Package 'msma' reference manual

Title:	Multiblock Sparse Multivariable Analysis
Description:	Several functions can be used to analyze multiblock multivariable data. If the input is a single matrix, then principal components analysis (PCA) is implemented. If the input is a list of matrices, then multiblock PCA is implemented. If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided.
Authors:	Atsushi Kawaguchi
Maintainer:	Atsushi Kawaguchi <[email protected]>
License:	GPL (>= 2)
Version:	3.1
Built:	2024-12-11 07:03:36 UTC
Source:	CRAN

Multiblock Sparse Matrix Analysis Package

Description

A Package for Implementation of the method

Author(s)

References

Kawaguchi A, Yamashita F (2017). Supervised Multiblock Sparse Multivariable Analysis with Application to Multimodal Brain Imaging Genetics. Biostatistics, 18(4) 651-665.

Cross-Validation

Description

cross-validated method to evaluate the fit of "msma".

Usage

cvmsma(
  X,
  Y = NULL,
  Z = NULL,
  comp = 1,
  lambdaX,
  lambdaY = NULL,
  lambdaXsup = NULL,
  lambdaYsup = NULL,
  eta = 1,
  type = "lasso",
  inX = NULL,
  inY = NULL,
  inXsup = NULL,
  inYsup = NULL,
  muX = 0,
  muY = 0,
  nfold = 5,
  seed = 1,
  intseed = 1
)
cvmsma(
  X,
  Y = NULL,
  Z = NULL,
  comp = 1,
  lambdaX,
  lambdaY = NULL,
  lambdaXsup = NULL,
  lambdaYsup = NULL,
  eta = 1,
  type = "lasso",
  inX = NULL,
  inY = NULL,
  inXsup = NULL,
  inYsup = NULL,
  muX = 0,
  muY = 0,
  nfold = 5,
  seed = 1,
  intseed = 1
)

Arguments

`X`	a (list of) matrix, explanatory variable(s).
`Y`	a (list of) matrix, objective variable(s).
`Z`	a (list of) matrix, response variable(s).
`comp`	numeric scalar for the maximum number of componets to be considered.
`lambdaX`	numeric vector of regularized parameters for X with length equal to the number of blocks. If omitted, no regularization is conducted.
`lambdaY`	numeric vector of regularized parameters for Y with length equal to the number of blocks. If omitted, no regularization is conducted.
`lambdaXsup`	numeric vector of regularized parameters for the super weight of X with length equal to the number of blocks. If omitted, no regularization is conducted.
`lambdaYsup`	numeric vector of regularized parameters for the super weight of Y with length equal to the number of blocks. If omitted, no regularization is conducted.
`eta`	numeric scalar the parameter indexing the penalty family.
`type`	a character.
`inX`	a (list of) numeric vector to specify the variables of X which are always in the model.
`inY`	a (list of) numeric vector to specify the variables of X which are always in the model.
`inXsup`	a (list of) numeric vector to specify the blocks of X which are always in the model.
`inYsup`	a (list of) numeric vector to specify the blocks of Y which are always in the model.
`muX`	a numeric scalar for the weight of X for the supervised.
`muY`	a numeric scalar for the weight of Y for the supervised.
`nfold`	number of folds - default is 5.
`seed`	number of seed for the random number.
`intseed`	seed number for the random number in the parameter estimation algorithm.

Details

k-fold cross-validation for msma

Value

err

The mean cross-validated errors which has three elements consisting of the mean of errors for X and Y, the errors for X and for Y.

Examples

##### data #####
tmpdata = simdata(n = 50, rho = 0.8, Yps = c(10, 12, 15), Xps = 20, seed=1)
X = tmpdata$X; Y = tmpdata$Y 

##### One Component CV #####
cv1 = cvmsma(X, Y, comp = 1, lambdaX=2, lambdaY=1:3, nfold=5, seed=1)
cv1

##### Two Component CV #####
cv2 = cvmsma(X, Y, comp = 2, lambdaX=2, lambdaY=1:3, nfold=5, seed=1)
cv2

##### data #####
tmpdata = simdata(n = 50, rho = 0.8, Yps = c(10, 12, 15), Xps = 20, seed=1)
X = tmpdata$X; Y = tmpdata$Y 

##### One Component CV #####
cv1 = cvmsma(X, Y, comp = 1, lambdaX=2, lambdaY=1:3, nfold=5, seed=1)
cv1

##### Two Component CV #####
cv2 = cvmsma(X, Y, comp = 2, lambdaX=2, lambdaY=1:3, nfold=5, seed=1)
cv2

Hierarchical cluster analysis

Description

This is a function for performing a hierarchical cluster analysis using scores

Usage

hcmsma(
  object,
  nclust = 4,
  graph = FALSE,
  hmethod = "ward.D2",
  axes = c(1, 2),
  block = "block",
  XY = "X"
)
hcmsma(
  object,
  nclust = 4,
  graph = FALSE,
  hmethod = "ward.D2",
  axes = c(1, 2),
  block = "block",
  XY = "X"
)

