Help for package SISIR

Type:

Package

Title:

Select Intervals Suited for Functional Regression

Version:

0.2.3

Date:

2024-08-16

Maintainer:

Nathalie Vialaneix <nathalie.vialaneix@inrae.fr>

Description:

Interval fusion and selection procedures for regression with functional inputs. Methods include a semiparametric approach based on Sliced Inverse Regression (SIR), as described in <doi:10.1007/s11222-018-9806-6> (standard ridge and sparse SIR are also included in the package) and a random forest based approach, as described in <doi:10.1002/sam.11705>.

Depends:

R (≥ 3.5.0), foreach, doParallel, graphics, stats

URL:

https://forgemia.inra.fr/sfcb/sisir

BugReports:

https://forgemia.inra.fr/sfcb/sisir/-/issues

Imports:

Matrix, expm, RSpectra, glmnet, Boruta, CORElearn, dplyr, mixOmics, purrr, ranger, tidyr, tidyselect, adjclust, magrittr, rlang, ggplot2, aricode, dendextend, reshape2, RColorBrewer

Suggests:

testthat

License:

GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]

RoxygenNote:

7.3.1

Encoding:

UTF-8

Repository:

CRAN

NeedsCompilation:

Packaged:

2024-08-16 11:51:16 UTC; nathalie

Author:

Victor Picheny [aut], Remi Servien [aut], Nathalie Vialaneix

[aut, cre]

Date/Publication:

2024-08-16 12:50:02 UTC

Methods for SFCB objects

Description

Print, plot, manipulate or compute quality for outputs of the sfcb function (SFCB object)

Usage

## S3 method for class 'SFCB'
summary(object, ...)

## S3 method for class 'SFCB'
print(x, ...)

## S3 method for class 'SFCB'
plot(
  x,
  ...,
  plot.type = c("dendrogram", "selection", "importance", "quality"),
  sel.type = c("importance", "selection"),
  threshold = "none",
  shape.imp = c("boxplot", "histogram"),
  quality.crit = "mse"
)

extract_at(object, at)

quality(object, ground_truth, threshold = NULL)

Arguments

object

a SFCB object

...

not used

x

a SFCB object

plot.type

type of the plot. Default to "dendrogram" (see Details)

sel.type

when plot.type == "selection", criterion on which to base the selection. Default to "importance"

threshold

numeric value. If not NULL, selection of variables to compute qualities is based on a threshold of importance values extract_at

shape.imp

when plot.type == "importance", type of plot to represent the importance. Default to "boxplot"

quality.crit

character vector (length 1 or 2) indicating one or two quality criteria to display. The values have to be taken in {"mse", "time", "Precision", "Recall", "ARI", "NMI"}. If "time" is chosen, it can not be associated with any other criterion

at

numeric vector. Set of the number of intervals to extract for

ground_truth

numeric vector of ground truth. Target variables to compute qualities correspond to non-zero entries of this vector

Details

The plot functions can be used in four different ways to extract information from the SFCB object:

plot.type == "dendrogram" displays the dendrogram obtained at the clustering step of the method. Depending on the cases, the dendrogram comes with additional information on clusters, variable selections and/or importance values;
plot.type == "selection" displays either the evolution of the importance for the simulation with the best (smallest) MSE for each time step in the range of the functional predictor or the evolution of the selected intervals along the whole range of the functional prediction also for the best MSE;
plot.type == "importance" displays a summary of the importance values over the whole range of the functional predictor and for the different experiments. This summary can take the form of a boxplot or of an histogram;
plot.type == "quality" displays one or two quality distribution with respect to the different experiments and different number of intervals.

Author(s)

Remi Servien, remi.servien@inrae.fr
Nathalie Vialaneix, nathalie.vialaneix@inrae.fr

References

Servien, R. and Vialaneix, N. (2023) A random forest approach for interval selection in functional regression. Preprint.

Examples

data(truffles)
out1 <- sfcb(rainfall, truffles, group.method = "adjclust", 
             summary.method = "pls", selection.method = "relief")
summary(out1)

plot(out1)
plot(out1, plot.type = "selection")
plot(out1, plot.type = "importance")

out2 <- sfcb(rainfall, truffles, group.method = "adjclust", 
             summary.method = "basics", selection.method = "none",
             range.at = c(5, 7))
out3 <- extract_at(out2, at = 6)
summary(out3)

Interval Sparse SIR

Description

SISIR performs an automatic search of relevant intervals

Usage

SISIR(
  object,
  inter_len = rep(1, nrow(object$EDR)),
  sel_prop = 0.05,
  itermax = Inf,
  minint = 2,
  parallel = TRUE,
  ncores = NULL
)

