sMB-PLS is an adaptation of MB-PLS (mbpls
) that enforces sparseness in loading weights
when computing PLS components in the global model.
smbpls(
formula,
data,
subset,
na.action,
X = NULL,
Y = NULL,
ncomp = 1,
scale = FALSE,
shrink = NULL,
truncation = NULL,
trunc.width = 0.95,
blockScale = c("sqrtnvar", "ssq", "none"),
...
)
Model formula accepting a single response (block) and predictor block names separated by + signs.
The data set to analyse.
Expression for subsetting the data before modelling.
How to handle NAs (no action implemented).
list
of input blocks. If X is supplied, the formula interface is skipped.
matrix
of responses.
integer
number of PLS components.
logical
for autoscaling inputs (default = FALSE).
numeric
scalar indicating degree of L1-shrinkage/Soft-Thresholding (optional), 0 <= shrink < 1.
character
indicating type of truncation (optional) "Lenth" uses
asymmetric confidence intervals to determine outlying loading weights. "quantile" uses
a quantile plot approach to determining outliers.
numeric
indicating confidence of "Lenth type" confidence interval
or quantile in "quantile plot" approach. Default = 0.95.
Either a character
indicating type of block scaling or a numeric
vector of block weights (see Details).
additional arguments to pls::plsr.
multiblock, mvr
object with super-scores, super-loadings, block-scores and block-loading, and the underlying
mvr
(PLS) object for the super model, with all its result and plot possibilities. Relevant plotting functions: multiblock_plots
and result functions: multiblock_results
.
Two versions of sparseness are supplied: Soft-Threshold PLS, also known as Sparse PLS, and Truncation PLS. The former uses L1 shrinkage of loading weights, while the latter comes in two flavours, both estimating inliers and outliers. The "Lenth" method uses asymmetric confidence intervals around the median of a loading weigh vector to estimate inliers. The "quantile" method uses a quantile plot approach to estimate outliers as deviations from the estimated quantile line. As with ordinary MB-PLS scaled input blocks (1/sqrt(ncol)) are used.
Block weighting is performed after scaling all variables and is by default
"sqrtnvar"
: 1/sqrt(ncol(X[[i]])) in each block. Alternatives
are "ssq"
: 1/norm(X[[i]], "F")^2 and "none"
: 1/1. Finally, if
a numeric
vector is supplied, it will be used to scale the blocks
after "ssq"
scaling, i.e., Z[[i]] = X[[i]] / norm(X[[i]], "F")^2 * blockScale[i].
Sæbø, S.; Almøy, T.; Aarøe, J. & Aastveit, A. ST-PLS: a multi-directional nearest shrunken centroid type classifier via PLS Journal of Chemometrics: A Journal of the Chemometrics Society, Wiley Online Library, 2008, 22, 54-62.
Lê Cao, K.; Rossouw, D.; Robert-Granié, C. & Besse, P. A sparse PLS for variable selection when integrating omics data Statistical applications in genetics and molecular biology, 2008, 7.
Liland, K.; Høy, M.; Martens, H. & Sæbø, S. Distribution based truncation for variable selection in subspace methods for multivariate regression Chemometrics and Intelligent Laboratory Systems, 2013, 122, 103-111.
Karaman, I.; Nørskov, N.; Yde, C.; Hedemann, M.; Knudsen, K. & Kohler, A. Sparse multi-block PLSR for biomarker discovery when integrating data from LC--MS and NMR metabolomics Metabolomics, 2015, 11, 367-379.
Overviews of available methods, multiblock
, and methods organised by main structure: basic
, unsupervised
, asca
, supervised
and complex
.
data(potato)
# Truncation MB-PLS
# Loading weights inside 60% confidence intervals around the median are set to 0.
tmb <- smbpls(Sensory ~ Chemical+Compression, data=potato, ncomp = 5,
truncation = "Lenth", trunc.width = 0.6)
# Alternative XY-interface
tmb.XY <- smbpls(X=potato[c('Chemical','Compression')], Y=potato[['Sensory']], ncomp = 5,
truncation = "Lenth", trunc.width = 0.6)
identical(tmb, tmb.XY)
#> [1] FALSE
scoreplot(tmb, labels="names") # Exploiting mvr object structure from pls package
loadingweightplot(tmb, labels="names")
# Soft-Threshold / Sparse MB-PLS
# Loading weights are subtracted by 60% of maximum value.
smb <- smbpls(X=potato[c('Chemical','Compression')], Y=potato[['Sensory']],
ncomp = 5, shrink = 0.6)
print(smb)
#> Sparse Multiblock PLS (Soft-Threshold)
#>
#> Call:
#> smbpls(X = potato[c("Chemical", "Compression")], Y = potato[["Sensory"]], ncomp = 5, shrink = 0.6)
scoreplot(smb, labels="names") # Exploiting mvr object structure from pls package
loadingweightplot(smb, labels="names")
# Emphasis may be different for blocks
smb <- smbpls(X=potato[c('Chemical','Compression')], Y=potato[['Sensory']],
ncomp = 5, shrink = 0.6, blockScale = c(1, 10))