov'
predict(object, newdata = object$call$X, ncomp = object$call$ncomp, ...)
Arguments
object |
An object resulting from |
newdata |
A matrix or data.frame of (new) observations having the same ncol and same colnames as the X of fitting observations. |
ncomp |
The number of components to be considered in the model to perform the predictions. By default, all components computed in |
... |
further arguments passed to or from other methods. |
Value
A matrix of predicted Y values where each row corresponds to an observation and each column corresponds to a Y variable.
See Also
Examples
data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbwcov <- MBWCov(X, Y, block, name.block = names(block))
predict(res.mbwcov)
Print of ComDim objects
Description
Print of ComDim objects
Usage
## S3 method for class 'ComDim'
print(x, ...)
Arguments
x |
An object resulting from |
... |
further arguments passed to or from other methods. |
See Also
Examples
data(ham)
X=ham$X
block=ham$block
res.comdim <- ComDim(X,block,name.block=names(block))
print(res.comdim)
Print of MBPCA objects
Description
Print of MBPCA objects
Usage
## S3 method for class 'MBPCA'
print(x, ...)
Arguments
x |
An object resulting from |
... |
further arguments passed to or from other methods. |
See Also
Examples
data(ham)
X=ham$X
block=ham$block
res.mbpca <- MBPCA(X,block, name.block=names(block))
print(res.mbpca)
Print of MBPLS objects
Description
Print of MBPLS objects
Usage
## S3 method for class 'MBPLS'
print(x, ...)
Arguments
x |
An object resulting from |
... |
further arguments passed to or from other methods. |
See Also
Examples
data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbpls <- MBPLS(X, Y, block, name.block = names(block))
print(res.mbpls)
Print of MBWCov objects
Description
Print of MBWCov objects
Usage
## S3 method for class 'MBWCov'
print(x, ...)
Arguments
x |
An object resulting from |
... |
further arguments passed to or from other methods. |
See Also
Examples
data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbwcov <- MBWCov(X, Y, block, name.block = names(block))
print(res.mbwcov)
Summary of ComDim objects
Description
Edits the Cumulative Explained Variance, Block Explained Variance per Dimension and Block Saliences per Dimension of a ComDim object.
Usage
## S3 method for class 'ComDim'
summary(object, ...)
Arguments
object |
An object resulting from |
... |
further arguments passed to or from other methods. |
Value
The summary.
See Also
Examples
data(ham)
X=ham$X
block=ham$block
res.comdim <- ComDim(X,block,name.block=names(block))
summary(res.comdim)
Summary of MBPCA objects
Description
Edits the Cumulative Explained Variance, Block Explained Variance per Dimension and Block Saliences per Dimension of a MBPCA object.
Usage
## S3 method for class 'MBPCA'
summary(object, ...)
Arguments
object |
An object resulting from |
... |
further arguments passed to or from other methods. |
Value
The summary.
See Also
Examples
data(ham)
X=ham$X
block=ham$block
res.mbpca <- MBPCA(X,block, name.block=names(block))
summary(res.mbpca)
Summary of MBPLS objects
Description
Edits the Cumulative Explained Variance, Block Explained Variance per Dimension and Block Saliences per Dimension of a MBPLS object.
Usage
## S3 method for class 'MBPLS'
summary(object, ...)
Arguments
object |
An object resulting from |
... |
further arguments passed to or from other methods. |
Value
The summary.
See Also
Examples
data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbpls <- MBPLS(X, Y, block, name.block = names(block))
summary(res.mbpls)
Summary of MBWCov objects
Description
Edits the Cumulative Explained Variance, Block Explained Variance per Dimension and Block Saliences per Dimension of a MBWCov object.
Usage
## S3 method for class 'MBWCov'
summary(object, ...)
Arguments
object |
An object resulting from |
... |
further arguments passed to or from other methods. |
Value
The summary.
See Also
Examples
data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbwcov <- MBWCov(X, Y, block, name.block = names(block))
summary(res.mbwcov)