Arguments

`object`	an object of class "`msma`", usually, a result of a call to `msma`
`nclust`	a numeric scalar, number of clusters.
`graph`	a numeric vector, numbers of columns for Y. The length of vector corresponds to the number of blocks.
`hmethod`	the agglomeration method to be used in the function "`hclust`".
`axes`	a numeric (or vector), specifying the component(s) to analyze.
`block`	a character, indicating which the "block" or "super" is used.
`XY`	a character, indicating "X" or "Y".

Details

This function performs a hierarchical cluster analysis using scores.

Value

`hcout`	An object of class hclust
`clusters`	a vector with group memberships
`object`	an object of class "`msma`", usually, a result of a call to `msma`

Multiblock Sparse Partial Least Squares

Description

This is a function for a matrix decomposition method incorporating sparse and supervised modeling for a multiblock multivariable data analysis

Usage

msma(X, ...)

## Default S3 method:
msma(
  X,
  Y = NULL,
  Z = NULL,
  comp = 2,
  lambdaX = NULL,
  lambdaY = NULL,
  lambdaXsup = NULL,
  lambdaYsup = NULL,
  eta = 1,
  type = "lasso",
  inX = NULL,
  inY = NULL,
  inXsup = NULL,
  inYsup = NULL,
  muX = 0,
  muY = 0,
  defmethod = "canonical",
  scaling = TRUE,
  verbose = FALSE,
  intseed = 1,
  ceps = 1e-04,
  ...
)

## S3 method for class 'msma'
print(x, ...)
msma(X, ...)

## Default S3 method:
msma(
  X,
  Y = NULL,
  Z = NULL,
  comp = 2,
  lambdaX = NULL,
  lambdaY = NULL,
  lambdaXsup = NULL,
  lambdaYsup = NULL,
  eta = 1,
  type = "lasso",
  inX = NULL,
  inY = NULL,
  inXsup = NULL,
  inYsup = NULL,
  muX = 0,
  muY = 0,
  defmethod = "canonical",
  scaling = TRUE,
  verbose = FALSE,
  intseed = 1,
  ceps = 1e-04,
  ...
)

## S3 method for class 'msma'
print(x, ...)

Arguments

`X`	a matrix or list of matrices indicating the explanatory variable(s). This parameter is required.
`...`	further arguments passed to or from other methods.
`Y`	a matrix or list of matrices indicating objective variable(s). This is optional. If there is no input for Y, then PCA is implemented.
`Z`	a vector, response variable(s) for implementing the supervised version of (multiblock) PCA or PLS. This is optional. The length of Z is the number of subjects. If there is no input for Z, then unsupervised PLS/PCA is implemented.
`comp`	numeric scalar for the maximum number of componets to be considered.
`lambdaX`	numeric vector of regularized parameters for X, with a length equal to the number of blocks. If lambdaX is omitted, no regularization is conducted.
`lambdaY`	numeric vector of regularized parameters for Y, with a length equal to the number of blocks. If lambdaY is omitted, no regularization is conducted.
`lambdaXsup`	numeric vector of regularized parameters for the super weight of X with length equal to the number of blocks. If omitted, no regularization is conducted.
`lambdaYsup`	numeric vector of regularized parameters for the super weight of Y with length equal to the number of blocks. If omitted, no regularization is conducted.
`eta`	numeric scalar indicating the parameter indexing the penalty family. This version contains only choice 1.
`type`	a character, indicating the penalty family. In this version, only one choice is available: "lasso."
`inX`	a vector or list of numeric vectors specifying the variables in X, always included in the model
`inY`	a vector or list of numeric vectors specifying the variables in Y, always included in the model
`inXsup`	a (list of) numeric vector to specify the blocks of X which are always in the model.
`inYsup`	a (list of) numeric vector to specify the blocks of Y which are always in the model.
`muX`	a numeric scalar for the weight of X for the supervised case. 0 <= muX <= 1.
`muY`	a numeric scalar for the weight of Y for the supervised case. 0 <= muY <= 1.
`defmethod`	a character representing the deflation method. This version has only the choice "canonical."
`scaling`	a logical, indicating whether or not data scaling is performed. The default is TRUE.
`verbose`	information
`intseed`	seed number for the random number in the parameter estimation algorithm.
`ceps`	a numeric scalar for the convergence condition of the algorithm
`x`	an object of class "`msma`", usually, a result of a call to `msma`

Details

msma requires at least one input X (a matrix or list). In this case, (multiblock) PCA is conducted. If Y is also specified, then a PLS is conducted using X as explanatory variables and Y as objective variables. This function scales each data matrix to a mean of 0 and variance of 1 in the default. The block structure can be represented as a list. If Z is also specified, a supervised version is implemented, and the degree is controlled by muX or muY, where 0 <= muX <= 1, 0 <= muY <= 1, and 0 <= muX + muY < 1. If a positive lambdaX or lambdaY is specified, then a sparse estimation based on the L1 penalty is implemented.

