parafac: Parallel Factor Analysis-1

Description Usage Arguments Details Value Warnings Note Author(s) References See Also Examples

View source: R/parafac.R

Description

Fits Richard A. Harshman's Parallel Factors (Parafac) model to 3-way or 4-way data arrays. Parameters are estimated via alternating least squares with optional constraints.

Usage

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parafac(X, nfac, nstart = 10, const = NULL, control = NULL,
        Afixed = NULL, Bfixed = NULL, Cfixed = NULL, Dfixed = NULL,
        Astart = NULL, Bstart = NULL, Cstart = NULL, Dstart = NULL,
        Astruc = NULL, Bstruc = NULL, Cstruc = NULL, Dstruc = NULL,
        Amodes = NULL, Bmodes = NULL, Cmodes = NULL, Dmodes = NULL,
        maxit = 500, ctol = 1e-4, parallel = FALSE, cl = NULL,
        output = c("best", "all"), verbose = TRUE, backfit = FALSE)

Arguments

X

Three-way data array with dim=c(I,J,K) or four-way data array with dim=c(I,J,K,L). Missing data are allowed (see Note).

nfac

Number of factors.

nstart

Number of random starts.

const

Character vector of length 3 or 4 giving the constraints for each mode (defaults to unconstrained). See const for the 24 available options.

control

List of parameters controlling options for smoothness constraints. This is passed to const.control, which describes the available options.

Afixed

Used to fit model with fixed Mode A weights.

Bfixed

Used to fit model with fixed Mode B weights.

Cfixed

Used to fit model with fixed Mode C weights.

Dfixed

Used to fit model with fixed Mode D weights.

Astart

Starting Mode A weights. Default uses random weights.

Bstart

Starting Mode B weights. Default uses random weights.

Cstart

Starting Mode C weights. Default uses random weights.

Dstart

Starting Mode D weights. Default uses random weights.

Astruc

Structure constraints for Mode A weights. See Note.

Bstruc

Structure constraints for Mode B weights. See Note.

Cstruc

Structure constraints for Mode C weights. See Note.

Dstruc

Structure constraints for Mode D weights. See Note.

Amodes

Mode ranges for Mode A weights (for unimodality constraints). See Note.

Bmodes

Mode ranges for Mode B weights (for unimodality constraints). See Note.

Cmodes

Mode ranges for Mode C weights (for unimodality constraints). See Note.

Dmodes

Mode ranges for Mode D weights (for unimodality constraints). See Note.

maxit

Maximum number of iterations.

ctol

Convergence tolerance (R^2 change).

parallel

Logical indicating if parLapply should be used. See Examples.

cl

Cluster created by makeCluster. Only used when parallel=TRUE.

output

Output the best solution (default) or output all nstart solutions.

verbose

If TRUE, fitting progress is printed via txtProgressBar. Ignored if parallel=TRUE.

backfit

Should backfitting algorithm be used for cmls?

Details

Given a 3-way array X = array(x, dim = c(I,J,K)), the 3-way Parafac model can be written as

X[i,j,k] = sum A[i,r]*B[j,r]*C[k,r] + E[i,j,k]

where A = matrix(a,I,R) are the Mode A (first mode) weights, B = matrix(b,J,R) are the Mode B (second mode) weights, C = matrix(c,K,R) are the Mode C (third mode) weights, and E = array(e,dim=c(I,J,K)) is the 3-way residual array. The summation is for r = seq(1,R).

Given a 4-way array X = array(x, dim = c(I,J,K,L)), the 4-way Parafac model can be written as

X[i,j,k,l] = sum A[i,r]*B[j,r]*C[k,r]*D[l,r] + E[i,j,k,l]

where D = matrix(d,L,R) are the Mode D (fourth mode) weights, E = array(e,dim=c(I,J,K,L)) is the 4-way residual array, and the other terms can be interprered as previously described.

Weight matrices are estimated using an alternating least squares algorithm with optional constraints.

Value

If output = "best", returns an object of class "parafac" with the following elements:

A

Mode A weight matrix.

B

Mode B weight matrix.

C

Mode C weight matrix.

D

Mode D weight matrix.

SSE

Sum of Squared Errors.

Rsq

R-squared value.

GCV

Generalized Cross-Validation.

edf

Effective degrees of freedom.

iter

Number of iterations.

cflag

Convergence flag. See Note.

const

See argument const.

control

See argument control.

fixed

Logical vector indicating whether 'fixed' weights were used for each mode.

struc

Logical vector indicating whether 'struc' constraints were used for each mode.

Otherwise returns a list of length nstart where each element is an object of class "parafac".

Warnings

The algorithm can perform poorly if the number of factors nfac is set too large.

Note

Missing data should be specified as NA values in the input X. The missing data are randomly initialized and then iteratively imputed as a part of the algorithm.

Structure constraints should be specified with a matrix of logicals (TRUE/FALSE), such that FALSE elements indicate a weight should be constrained to be zero. Default uses unstructured weights, i.e., a matrix of all TRUE values.

When using unimodal constraints, the *modes inputs can be used to specify the mode search range for each factor. These inputs should be matrices with dimension c(2,nfac) where the first row gives the minimum mode value and the second row gives the maximum mode value (with respect to the indicies of the corresponding weight matrix).

Output cflag gives convergence information: cflag = 0 if algorithm converged normally, cflag = 1 if maximum iteration limit was reached before convergence, and cflag = 2 if algorithm terminated abnormally due to a problem with the constraints.

Author(s)

Nathaniel E. Helwig <[email protected]>

References

Harshman, R. A. (1970). Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multimodal factor analysis. UCLA Working Papers in Phonetics, 16, 1-84.

