-
Notifications
You must be signed in to change notification settings - Fork 55
/
Copy pathblock.spls.R
173 lines (164 loc) · 7.46 KB
/
block.spls.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
# =============================================================================
# block.spls: perform a horizontal PLS-DA on a combination of datasets,
# input as a list in X
# this function is a particular setting of internal_mint.block,
# the formatting of the input is checked in internal_wrapper.mint.block
# =============================================================================
#' N-integration and feature selection with sparse Projection to Latent
#' Structures models (sPLS)
#'
#' Integration of multiple data sets measured on the same samples or
#' observations, with variable selection in each data set, ie. N-integration.
#' The method is partly based on Generalised Canonical Correlation Analysis.
#'
#' \code{block.spls} function fits a horizontal sPLS model with a specified
#' number of components per block). An outcome needs to be provided, either by
#' \code{Y} or by its position \code{indY} in the list of blocks \code{X}.
#' Multi (continuous)response are supported. \code{X} and \code{Y} can contain
#' missing values. Missing values are handled by being disregarded during the
#' cross product computations in the algorithm \code{block.pls} without having
#' to delete rows with missing data. Alternatively, missing data can be imputed
#' prior using the \code{nipals} function.
#'
#' The type of algorithm to use is specified with the \code{mode} argument.
#' Four PLS algorithms are available: PLS regression \code{("regression")}, PLS
#' canonical analysis \code{("canonical")}, redundancy analysis
#' \code{("invariant")} and the classical PLS algorithm \code{("classic")} (see
#' References and \code{?pls} for more details).
#'
#' Note that our method is partly based on sparse Generalised Canonical
#' Correlation Analysis and differs from the MB-PLS approaches proposed by
#' Kowalski et al., 1989, J Chemom 3(1), Westerhuis et al., 1998, J Chemom,
#' 12(5) and sparse variants Li et al., 2012, Bioinformatics 28(19); Karaman et
#' al (2014), Metabolomics, 11(2); Kawaguchi et al., 2017, Biostatistics.
#'
#' Variable selection is performed on each component for each block of
#' \code{X}, and for \code{Y} if specified, via input parameter \code{keepX}
#' and \code{keepY}.
#'
#' Note that if \code{Y} is missing and \code{indY} is provided, then variable
#' selection on \code{Y} is performed by specifying the input parameter
#' directly in \code{keepX} (no \code{keepY} is needed).
#'
#' @inheritParams block.pls
#' @param Y Matrix response for a multivariate regression framework. Data
#' should be continuous variables (see \code{?block.splsda} for supervised
#' classification and factor response).
#' @param keepX A named list of same length as X. Each entry is the number of
#' variables to select in each of the blocks of X for each component. By
#' default all variables are kept in the model.
#' @param keepY Only if Y is provided (and not \code{indY}). Each entry is the number of variables to
#' select in each of the blocks of Y for each component.
#' @template arg/verbose.call
#' @return \code{block.spls} returns an object of class \code{"block.spls"}, a
#' list that contains the following components:
#'
#' \item{X}{the centered and standardized original predictor matrix.}
#' \item{indY}{the position of the outcome Y in the output list X.}
#' \item{ncomp}{the number of components included in the model for each block.}
#' \item{mode}{the algorithm used to fit the model.} \item{keepX}{Number of
#' variables used to build each component of each block} \item{keepY}{Number of
#' variables used to build each component of Y} \item{variates}{list containing
#' the variates of each block of X.} \item{loadings}{list containing the
#' estimated loadings for the variates.} \item{names}{list containing the names
#' to be used for individuals and variables.} \item{nzv}{list containing the
#' zero- or near-zero predictors information.} \item{iter}{Number of iterations
#' of the algorithm for each component} \item{prop_expl_var}{Percentage of
#' explained variance for each component and each block after setting possible
#' missing values in the centered data to zero}
#' \item{call}{if \code{verbose.call = FALSE}, then just the function call is returned.
#' If \code{verbose.call = TRUE} then all the inputted values are accessable via
#' this component}
#' Note that the argument 'scheme' has now been hardcoded to 'horst' and 'init' to 'svd.single'.
#' @author Florian Rohart, Benoit Gautier, Kim-Anh Lê Cao, Al J Abadi
#' @seealso \code{\link{plotIndiv}}, \code{\link{plotArrow}},
#' \code{\link{plotLoadings}}, \code{\link{plotVar}}, \code{\link{predict}},
#' \code{\link{perf}}, \code{\link{selectVar}}, \code{\link{block.pls}},
#' \code{\link{block.splsda}} and http://www.mixOmics.org for more details.
#' @references Tenenhaus, M. (1998). \emph{La regression PLS: theorie et
#' pratique}. Paris: Editions Technic.
#'
#' Wold H. (1966). Estimation of principal components and related models by
#' iterative least squares. In: Krishnaiah, P. R. (editors), \emph{Multivariate
#' Analysis}. Academic Press, N.Y., 391-420.
#'
#' Tenenhaus A. and Tenenhaus M., (2011), Regularized Generalized Canonical
#' Correlation Analysis, Psychometrika, Vol. 76, Nr 2, pp 257-284.
#'
#' Tenenhaus A., Philippe C., Guillemot V, Lê Cao K.A., Grill J, Frouin V.
#' Variable selection for generalized canonical correlation analysis.
#' \emph{Biostatistics}. kxu001
#' @keywords regression multivariate
#' @example ./examples/block.spls-examples.R
#' @export
block.spls = function(X,
Y,
indY,
ncomp = 2,
keepX,
keepY,
design,
mode,
scale = TRUE,
tol = 1e-06,
max.iter = 100,
near.zero.var = FALSE,
all.outputs = TRUE,
verbose.call = FALSE)
{
# call to 'internal_wrapper.mint.block'
result = internal_wrapper.mint.block(
X = X,
Y = Y,
indY = indY,
ncomp = ncomp,
keepX = keepX,
keepY = keepY,
design = design,
scheme = "horst",
mode = mode,
scale = scale,
init = "svd.single",
tol = tol,
max.iter = max.iter,
near.zero.var = near.zero.var,
all.outputs = all.outputs,
DA = FALSE
)
# calculate weights for each dataset
weights = get.weights(result$variates, indY = result$indY)
# choose the desired output from 'result'
out = list(
call = match.call(),
X = result$A,
indY = result$indY,
ncomp = result$ncomp,
mode = result$mode,
keepX = result$keepA[-result$indY],
keepY = result$keepA[result$indY][[1]],
variates = result$variates,
loadings = result$loadings,
crit = result$crit,
AVE = result$AVE,
names = result$names,
init = result$init,
tol = result$tol,
iter = result$iter,
max.iter = result$max.iter,
nzv = result$nzv,
scale = result$scale,
design = result$design,
scheme = result$scheme,
weights = weights,
prop_expl_var = result$prop_expl_var
)
if (verbose.call) {
c <- out$call
out$call <- mget(names(formals()))
out$call <- append(c, out$call)
names(out$call)[1] <- "simple.call"
}
# give a class
class(out) = c("block.spls", "sgcca")
return(invisible(out))
}