Value

`dmode`	Which modes "PLS" or "PCA"
`X`	Scaled X which has a list form.
`Y`	Scaled Y which has a list form.
`Xscale`	Scaling information for X. The means and standard deviations for each block of X are returned.
`Yscale`	Scaling information for Y. The means and standard deviations for each block of Y are returned.
`comp`	the number of componets
`wbX`	block loading for X
`sbX`	block score for X
`wbY`	block loading for Y
`sbY`	block score for Y
`ssX`	super score for X
`wsX`	super loading for X
`ssY`	super score for Y
`wsY`	super loading for Y
`nzwbX`	number of nonzeros in block loading for X
`nzwbY`	number of nonzeros in block loading for Y
`nzwsX`	number of nonzeros in super loading for X
`nzwsY`	number of nonzeros in super loading for Y
`selectXnames`	names of selected variables for X
`selectYnames`	names of selected variables for Y
`avX`	the adjusted variance of the score for X
`avY`	the adjusted variance of the score for Y
`cpevX`	the cumulative percentage of the explained variance for X
`cpevY`	the cumulative percentage of the explained variance for Y
`reproduct`	Predictivity. Correlation between Y and the predicted Y
`predictiv`	Reproductivity. Correlation between the score for Y and the outcome Z

Examples

##### data #####
tmpdata = simdata(n = 50, rho = 0.8, Yps = c(10, 12, 15), Xps = 20, seed=1)
X = tmpdata$X; Y = tmpdata$Y 

##### One Component #####
fit1 = msma(X, Y, comp=1, lambdaX=2, lambdaY=1:3)
fit1

##### Two Component #####
fit2 = msma(X, Y, comp=2, lambdaX=2, lambdaY=1:3)
fit2

##### Sparse Principal Component Analysis #####
fit3 = msma(X, comp=5, lambdaX=2.5)
summary(fit3)

##### data #####
tmpdata = simdata(n = 50, rho = 0.8, Yps = c(10, 12, 15), Xps = 20, seed=1)
X = tmpdata$X; Y = tmpdata$Y 

##### One Component #####
fit1 = msma(X, Y, comp=1, lambdaX=2, lambdaY=1:3)
fit1

##### Two Component #####
fit2 = msma(X, Y, comp=2, lambdaX=2, lambdaY=1:3)
fit2

##### Sparse Principal Component Analysis #####
fit3 = msma(X, comp=5, lambdaX=2.5)
summary(fit3)

Search for Number of Components

Description

Determination of the number of components based on cross-validated method or Bayesian information criterion (BIC)

Usage

ncompsearch(
  X,
  Y = NULL,
  Z = NULL,
  comps = 1:3,
  lambdaX = NULL,
  lambdaY = NULL,
  lambdaXsup = NULL,
  lambdaYsup = NULL,
  eta = 1,
  type = "lasso",
  inX = NULL,
  inY = NULL,
  inXsup = NULL,
  inYsup = NULL,
  muX = 0,
  muY = 0,
  nfold = 5,
  regpara = FALSE,
  maxrep = 3,
  minpct = 0,
  maxpct = 1,
  criterion = c("CV", "BIC")[1],
  whichselect = NULL,
  intseed = 1
)

## S3 method for class 'ncompsearch'
print(x, ...)

## S3 method for class 'ncompsearch'
plot(x, cidx = 1, ...)
ncompsearch(
  X,
  Y = NULL,
  Z = NULL,
  comps = 1:3,
  lambdaX = NULL,
  lambdaY = NULL,
  lambdaXsup = NULL,
  lambdaYsup = NULL,
  eta = 1,
  type = "lasso",
  inX = NULL,
  inY = NULL,
  inXsup = NULL,
  inYsup = NULL,
  muX = 0,
  muY = 0,
  nfold = 5,
  regpara = FALSE,
  maxrep = 3,
  minpct = 0,
  maxpct = 1,
  criterion = c("CV", "BIC")[1],
  whichselect = NULL,
  intseed = 1
)