Arguments

object

an object of class ridgeRes as obtained from the function ridgeSIR

inter_len

(numeric) vector with interval lengths for the initial state. Default is to set one interval for each variable (all intervals have length 1)

sel_prop

fraction of the coefficients that will be considered as strong zeros and strong non zeros. Default to 0.05

itermax

maximum number of iterations. Default to Inf

minint

minimum number of intervals. Default to 2

parallel

whether the computation should be performed in parallel or not. Logical. Default is FALSE

ncores

number of cores to use if parallel = TRUE. If left to NULL, all available cores minus one are used

Details

Different quality criteria used to select the best models among a list of models with different interval definitions. Quality criteria are: log-likelihood (loglik), cross-validation error as provided by the function glmnet, two versions of the AIC (AIC and AIC2) and of the BIC (BIC and BIC2) in which the number of parameters is either the number of non null intervals or the number of non null parameters with respect to the original variables

Value

S3 object of class SISIR: a list consisting of

sEDR: the estimated EDR spaces (a list of p x d matrices)
alpha: the estimated shrinkage coefficients (a list of vectors)
intervals: the interval lengths (a list of vectors)
quality: a data frame with various qualities for the model. The chosen quality measures are the same than for the function sparseSIR plus the number of intervals nbint
init_sel_prop: initial fraction of the coefficients which are considered as strong zeros or strong non zeros
rSIR: same as the input object

Author(s)

Victor Picheny, victor.picheny@inrae.fr
Remi Servien, remi.servien@inrae.fr
Nathalie Vialaneix, nathalie.vialaneix@inrae.fr

References

Picheny, V., Servien, R. and Villa-Vialaneix, N. (2016) Interpretable sparse SIR for digitized functional data. Statistics and Computing, 29(2), 255–267.

Examples

set.seed(1140)
tsteps <- seq(0, 1, length = 200)
nsim <- 100
simulate_bm <- function() return(c(0, cumsum(rnorm(length(tsteps)-1, sd=1))))
x <- t(replicate(nsim, simulate_bm()))
beta <- cbind(sin(tsteps*3*pi/2), sin(tsteps*5*pi/2))
beta[((tsteps < 0.2) | (tsteps > 0.5)), 1] <- 0
beta[((tsteps < 0.6) | (tsteps > 0.75)), 2] <- 0
y <- log(abs(x %*% beta[ ,1]) + 1) + sqrt(abs(x %*% beta[ ,2]))
y <- y + rnorm(nsim, sd = 0.1)
res_ridge <- ridgeSIR(x, y, H = 10, d = 2, mu2 = 10^8)
res_fused <- SISIR(res_ridge, rep(1, ncol(x)), ncores = 2)
res_fused

Print SISIRres object

Description

Print a summary of the result of SISIRres ( SISIRres object)

Usage

## S3 method for class 'SISIRres'
summary(object, ...)

## S3 method for class 'SISIRres'
print(x, ...)

Arguments

object

a SISIRres object

...

not used

x

a SISIRres object

Author(s)

Victor Picheny, victor.picheny@inrae.fr
Remi Servien, remi.servien@inrae.fr
Nathalie Vialaneix, nathalie.vialaneix@inrae.fr

sparse SIR

Description

project performs the projection on the sparse EDR space (as obtained by the glmnet)

Usage

## S3 method for class 'sparseRes'
project(object)

project(object)

Arguments

object

an object of class sparseRes as obtained from the function sparseSIR

Details

The projection is obtained by the function predict.glmnet.

Value

a matrix of dimension n x d with the projection of the observations on the d dimensions of the sparse EDR space

Author(s)

Victor Picheny, victor.picheny@inrae.fr
Remi Servien, remi.servien@inrae.fr
Nathalie Vialaneix, nathalie.vialaneix@inrae.fr

References

Picheny, V., Servien, R. and Villa-Vialaneix, N. (2016) Interpretable sparse SIR for digitized functional data. Statistics and Computing, 29(2), 255–267.

Examples

set.seed(1140)
tsteps <- seq(0, 1, length = 200)
nsim <- 100
simulate_bm <- function() return(c(0, cumsum(rnorm(length(tsteps)-1, sd=1))))
x <- t(replicate(nsim, simulate_bm()))
beta <- cbind(sin(tsteps*3*pi/2), sin(tsteps*5*pi/2))
beta[((tsteps < 0.2) | (tsteps > 0.5)), 1] <- 0
beta[((tsteps < 0.6) | (tsteps > 0.75)), 2] <- 0
y <- log(abs(x %*% beta[ ,1]) + 1) + sqrt(abs(x %*% beta[ ,2]))
y <- y + rnorm(nsim, sd = 0.1)

res_ridge <- ridgeSIR(x, y, H = 10, d = 2)
res_sparse <- sparseSIR(res_ridge, rep(1, ncol(x)))
proj_data <- project(res_sparse)

