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
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pls.R
\name{step_pls}
\alias{step_pls}
\title{Partial Least Squares Feature Extraction}
\usage{
step_pls(
recipe,
...,
role = "predictor",
trained = FALSE,
num_comp = 2,
predictor_prop = 1,
outcome = NULL,
options = list(scale = TRUE),
preserve = deprecated(),
res = NULL,
columns = NULL,
prefix = "PLS",
keep_original_cols = FALSE,
skip = FALSE,
id = rand_id("pls")
)
}
\arguments{
\item{recipe}{A recipe object. The step will be added to the
sequence of operations for this recipe.}
\item{...}{One or more selector functions to choose variables
for this step. See \code{\link[=selections]{selections()}} for more details.}
\item{role}{For model terms created by this step, what analysis role should
they be assigned? By default, the new columns created by this step from
the original variables will be used as \emph{predictors} in a model.}
\item{trained}{A logical to indicate if the quantities for
preprocessing have been estimated.}
\item{num_comp}{The number of components to retain as new predictors.
If \code{num_comp} is greater than the number of columns or the number of
possible components, a smaller value will be used. If \code{num_comp = 0}
is set then no transformation is done and selected variables will
stay unchanged.}
\item{predictor_prop}{The maximum number of original predictors that can have
non-zero coefficients for each PLS component (via regularization).}
\item{outcome}{When a single outcome is available, character
string or call to \code{\link[dplyr:vars]{dplyr::vars()}} can be used to specify a single outcome
variable.}
\item{options}{A list of options to \code{mixOmics::pls()}, \code{mixOmics::spls()},
\code{mixOmics::plsda()}, or \code{mixOmics::splsda()} (depending on the data and
arguments).}
\item{preserve}{Use \code{keep_original_cols} instead to specify whether the
original predictor data should be retained along with the new features.}
\item{res}{A list of results are stored here once this preprocessing step
has been trained by \code{\link[=prep]{prep()}}.}
\item{columns}{A character string of variable names that will
be populated elsewhere.}
\item{prefix}{A character string for the prefix of the resulting new
variables. See notes below.}
\item{keep_original_cols}{A logical to keep the original variables in the
output. Defaults to \code{FALSE}.}
\item{skip}{A logical. Should the step be skipped when the
recipe is baked by \code{\link[=bake]{bake()}}? While all operations are baked
when \code{\link[=prep]{prep()}} is run, some operations may not be able to be
conducted on new data (e.g. processing the outcome variable(s)).
Care should be taken when using \code{skip = TRUE} as it may affect
the computations for subsequent operations.}
\item{id}{A character string that is unique to this step to identify it.}
}
\value{
An updated version of \code{recipe} with the new step added to the
sequence of any existing operations.
}
\description{
\code{step_pls} creates a \emph{specification} of a recipe step that will
convert numeric data into one or more new dimensions.
}
\details{
PLS is a supervised version of principal component
analysis that requires the outcome data to compute
the new features.
This step requires the Bioconductor \pkg{mixOmics} package. If not installed, the
step will stop with a note about installing the package.
The argument \code{num_comp} controls the number of components that will
be retained (the original variables that are used to derive the
components are removed from the data). The new components will
have names that begin with \code{prefix} and a sequence of numbers.
The variable names are padded with zeros. For example, if \code{num_comp < 10}, their names will be \code{PLS1} - \code{PLS9}. If \code{num_comp = 101}, the
names would be \code{PLS001} - \code{PLS101}.
Sparsity can be encouraged using the \code{predictor_prop} parameter. This affects
each PLS component, and indicates the maximum proportion of predictors with
non-zero coefficients in each component. \code{step_pls()} converts this
proportion to determine the \code{keepX} parameter in \code{mixOmics::spls()} and
\code{mixOmics::splsda()}. See the references in \code{mixOmics::spls()} for details.
}
\section{Tidying}{
The \code{\link[=tidy.recipe]{tidy()}} method returns the coefficients that are
usually defined as
\deqn{W(P'W)^{-1}}
(See the Wikipedia article below)
When applied to data, these values are usually scaled by a column-specific
norm. The \code{tidy()} method applies this same norm to the coefficients shown
above. When you \code{tidy()} this step, a tibble with columns \code{terms} (the
selectors or variables selected), \code{components}, and \code{values} is returned.
}
\section{Case weights}{
The underlying operation does not allow for case weights.
}
\examples{
\dontshow{if (rlang::is_installed("modeldata")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
# requires the Bioconductor mixOmics package
data(biomass, package = "modeldata")
biom_tr <-
biomass \%>\%
dplyr::filter(dataset == "Training") \%>\%
dplyr::select(-dataset, -sample)
biom_te <-
biomass \%>\%
dplyr::filter(dataset == "Testing") \%>\%
dplyr::select(-dataset, -sample, -HHV)
dense_pls <-
recipe(HHV ~ ., data = biom_tr) \%>\%
step_pls(all_numeric_predictors(), outcome = "HHV", num_comp = 3)
sparse_pls <-
recipe(HHV ~ ., data = biom_tr) \%>\%
step_pls(all_numeric_predictors(), outcome = "HHV", num_comp = 3, predictor_prop = 4 / 5)
## -----------------------------------------------------------------------------
## PLS discriminant analysis
data(cells, package = "modeldata")
cell_tr <-
cells \%>\%
dplyr::filter(case == "Train") \%>\%
dplyr::select(-case)
cell_te <-
cells \%>\%
dplyr::filter(case == "Test") \%>\%
dplyr::select(-case, -class)
dense_plsda <-
recipe(class ~ ., data = cell_tr) \%>\%
step_pls(all_numeric_predictors(), outcome = "class", num_comp = 5)
sparse_plsda <-
recipe(class ~ ., data = cell_tr) \%>\%
step_pls(all_numeric_predictors(), outcome = "class", num_comp = 5, predictor_prop = 1 / 4)
\dontshow{\}) # examplesIf}
}
\references{
\url{https://en.wikipedia.org/wiki/Partial_least_squares_regression}
Rohart F, Gautier B, Singh A, Lê Cao K-A (2017) \emph{mixOmics: An R package for
'omics feature selection and multiple data integration}. PLoS Comput Biol
13(11): e1005752. \doi{10.1371/journal.pcbi.1005752}
}
\seealso{
Other multivariate transformation steps:
\code{\link{step_classdist}()},
\code{\link{step_depth}()},
\code{\link{step_geodist}()},
\code{\link{step_ica}()},
\code{\link{step_isomap}()},
\code{\link{step_kpca_poly}()},
\code{\link{step_kpca_rbf}()},
\code{\link{step_kpca}()},
\code{\link{step_mutate_at}()},
\code{\link{step_nnmf_sparse}()},
\code{\link{step_nnmf}()},
\code{\link{step_pca}()},
\code{\link{step_ratio}()},
\code{\link{step_spatialsign}()}
}
\concept{multivariate transformation steps}
|
