Package 'MBAnalysis' reference manual

Title:	Multiblock Exploratory and Predictive Data Analysis
Description:	Exploratory and predictive methods for the analysis of several blocks of variables measured on the same individuals.
Authors:	Benjamin Mahieu [aut, cre], Essomanda Tchandao Mangamana [aut], Evelyne Vigneau [aut], Veronique Cariou [aut]
Maintainer:	Benjamin Mahieu <[email protected]>
License:	GPL (>= 3)
Version:	2.0.2
Built:	2024-11-05 04:27:09 UTC
Source:	https://github.com/cran/MBAnalysis

Regression coefficients of MBPLS or MBWCov models

Description

Computes regression coefficients from MBPLS or MBWCov.

Usage

Beta(res, ncomp = res$call$ncomp)
Beta(res, ncomp = res$call$ncomp)

Arguments

`res`	An object resulting from `MBPLS` or `MBWCov`.
`ncomp`	The number of components to be considered in the model. By default, all components computed in `MBPLS` or `MBWCov` are considered.

Value

A matrix of regression coefficients where each row corresponds to a variable in X and each column corresponds to a variable in Y.

Examples

# With MBPLS

data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbpls <- MBPLS(X, Y, block, name.block = names(block))
Beta(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))
Beta(res.mbwcov)

# With MBPLS

data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbpls <- MBPLS(X, Y, block, name.block = names(block))
Beta(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))
Beta(res.mbwcov)

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,
  ncomp = NULL,
  scale = TRUE,
  scale.block = TRUE,
  threshold = 1e-08
)
ComDim(
  X,
  block,
  name.block,
  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.

Examples

data(ham)
X=ham$X
block=ham$block
res.comdim <- ComDim(X,block,name.block=names(block))
summary(res.comdim)
plot(res.comdim)

data(ham)
X=ham$X
block=ham$block
res.comdim <- ComDim(X,block,name.block=names(block))
summary(res.comdim)
plot(res.comdim)

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)
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$block
data(ham)
ham$X
ham$Y
ham$block

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
)
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.

Examples

data(ham)
X=ham$X
block=ham$block
res.mbpca <- MBPCA(X,block, name.block=names(block))
summary(res.mbpca)
plot(res.mbpca)

data(ham)
X=ham$X
block=ham$block
res.mbpca <- MBPCA(X,block, name.block=names(block))
summary(res.mbpca)
plot(res.mbpca)

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
)
MBplotBlocks(
  res,
  which = "explained.blocks&Y",
  axes = c(1, 2),
  blocks.axes = 1:max(axes),
  title = NULL,
  size = 2.25
)

Arguments

`res`	An object resulting from `MBPCA`, `ComDim`, `MBPLS` or `MBWCov`.
`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 MBPLS or MBWCov) 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.

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")

# 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$Scor.g),
  title = NULL,
  size = 2.25
)
MBplotScores(
  res,
  axes = c(1, 2),
  block = 0,
  color = NULL,
  select = 1:nrow(res$Scor.g),
  title = NULL,
  size = 2.25
)

Arguments

`res`	An object resulting from `MBPCA`, `ComDim`, `MBPLS` or `MBWCov`.
`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 `MBplotVars`) for block components.
`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.

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)

# 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
)
MBplotVars(
  res,
  axes = c(1, 2),
  which = ifelse(res$call$scale, "correlation", "loading"),
  block = 0,
  select = 0,
  title = NULL,
  size = 2.25
)

Arguments

`res`	An object resulting from `MBPCA`, `ComDim`, `MBPLS` or `MBWCov`.
`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 `MBPLS` and `MBWCov` the Y response block corresponds to the index length(res$call$size.block)+1. By default, all variables of all blocks are plotted. Only one of block and select can differ from 0 (the default) both at the same time.
`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 `MBPLS` and `MBWCov` the Y variables index start from ncol(res$call$X)+1. By default, all variables of all blocks are plotted. Only one of block and select can differ from 0 (the default) both at the same time.
`title`	An optional title to be added to the plot.
`size`	The overall size of labels, points, etc.

Value

The required plot.

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)

# 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)

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
)
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.

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)

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
)
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`	An object resulting from `MBPLS` or `MBWCov`.
`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.

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)

# 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
)
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

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)

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)

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, ...)
## S3 method for class 'ComDim'
plot(x, axes = c(1, 2), size = 2.25, ...)