Harshman, R. A., & Lundy, M. E. (1994). PARAFAC: Parallel factor analysis. Computational Statistics and Data Analysis, 18, 39-72.

Helwig, N. E. (2017). Estimating latent trends in multivariate longitudinal data via Parafac2 with functional and structural constraints. Biometrical Journal, 59(4), 783-803.

Helwig, N. E. (in prep). Constrained parallel factor analysis via the R package multiway.

See Also

The fitted.parafac function creates the model-implied fitted values from a fit "parafac" object.

The resign.parafac function can be used to resign factors from a fit "parafac" object.

The rescale.parafac function can be used to rescale factors from a fit "parafac" object.

The reorder.parafac function can be used to reorder factors from a fit "parafac" object.

The cmls function (from CMLS package) is called as a part of the alternating least squares algorithm.

Examples

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##########   3-way example   ##########

# create random data array with Parafac structure
set.seed(3)
mydim <- c(50, 20, 5)
nf <- 3
Amat <- matrix(rnorm(mydim[1]*nf), nrow = mydim[1], ncol = nf)
Bmat <- matrix(runif(mydim[2]*nf), nrow = mydim[2], ncol = nf)
Cmat <- matrix(runif(mydim[3]*nf), nrow = mydim[3], ncol = nf)
Xmat <- tcrossprod(Amat, krprod(Cmat, Bmat))
Xmat <- array(Xmat, dim = mydim)
Emat <- array(rnorm(prod(mydim)), dim = mydim)
Emat <- nscale(Emat, 0, ssnew = sumsq(Xmat))   # SNR = 1
X <- Xmat + Emat

# fit Parafac model (unconstrained)
pfac <- parafac(X, nfac = nf, nstart = 1)
pfac

# fit Parafac model (non-negativity on Modes B and C)
pfacNN <- parafac(X, nfac = nf, nstart = 1, 
                  const = c("uncons", "nonneg", "nonneg"))
pfacNN

# check solution
Xhat <- fitted(pfac)
sum((Xmat - Xhat)^2) / prod(mydim)

# reorder and resign factors
pfac$B[1:4,]
pfac <- reorder(pfac, c(3,1,2))
pfac$B[1:4,]
pfac <- resign(pfac, mode="B")
pfac$B[1:4,]
Xhat <- fitted(pfac)
sum((Xmat - Xhat)^2) / prod(mydim)

# rescale factors
colSums(pfac$B^2)
colSums(pfac$C^2)
pfac <- rescale(pfac, mode = "C", absorb = "B")
colSums(pfac$B^2)
colSums(pfac$C^2)
Xhat <- fitted(pfac)
sum((Xmat - Xhat)^2) / prod(mydim)


##########   4-way example   ##########

# create random data array with Parafac structure
set.seed(4)
mydim <- c(30,10,8,10)
nf <- 4
aseq <- seq(-3, 3, length.out = mydim[1])
Amat <- cbind(dnorm(aseq), dchisq(aseq+3.1, df=3),
              dt(aseq-2, df=4), dgamma(aseq+3.1, shape=3, rate=1))
Bmat <- svd(matrix(runif(mydim[2]*nf), nrow = mydim[2], ncol = nf), nv = 0)$u
Cmat <- matrix(runif(mydim[3]*nf), nrow = mydim[3], ncol = nf)
Cstruc <- Cmat > 0.5
Cmat <- Cmat * Cstruc
Dmat <- matrix(runif(mydim[4]*nf), nrow = mydim[4], ncol = nf)
Xmat <- tcrossprod(Amat, krprod(Dmat, krprod(Cmat, Bmat)))
Xmat <- array(Xmat, dim = mydim)
Emat <- array(rnorm(prod(mydim)), dim = mydim)
Emat <- nscale(Emat, 0, ssnew = sumsq(Xmat))   # SNR = 1
X <- Xmat + Emat

# fit Parafac model (unimodal and smooth A, orthogonal B, 
#                    non-negative and structured C, non-negative D)
pfac <- parafac(X, nfac = nf, nstart = 1, Cstruc = Cstruc, 
                const = c("unismo", "orthog", "nonneg", "nonneg"))
pfac

# check solution
Xhat <- fitted(pfac)
sum((Xmat - Xhat)^2) / prod(mydim)
congru(Amat, pfac$A)
crossprod(pfac$B)
pfac$C
Cstruc

## Not run: 

##########   parallel computation   ##########

# create random data array with Parafac structure
set.seed(3)
mydim <- c(50,20,5)
nf <- 3
Amat <- matrix(rnorm(mydim[1]*nf), nrow = mydim[1], ncol = nf)
Bmat <- matrix(runif(mydim[2]*nf), nrow = mydim[2], ncol = nf)
Cmat <- matrix(runif(mydim[3]*nf), nrow = mydim[3], ncol = nf)
Xmat <- tcrossprod(Amat, krprod(Cmat, Bmat))
Xmat <- array(Xmat, dim = mydim)
Emat <- array(rnorm(prod(mydim)), dim = mydim)
Emat <- nscale(Emat, 0, ssnew = sumsq(Xmat))   # SNR = 1
X <- Xmat + Emat

# fit Parafac model (10 random starts -- sequential computation)
set.seed(1)
system.time({pfac <- parafac(X, nfac = nf)})
pfac

# fit Parafac model (10 random starts -- parallel computation)
cl <- makeCluster(detectCores())
ce <- clusterEvalQ(cl, library(multiway))
clusterSetRNGStream(cl, 1)
system.time({pfac <- parafac(X, nfac = nf, parallel = TRUE, cl = cl)})
pfac
stopCluster(cl)

## End(Not run)

multiway documentation built on June 11, 2018, 1:09 a.m.