## S3 method for class 'ncompsearch'
print(x, ...)

## S3 method for class 'ncompsearch'
plot(x, cidx = 1, ...)

Arguments

`X`	a matrix or list of matrices indicating the explanatory variable(s). This parameter is required.
`Y`	a matrix or list of matrices indicating objective variable(s). This is optional. If there is no input for Y, then PCA is implemented.
`Z`	a vector, response variable(s) for implementing the supervised version of (multiblock) PCA or PLS. This is optional. The length of Z is the number of subjects. If there is no input for Z, then unsupervised PLS/PCA is implemented.
`comps`	numeric vector for the maximum numbers of componets to be considered.
`lambdaX`	numeric vector of regularized parameters for X, with a length equal to the number of blocks. If lambdaX is omitted, no regularization is conducted.
`lambdaY`	numeric vector of regularized parameters for Y, with a length equal to the number of blocks. If lambdaY is omitted, no regularization is conducted.
`lambdaXsup`	numeric vector of regularized parameters for the super weight of X with length equal to the number of blocks. If omitted, no regularization is conducted.
`lambdaYsup`	numeric vector of regularized parameters for the super weight of Y with length equal to the number of blocks. If omitted, no regularization is conducted.
`eta`	numeric scalar indicating the parameter indexing the penalty family. This version contains only choice 1.
`type`	a character, indicating the penalty family. In this version, only one choice is available: "lasso."
`inX`	a (list of) numeric vector to specify the variables of X which are always in the model.
`inY`	a (list of) numeric vector to specify the variables of X which are always in the model.
`inXsup`	a (list of) numeric vector to specify the blocks of X which are always in the model.
`inYsup`	a (list of) numeric vector to specify the blocks of Y which are always in the model.
`muX`	a numeric scalar for the weight of X for the supervised case. 0 <= muX <= 1.
`muY`	a numeric scalar for the weight of Y for the supervised case. 0 <= muY <= 1.
`nfold`	number of folds - default is 5.
`regpara`	logical, If TRUE, the regularized parameters search is also conducted simultaneously.
`maxrep`	numeric scalar for the number of iteration.
`minpct`	minimum candidate parameters defined as a percentile of automatically determined (possible) candidates.
`maxpct`	maximum candidate parameters defined as a percentile of automatically determined (possible) candidates.
`criterion`	a character, the evaluation criterion, "CV" for cross-validation, based on a matrix element-wise error, and "BIC" for Bayesian information criteria. The "BIC" is the default.
`whichselect`	which blocks selected.
`intseed`	seed number for the random number in the parameter estimation algorithm.
`x`	an object of class "`ncompsearch`", usually, a result of a call to `ncompsearch`
`...`	further arguments passed to or from other methods.
`cidx`	Parameters used in the plot function to specify whether block or super is used. 1=block (default), 2=super.

Details

This function searches for the optimal number of components.

Value

`comps`	numbers of components
`mincriterion`	minimum criterion values
`criterions`	criterion values
`optncomp`	optimal number of components based on minimum cross-validation error

Examples

##### data #####
tmpdata = simdata(n = 50, rho = 0.8, Yps = c(10, 12, 15), Xps = 20, seed=1)
X = tmpdata$X; Y = tmpdata$Y 

##### number of components search #####
ncomp1 = ncompsearch(X, Y, comps = c(1, 5, 10*(1:2)), nfold=5)
#plot(ncomp1)

##### data #####
tmpdata = simdata(n = 50, rho = 0.8, Yps = c(10, 12, 15), Xps = 20, seed=1)
X = tmpdata$X; Y = tmpdata$Y 

##### number of components search #####
ncomp1 = ncompsearch(X, Y, comps = c(1, 5, 10*(1:2)), nfold=5)
#plot(ncomp1)

Parameters Search

Description

Combined method for optimizing the number of components and regularized parameters for "msma".

Usage

optparasearch(
  X,
  Y = NULL,
  Z = NULL,
  search.method = c("ncomp1st", "regpara1st", "regparaonly", "simultaneous")[1],
  eta = 1,
  type = "lasso",
  inX = NULL,
  inY = NULL,
  muX = 0,
  muY = 0,
  comp = 10,
  nfold = 5,
  maxrep = 3,
  minpct = 0,
  maxpct = 1,
  maxpct4ncomp = NULL,
  criterion = c("BIC", "CV")[1],
  criterion4ncomp = NULL,
  whichselect = NULL,
  homo = NULL,
  intseed = 1
)

## S3 method for class 'optparasearch'
print(x, ...)
optparasearch(
  X,
  Y = NULL,
  Z = NULL,
  search.method = c("ncomp1st", "regpara1st", "regparaonly", "simultaneous")[1],
  eta = 1,
  type = "lasso",
  inX = NULL,
  inY = NULL,
  muX = 0,
  muY = 0,
  comp = 10,
  nfold = 5,
  maxrep = 3,
  minpct = 0,
  maxpct = 1,
  maxpct4ncomp = NULL,
  criterion = c("BIC", "CV")[1],
  criterion4ncomp = NULL,
  whichselect = NULL,
  homo = NULL,
  intseed = 1
)