Arguments

`x`	An object resulting from `ComDim`.
`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.

Examples

data(ham)
X=ham$X
block=ham$block
res.comdim <- ComDim(X,block,name.block=names(block))
plot(res.comdim)

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, ...)
## S3 method for class 'MBPCA'
plot(x, axes = c(1, 2), size = 2.25, ...)

Arguments

`x`	An object resulting from `MBPCA`.
`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.

Examples

data(ham)
X=ham$X
block=ham$block
res.mbpca <- MBPCA(X,block, name.block=names(block))
plot(res.mbpca)

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, ...)
## S3 method for class 'MBPLS'
plot(x, axes = c(1, 2), size = 2.25, ...)

Arguments

`x`	An object resulting from `MBPLS`.
`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.

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)

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, ...)
## S3 method for class 'MBWCov'
plot(x, axes = c(1, 2), size = 2.25, ...)

Arguments

`x`	An object resulting from `MBWCov`.
`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.

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)

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, ...)
## S3 method for class 'MBPLS'
predict(object, newdata = object$call$X, ncomp = object$call$ncomp, ...)

Arguments

`object`	An object resulting from `MBPLS`.
`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 `MBPLS` are considered.
`...`	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.

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)

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, ...)
## S3 method for class 'MBWCov'
predict(object, newdata = object$call$X, ncomp = object$call$ncomp, ...)

Arguments

`object`	An object resulting from `MBWCov`.
`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 `MBWCov` are considered.
`...`	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.

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)

data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbwcov <- MBWCov(X, Y, block, name.block = names(block))
predict(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, ...)
## S3 method for class 'ComDim'
summary(object, ...)

Arguments

`object`	An object resulting from `ComDim`.
`...`	further arguments passed to or from other methods.

Value

The summary.

Examples

data(ham)
X=ham$X
block=ham$block
res.comdim <- ComDim(X,block,name.block=names(block))
summary(res.comdim)

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, ...)
## S3 method for class 'MBPCA'
summary(object, ...)

Arguments

`object`	An object resulting from `MBPCA`.
`...`	further arguments passed to or from other methods.

Value

The summary.

Examples

data(ham)
X=ham$X
block=ham$block
res.mbpca <- MBPCA(X,block, name.block=names(block))
summary(res.mbpca)

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, ...)
## S3 method for class 'MBPLS'
summary(object, ...)

Arguments

`object`	An object resulting from `MBPLS`.
`...`	further arguments passed to or from other methods.

Value

The summary.

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)

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, ...)
## S3 method for class 'MBWCov'
summary(object, ...)

Arguments

`object`	An object resulting from `MBWCov`.
`...`	further arguments passed to or from other methods.

Value

The summary.

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)

data(ham)
X=ham$X
block=ham$block
Y=ham$Y
res.mbwcov <- MBWCov(X, Y, block, name.block = names(block))
summary(res.mbwcov)

Package 'MBAnalysis'

Help Index

Regression coefficients of MBPLS or MBWCov models

Description

Usage

Arguments

Value

See Also

Examples

Common Dimensions analysis (ComDim)

Description

Usage

Arguments

Value

References

See Also

Examples

Ham data

Description

Usage

Format

References

Examples

Multiblock Principal Components Analysis (MB-PCA)

Description

Usage

Arguments

Value

References

See Also

Examples

Customizable plots of blocks related information

Description

Usage

Arguments

Details

Value

See Also

Examples

Customizable plots of scores related information

Description

Usage

Arguments

Value

See Also

Examples

Customizable plots of variables related information

Description

Usage

Arguments

Value

See Also

Examples

Multiblock Partial Least Squares (MB-PLS) regression

Description

Usage

Arguments

Value

References

See Also

Examples

Cross-Validation of MBPLS or MBWCov models

Description

Usage

Arguments

Value

See Also

Examples

Multiblock Weighted Covariate analysis (MB-WCov)

Description

Usage

Arguments

Value

References

See Also

Examples

Default plots for ComDim objects

Description

Usage

Arguments

Default plots for `ComDim` objects

Default plots for `MBPCA` objects

Default plots for `MBPLS` objects

Default plots for `MBWCov` objects

Summary of `ComDim` objects

Summary of `MBPCA` objects

Summary of `MBPLS` objects

Summary of `MBWCov` objects