spca {mixOmics} | R Documentation |
Performs a sparse principal components analysis to perform variable selection by using singular value decomposition.
spca( X, ncomp = 2, center = TRUE, scale = TRUE, keepX = rep(ncol(X), ncomp), max.iter = 500, tol = 1e-06, logratio = "none", multilevel = NULL )
X |
a numeric matrix (or data frame) which provides the data for the principal components analysis. It can contain missing values. |
ncomp |
integer, if data is complete |
center |
a logical value indicating whether the variables should be
shifted to be zero centered. Alternately, a vector of length equal the
number of columns of |
scale |
a logical value indicating whether the variables should be
scaled to have unit variance before the analysis takes place. The default is
|
keepX |
numeric vector of length ncomp, the number of variables to keep in loading vectors. By default all variables are kept in the model. See details. |
max.iter |
integer, the maximum number of iterations in the NIPALS algorithm. |
tol |
a positive real, the tolerance used in the NIPALS algorithm. |
logratio |
one of ('none','CLR'). Specifies the log ratio transformation to deal with compositional values that may arise from specific normalisation in sequencing data. Default to 'none' |
multilevel |
sample information for multilevel decomposition for repeated measurements. |
The calculation employs singular value decomposition of the (centered and scaled) data matrix and LASSO to generate sparsity on the loading vectors.
scale= TRUE
is highly recommended as it will help obtaining
orthogonal sparse loading vectors.
keepX
is the number of variables to keep in loading vectors. The
difference between number of columns of X
and keepX
is the
degree of sparsity, which refers to the number of zeros in each loading
vector.
Note that spca
does not apply to the data matrix with missing values.
The biplot function for spca
is not available.
According to Filzmoser et al., a ILR log ratio transformation is more appropriate for PCA with compositional data. Both CLR and ILR are valid.
Logratio transform and multilevel analysis are performed sequentially as
internal pre-processing step, through logratio.transfo
and
withinVariation
respectively.
Logratio can only be applied if the data do not contain any 0 value (for count data, we thus advise the normalise raw data with a 1 offset). For ILR transformation and additional offset might be needed.
spca
returns a list with class "spca"
containing the
following components:
ncomp |
the number of components to keep in the calculation. |
varX |
the adjusted cumulative percentage of variances explained. |
keepX |
the number of variables kept in each loading vector. |
iter |
the number of iterations needed to reach convergence for each component. |
rotation |
the matrix containing the sparse loading vectors. |
x |
the matrix containing the principal components. |
Kim-Anh LĂȘ Cao, Fangzhou Yao, Leigh Coonan, Ignacio Gonzalez, Al J Abadi
Shen, H. and Huang, J. Z. (2008). Sparse principal component analysis via regularized low rank matrix approximation. Journal of Multivariate Analysis 99, 1015-1034.
pca
and http://www.mixOmics.org for more details.
data(liver.toxicity) spca.rat <- spca(liver.toxicity$gene, ncomp = 3, keepX = rep(50, 3)) spca.rat ## variable representation plotVar(spca.rat, cex = 0.5) ## Not run: plotVar(spca.rat,style="3d") ## End(Not run) ## Not run: ## samples representation plotIndiv(spca.rat, ind.names = liver.toxicity$treatment[, 3], group = as.numeric(liver.toxicity$treatment[, 3])) plotIndiv(spca.rat, cex = 0.01, col = as.numeric(liver.toxicity$treatment[, 3]),style="3d") # example with multilevel decomposition and CLR log ratio transformation # ---------------- data("diverse.16S") pca.res = pca(X = diverse.16S$data.TSS, ncomp = 5, logratio = 'CLR', multilevel = diverse.16S$sample) plot(pca.res) plotIndiv(pca.res, ind.names = FALSE, group = diverse.16S$bodysite, title = '16S diverse data', legend=TRUE) ## End(Not run)