## S3 method for class 'optparasearch'
print(x, ...)

Arguments

`X`	a matrix or list of matrices indicating the explanatory variable(s). This parameter is required.
`Y`	a matrix or list of matrices indicating objective variable(s). This is optional. If there is no input for Y, then PCA is implemented.
`Z`	a vector, response variable(s) for implementing the supervised version of (multiblock) PCA or PLS. This is optional. The length of Z is the number of subjects. If there is no input for Z, then unsupervised PLS/PCA is implemented.
`search.method`	a character indicationg search methods, see Details. Default is "ncomp1st" (this is version 3.0 or later).
`eta`	numeric scalar indicating the parameter indexing the penalty family. This version contains only choice 1.
`type`	a character, indicating the penalty family. In this version, only one choice is available: "lasso."
`inX`	a vector or list of numeric vectors specifying the variables in X, always included in the model
`inY`	a vector or list of numeric vectors specifying the variables in Y, always included in the model
`muX`	a numeric scalar for the weight of X for the supervised case. 0 <= muX <= 1.
`muY`	a numeric scalar for the weight of Y for the supervised case. 0 <= muY <= 1.
`comp`	numeric scalar for the number of components to be considered or the maximum canditate number of components.
`nfold`	number of folds - default is 5.
`maxrep`	numeric scalar for the number of iteration.
`minpct`	minimum candidate parameters defined as a percentile of automatically determined (possible) candidates.
`maxpct`	maximum candidate parameters defined as a percentile of automatically determined (possible) candidates.
`maxpct4ncomp`	maximum candidate parameters defined as a percentile of automatically determined (possible) candidates.
`criterion`	a character, the evaluation criterion, "CV" for cross-validation, based on a matrix element-wise error, and "BIC" for Bayesian information criteria. The "BIC" is the default.
`criterion4ncomp`	a character, the evaluation criterion for the selection of the number of components, "CV" for cross-validation, based on a matrix element-wise error, and "BIC" for Bayesian information criteria.
`whichselect`	which blocks selected.
`homo`	same parameters.
`intseed`	seed number for the random number in the parameter estimation algorithm.
`x`	an object of class "`optparasearch`", usually, a result of a call to `optparasearch`
`...`	further arguments passed to or from other methods.

Details

A function for identifying the regularized sparseness parameters lambdaX and lambdaY and the number of components for msma. Four search methods are available. The "simultaneous" method identifies the number of components by searching the regularized parameters in each component. The "regpara1st" identifies the regularized parameters by fixing the number of components, then searching for the number of components with the selected regularized parameters. The "ncomp1st" method identifies the number of components with a regularized parameter of 0, then searches for the regularized parameters with the selected number of components. The "regparaonly" method searches for the regularized parameters with a fixed number of components.

Value

`optncomp`	Optimal number of components
`optlambdaX`	Optimal parameters for X
`optlambdaY`	Optimal parameters for Y
`mincriterion`	Minimum criterion value
`criteria`	All resulting criterion values in the process
`pararange`	Range of candidates parameters

Examples

##### data #####
tmpdata = simdata(n = 50, rho = 0.8, Yps = c(10, 12, 15), Xps = 20, seed=1)
X = tmpdata$X; Y = tmpdata$Y 

##### Regularized parameters search #####
opt1 = optparasearch(X, Y, search.method = "regparaonly", comp=1, nfold=5, maxrep=2)
opt1
fit4 = msma(X, Y, comp=opt1$optncomp, lambdaX=opt1$optlambdaX, lambdaY=opt1$optlambdaY)
fit4
summary(fit4)

##### Restrict search range #####
opt2 = optparasearch(X, Y, comp=3, nfold=5, maxrep=2, minpct=0.5)
opt2

##### data #####
tmpdata = simdata(n = 50, rho = 0.8, Yps = c(10, 12, 15), Xps = 20, seed=1)
X = tmpdata$X; Y = tmpdata$Y 

##### Regularized parameters search #####
opt1 = optparasearch(X, Y, search.method = "regparaonly", comp=1, nfold=5, maxrep=2)
opt1
fit4 = msma(X, Y, comp=opt1$optncomp, lambdaX=opt1$optlambdaX, lambdaY=opt1$optlambdaY)
fit4
summary(fit4)

##### Restrict search range #####
opt2 = optparasearch(X, Y, comp=3, nfold=5, maxrep=2, minpct=0.5)
opt2

Plot msma

Description

plot method for class "msma".