Print ridgeRes object

Description

Print a summary of the result of ridgeSIR ( ridgeRes object)

Usage

## S3 method for class 'ridgeRes'
summary(object, ...)

## S3 method for class 'ridgeRes'
print(x, ...)

Arguments

object

a ridgeRes object

...

not used

x

a ridgeRes object

Author(s)

Victor Picheny, victor.picheny@inrae.fr
Remi Servien, remi.servien@inrae.fr
Nathalie Vialaneix, nathalie.vialaneix@inrae.fr

ridge SIR

Description

ridgeSIR performs the first step of the method (ridge regularization of SIR)

Usage

ridgeSIR(x, y, H, d, mu2 = NULL)

Arguments

x

explanatory variables (numeric matrix or data frame)

y

target variable (numeric vector)

H

number of slices (integer)

d

number of dimensions to be kept

mu2

ridge regularization parameter (numeric, positive)

Details

SI-SIR

Value

S3 object of class ridgeRes: a list consisting of

EDR

the estimated EDR space (a p x d matrix)

condC

the estimated slice projection on EDR (a d x H matrix)

eigenvalues

the eigenvalues obtained during the generalized eigendecomposition performed by SIR

parameters

a list of hyper-parameters for the method:

H: number of slices
d: dimension of the EDR space
mu2: regularization parameter for the ridge penalty

utils

useful outputs for further computations:

Sigma: covariance matrix for x
slices: slice number for all observations
invsqrtS: value of the inverse square root of the regularized covariance matrix for x

Author(s)

Victor Picheny, victor.picheny@inrae.fr
Remi Servien, remi.servien@inrae.fr
Nathalie Vialaneix, nathalie.vialaneix@inrae.fr

References

Picheny, V., Servien, R. and Villa-Vialaneix, N. (2019) Interpretable sparse SIR for digitized functional data. Statistics and Computing, 29(2), 255–267.

Examples

set.seed(1140)
tsteps <- seq(0, 1, length = 50)
simulate_bm <- function() return(c(0, cumsum(rnorm(length(tsteps)-1, sd=1))))
x <- t(replicate(50, simulate_bm()))
beta <- cbind(sin(tsteps*3*pi/2), sin(tsteps*5*pi/2)) 
y <- log(abs(x %*% beta[ ,1])) + sqrt(abs(x %*% beta[ ,2]))
y <- y + rnorm(50, sd = 0.1)
res_ridge <- ridgeSIR(x, y, H = 10, d = 2, mu2 = 10^8)
res_ridge

sfcb

Description

sfcb performs interval selection based on random forests

Usage

sfcb(
  X,
  Y,
  group.method = c("adjclust", "cclustofvar"),
  summary.method = c("pls", "basics", "cclustofvar"),
  selection.method = c("none", "boruta", "relief"),
  at = round(0.15 * ncol(X)),
  range.at = NULL,
  seed = NULL,
  repeats = 5,
  keep.time = TRUE,
  verbose = TRUE,
  parallel = FALSE
)

Arguments

X

input predictors (matrix or data.frame)

Y

target variable (vector whose length is equal to the number of rows in X)

group.method

group method. Default to "adjclust"

summary.method

summary method. Default to "pls"

selection.method

selection method. Default to "none" (no selection performed)

at

number of groups targeted for output results (integer). Not used when range.at is not NULL

range.at

(vector of integer) sequence of the numbers of groups for output results

seed

random seed (integer)

repeats

number of repeats for the final random forest computation

keep.time

keep computational times for each step of the method? (logical; default to TRUE)

verbose

print messages? (logical; default to TRUE)

parallel

not implemented yet

Value

an object of class "SFCB" with elements:

dendro

a dendrogram corresponding to the method chosen in group.method

groups

a list of length length(range.at) (or of length 1 if range.at == NULL) that contains the clusterings of input variables for the selected group numbers

summaries

a list of the same length than $groups that contains the summarized predictors according to the method chosen in summary.methods

selected

a list of the same length than $groups that contains the names of the variable selected by selection.method if it is not equal to "none"

mse

a data.frame with repeats \times length($groups) rows that contains Mean Squared Errors of the repeats random forests fitted for each number of groups

importance

a list of the same length than $groups that contains a data.frame providing variable importances for the variables in selected groups in repeats columns (one for each iteration of the random forest method). When summary.method == "basics", importance for mean and sd are provided in separated columns, in which case, the number of columns is equal to 2repeats

computational.times

a vector with 4 values corresponding to the computational times of (respectively) the group, summary, selection, and RF steps. Only if keep.time == TRUE

call

function call

Author(s)

Remi Servien, remi.servien@inrae.fr
Nathalie Vialaneix, nathalie.vialaneix@inrae.fr