Usage

## S3 method for class 'msma'
plot(
  x,
  v = c("weight", "score", "cpev")[1],
  axes = 1,
  axes2 = 1,
  block = c("block", "super")[1],
  plottype = c("bar", "scatter")[1],
  XY = c("X", "Y", "XY")[1],
  col = NULL,
  signflip = FALSE,
  xlim = NULL,
  ylim = NULL,
  ...
)
## S3 method for class 'msma'
plot(
  x,
  v = c("weight", "score", "cpev")[1],
  axes = 1,
  axes2 = 1,
  block = c("block", "super")[1],
  plottype = c("bar", "scatter")[1],
  XY = c("X", "Y", "XY")[1],
  col = NULL,
  signflip = FALSE,
  xlim = NULL,
  ylim = NULL,
  ...
)

Arguments

`x`	an object of class "`msma`." Usually, a result of a call to `msma`
`v`	a character, "weight" for the weight, "score" for the score, and "cpev" for the cumulative percentage of explained variance (CPEV) .
`axes`	a numeric (or vector), specifying the root component(s) to plot.
`axes2`	a numeric (or vector), specifying the nested component(s) to plot.
`block`	a character, indicating which the "block" or "super" is used.
`plottype`	a character, indicating the plot type. "bar" for the bar plot, "scatter" for the scatter plot.
`XY`	a character, indicating "X" or "Y". "XY" for the scatter plots using X and Y scores from PLS.
`col`	a color vector.
`signflip`	a numeric vector if the sign in the block is flipped to pose the super as possitive.
`xlim`	a numeric vector x coordinate ranges.
`ylim`	a numeric vector y coordinate ranges.
`...`	further arguments passed to or from other methods.

Details

This function provides a plot of results.

Examples

tmpdata = simdata(n = 50, rho = 0.8, Yps = c(10, 12, 15), Xps = 20, seed=1)
X = tmpdata$X; Y = tmpdata$Y 

fit1 = msma(X, Y, comp=1, lambdaX=2, lambdaY=1:3)
#plot(fit1)

tmpdata = simdata(n = 50, rho = 0.8, Yps = c(10, 12, 15), Xps = 20, seed=1)
X = tmpdata$X; Y = tmpdata$Y 

fit1 = msma(X, Y, comp=1, lambdaX=2, lambdaY=1:3)
#plot(fit1)

Prediction

Description

predict method for class "msma".

Usage

## S3 method for class 'msma'
predict(object, newX, newY = NULL, ...)
## S3 method for class 'msma'
predict(object, newX, newY = NULL, ...)

Arguments

`object`	an object of class "`msma`", usually, a result of a call to `msma`
`newX`	a matrix in which to look for variables with which to predict X.
`newY`	a matrix in which to look for variables with which to predict Y.
`...`	further arguments passed to or from other methods.

Details

This function produces a prediction from new data based on msma fit. It is mainly used in cross-validation

Value

`X`	predicted X
`sbX`	block score for X
`Y`	predicted Y
`sbY`	block score for Y

Examples

##### data #####
tmpdata = simdata(n = 50, rho = 0.8, Yps = c(10, 12, 15), Xps = 20, seed=1)
X = tmpdata$X; Y = tmpdata$Y 

##### Two Component #####
fit2 = msma(X, Y, comp=2, lambdaX=2, lambdaY=1:3)
summary(fit2)

##### Predict #####
test = predict(fit2, newX=X, newY=Y)

##### data #####
tmpdata = simdata(n = 50, rho = 0.8, Yps = c(10, 12, 15), Xps = 20, seed=1)
X = tmpdata$X; Y = tmpdata$Y 

##### Two Component #####
fit2 = msma(X, Y, comp=2, lambdaX=2, lambdaY=1:3)
summary(fit2)

##### Predict #####
test = predict(fit2, newX=X, newY=Y)

Regularized Parameters Search

Description

Regularized parameters search method for "msma".

Usage

regparasearch(
  X,
  Y = NULL,
  Z = NULL,
  eta = 1,
  type = "lasso",
  inX = NULL,
  inY = NULL,
  inXsup = NULL,
  inYsup = NULL,
  muX = 0,
  muY = 0,
  comp = 1,
  nfold = 5,
  maxrep = 3,
  minpct = 0,
  maxpct = 1,
  criterion = c("CV", "BIC")[1],
  whichselect = NULL,
  homo = NULL,
  intseed = 1
)

## S3 method for class 'regparasearch'
print(x, ...)
regparasearch(
  X,
  Y = NULL,
  Z = NULL,
  eta = 1,
  type = "lasso",
  inX = NULL,
  inY = NULL,
  inXsup = NULL,
  inYsup = NULL,
  muX = 0,
  muY = 0,
  comp = 1,
  nfold = 5,
  maxrep = 3,
  minpct = 0,
  maxpct = 1,
  criterion = c("CV", "BIC")[1],
  whichselect = NULL,
  homo = NULL,
  intseed = 1
)

## S3 method for class 'regparasearch'
print(x, ...)

Arguments

`X`	a matrix or list of matrices indicating the explanatory variable(s). This parameter is required.
`Y`	a matrix or list of matrices indicating objective variable(s). This is optional. If there is no input for Y, then PCA is implemented.
`Z`	a vector, response variable(s) for implementing the supervised version of (multiblock) PCA or PLS. This is optional. The length of Z is the number of subjects. If there is no input for Z, then unsupervised PLS/PCA is implemented.
`eta`	numeric scalar indicating the parameter indexing the penalty family. This version contains only choice 1.
`type`	a character, indicating the penalty family. In this version, only one choice is available: "lasso."
`inX`	a (list of) numeric vector to specify the variables of X which are always in the model.
`inY`	a (list of) numeric vector to specify the variables of X which are always in the model.
`inXsup`	a (list of) numeric vector to specify the blocks of X which are always in the model.
`inYsup`	a (list of) numeric vector to specify the blocks of Y which are always in the model.
`muX`	a numeric scalar for the weight of X for the supervised case. 0 <= muX <= 1.
`muY`	a numeric scalar for the weight of Y for the supervised case. 0 <= muY <= 1.
`comp`	numeric scalar for the maximum number of componets to be considered.
`nfold`	number of folds. Default is 5.
`maxrep`	numeric scalar for the number of iteration.
`minpct`	percent of minimum candidate parameters.
`maxpct`	percent of maximum candidate parameters.
`criterion`	a character, the evaluation criterion, "CV" for cross-validation, based on a matrix element-wise error, and "BIC" for Bayesian information criteria. The "BIC" is the default.
`whichselect`	which blocks selected.
`homo`	same parameters.
`intseed`	seed number for the random number in the parameter estimation algorithm.
`x`	an object of class "`regparasearch`", usually, a result of a call to `regparasearch`
`...`	further arguments passed to or from other methods.

Details

This is a function for identifying the regularized parameters of sparseness lambdaX and lambdaY for msma. The initial range of candidates is computed based on fit, with regularized parameter values of 0. A binary search is conducted for dividing the parameter range into two regions. The representative value for the region is a median value, and the optimal region is selected using the minimum criteria obtained from the fit with that median value. The CV error or BIC can be used as criteria. The selected region is also divided into two region and the same process is iterated by maxrep times. Thus, the final median value in the selected region is set to be the optimal regularized parameter. The search is conducted with combinations of parameters for X and Y. The range of candidates for regularized parameters can be restricted, with a percentile of the limit (minimum or maximum) for the range.

Value

`optlambdaX`	Optimal parameters for X
`optlambdaY`	Optimal parameters for Y
`mincriterion`	Minimum of criterion values
`criterions`	Resulting criterion value
`pararange`	Range of candidates parameters

Examples

##### data #####
tmpdata = simdata(n = 50, rho = 0.8, Yps = c(10, 12, 15), Xps = c(20, 15), seed=1)
X = tmpdata$X; Y = tmpdata$Y 

##### Regularized parameters search #####
opt1 = regparasearch(X, Y, comp=1, criterion="BIC", maxrep=2, 
whichselect=c("X", "Y", "Xsup", "Ysup"))
opt1
fit4 = msma(X, Y, comp=1, lambdaX=opt1$optlambdaX, lambdaY=opt1$optlambdaY, 
lambdaXsup=opt1$optlambdaXsup, lambdaYsup=opt1$optlambdaYsup)
fit4
summary(fit4)

##### data #####
tmpdata = simdata(n = 50, rho = 0.8, Yps = c(10, 12, 15), Xps = c(20, 15), seed=1)
X = tmpdata$X; Y = tmpdata$Y 

##### Regularized parameters search #####
opt1 = regparasearch(X, Y, comp=1, criterion="BIC", maxrep=2, 
whichselect=c("X", "Y", "Xsup", "Ysup"))
opt1
fit4 = msma(X, Y, comp=1, lambdaX=opt1$optlambdaX, lambdaY=opt1$optlambdaY, 
lambdaXsup=opt1$optlambdaXsup, lambdaYsup=opt1$optlambdaYsup)
fit4
summary(fit4)

Simulate Data sets

Description

This is a function for generating multiblock data based on the multivariable normal distribusion

Usage

simdata(n = 100, rho = 0.8, Yps = c(100, 120, 150), Xps = 500, seed = 1)
simdata(n = 100, rho = 0.8, Yps = c(100, 120, 150), Xps = 500, seed = 1)

Arguments

`n`	a numeric scalar, sample size.
`rho`	a numeric scalar, correlation coefficient.
`Yps`	a numeric vector, numbers of columns for Y. The length of vector corresponds to the number of blocks.
`Xps`	a numeric vector, numbers of columns for X. The length of vector corresponds to the number of blocks.
`seed`	a seed number for generating random numbers.

Details

The output is a list of matrics.

Value

`X`	Simulated X which has a list form
`Y`	Simulated Y which has a list form

Structured Simulate Data sets

Description

This is a function for generating multiblock data based on the multivariable normal distribusion

Usage

strsimdata(
  n = 100,
  WX = NULL,
  ncomp = 5,
  Xps = 10,
  Yps = FALSE,
  rho = 0.8,
  Ztype = c("none", "binary", "prob")[1],
  cz = c(1, 1),
  cwx = c(0.1, 0.1),
  cwy = c(0.1, 0.1),
  seed = 1,
  minpct = 0.25,
  maxpct = 0.75
)
strsimdata(
  n = 100,
  WX = NULL,
  ncomp = 5,
  Xps = 10,
  Yps = FALSE,
  rho = 0.8,
  Ztype = c("none", "binary", "prob")[1],
  cz = c(1, 1),
  cwx = c(0.1, 0.1),
  cwy = c(0.1, 0.1),
  seed = 1,
  minpct = 0.25,
  maxpct = 0.75
)

Arguments

`n`	a numeric scalar, sample size.
`WX`	a matrix or a list, weights.
`ncomp`	number of components
`Xps`	a numeric vector, numbers of columns for X. The length of vector corresponds to the number of blocks.
`Yps`	a numeric vector, numbers of columns for Y. The length of vector corresponds to the number of blocks.
`rho`	a numeric, correlation
`Ztype`	a character, outcome type ("none", "binary", "prob").
`cz`	a numeric vector, scale for outcome
`cwx`	a numeric vector, scale for weights of X
`cwy`	a numeric vector, scale for weights of Y
`seed`	a seed number for generating random numbers.
`minpct`	minimum percent of nonzero
`maxpct`	maximum percent of nonzero

Details

The output is a list of matrics.

Value

`X`	Simulated X which has a list form
`Y`	Simulated Y which has a list form
`Z`	Simulated Z which has a vector form
`ncomp`
`Xps`
`nZeroX`
`idxZeroX`
`Yps`
`nZeroY`
`idxZeroY`
`WX`
`WY`
`ZcoefX`
`ZcoefY`

Summarizing Fits

Description

summary method for class "msma".

Usage

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

## S3 method for class 'summary.msma'
print(x, ...)
## S3 method for class 'msma'
summary(object, ...)

## S3 method for class 'summary.msma'
print(x, ...)

Arguments

`object`, `x`	an object of class "`msma`", usually, a result of a call to `msma`
`...`	further arguments passed to or from other methods.

Details

This function provide the summary of results .

Examples

##### data #####
tmpdata = simdata(n = 50, rho = 0.8, Yps = c(10, 12, 15), Xps = 20, seed=1)
X = tmpdata$X; Y = tmpdata$Y 

##### One Component #####
fit1 = msma(X, Y, comp=1, lambdaX=2, lambdaY=1:3)
summary(fit1)

##### data #####
tmpdata = simdata(n = 50, rho = 0.8, Yps = c(10, 12, 15), Xps = 20, seed=1)
X = tmpdata$X; Y = tmpdata$Y 

##### One Component #####
fit1 = msma(X, Y, comp=1, lambdaX=2, lambdaY=1:3)
summary(fit1)

Package 'msma'

Help Index

Multiblock Sparse Matrix Analysis Package

Description

Author(s)

References

See Also

Cross-Validation

Description

Usage

Arguments

Details

Value

Examples

Hierarchical cluster analysis

Description

Usage

Arguments

Details

Value

Multiblock Sparse Partial Least Squares

Description

Usage

Arguments

Details

Value

Examples

Search for Number of Components

Description

Usage

Arguments

Details

Value

Examples

Parameters Search

Description

Usage

Arguments

Details

Value

Examples

Plot msma

Description

Usage

Arguments

Details

Examples

Prediction

Description

Usage

Arguments

Details

Value

Examples

Regularized Parameters Search

Description

Usage

Arguments

Details

Value

Examples

Simulate Data sets

Description

Usage

Arguments

Details

Value

Structured Simulate Data sets

Description

Usage

Arguments

Details

Value

Summarizing Fits

Description

Usage

Arguments

Details

Examples