References

Servien, R. and Vialaneix, N. (2024) A random forest approach for interval selection in functional regression. Statistical Analysis and Data Mining, 17(4), e11705. doi:10.1002/sam.11705

Examples

data(truffles)
out1 <- sfcb(rainfall, truffles, group.method = "adjclust", 
             summary.method = "pls", selection.method = "relief")
out2 <- sfcb(rainfall, truffles, group.method = "adjclust", 
             summary.method = "basics", selection.method = "none",
             range.at = c(5, 7))

Print sparseRes object

Description

Print a summary of the result of sparseSIR ( sparseRes object)

Usage

## S3 method for class 'sparseRes'
summary(object, ...)

## S3 method for class 'sparseRes'
print(x, ...)

Arguments

object

a sparseRes object

...

not used

x

a sparseRes object

Author(s)

Victor Picheny, victor.picheny@inrae.fr
Remi Servien, remi.servien@inrae.fr
Nathalie Vialaneix, nathalie.vialaneix@inra.fr

sparse SIR

Description

sparseSIR performs the second step of the method (shrinkage of ridge SIR results

Usage

sparseSIR(
  object,
  inter_len,
  adaptive = FALSE,
  sel_prop = 0.05,
  parallel = FALSE,
  ncores = NULL
)

Arguments

object

an object of class ridgeRes as obtained from the function ridgeSIR

inter_len

(numeric) vector with interval lengths

adaptive

should the function returns the list of strong zeros and non strong zeros (logical). Default to FALSE

sel_prop

used only when adaptive = TRUE. Fraction of the coefficients that will be considered as strong zeros and strong non zeros. Default to 0.05

parallel

whether the computation should be performed in parallel or not. Logical. Default is FALSE

ncores

number of cores to use if parallel = TRUE. If left to NULL, all available cores minus one are used

Value

S3 object of class sparseRes: a list consisting of

sEDR

the estimated EDR space (a p x d matrix)

alpha

the estimated shrinkage coefficients (a vector having a length similar to inter_len)

quality

a vector with various qualities for the model (see Details)

adapt_res

if adaptive = TRUE, a list of two vectors:

nonzeros: indexes of variables that are strong non zeros
zeros: indexes of variables that are strong zeros

parameters

a list of hyper-parameters for the method:

inter_len: lengths of intervals
sel_prop: if adaptive = TRUE, fraction of the coefficients which are considered as strong zeros or strong non zeros

rSIR

same as the input object

fit

a list for LASSO fit with:

glmnet: result of the glmnet function
lambda: value of the best Lasso parameter by CV
x: exploratory variable values as passed to fit the model

@details Different quality criteria used to select the best models among a list of models with different interval definitions. Quality criteria are: log-likelihood (loglik), cross-validation error as provided by the function glmnet, two versions of the AIC (AIC and AIC2) and of the BIC (BIC and BIC2) in which the number of parameters is either the number of non null intervals or the number of non null parameters with respect to the original variables.

Author(s)

Victor Picheny, victor.picheny@inrae.fr
Remi Servien, remi.servien@inrae.fr
Nathalie Vialaneix, nathalie.vialaneix@inrae.fr

References

Picheny, V., Servien, R., and Villa-Vialaneix, N. (2019) Interpretable sparse SIR for digitized functional data. Statistics and Computing, 29(2), 255–267.

Examples

set.seed(1140)
tsteps <- seq(0, 1, length = 200)
nsim <- 100
simulate_bm <- function() return(c(0, cumsum(rnorm(length(tsteps)-1, sd=1))))
x <- t(replicate(nsim, simulate_bm()))
beta <- cbind(sin(tsteps*3*pi/2), sin(tsteps*5*pi/2))
beta[((tsteps < 0.2) | (tsteps > 0.5)), 1] <- 0
beta[((tsteps < 0.6) | (tsteps > 0.75)), 2] <- 0
y <- log(abs(x %*% beta[ ,1]) + 1) + sqrt(abs(x %*% beta[ ,2]))
y <- y + rnorm(nsim, sd = 0.1)
res_ridge <- ridgeSIR(x, y, H = 10, d = 2, mu2 = 10^8)
res_sparse <- sparseSIR(res_ridge, rep(10, 20))

Dataset "Truffles"

Description

Yearly truffles production and corresponding monthly rainfall information of the Perigord black truffle in the Vaucluse (France) between 1924 and 1949.

Format

3 datasets are provided:

rainfall: a data frame with 15 columns (months from January Year n to March Year n+1) and 25 rows (production years from 1924/1925 to 1948/1949). Data correspond to cumulated rainfall in mm;
truffles: a vector with 25 values corresponding to the total production (in kg) of truffles in the truffle patch of T. melanosporum de Pernes-Les-Fontaines (Vaucluse, France);
beta: 0/1 vector with 15 values indicated the months during which the rainfall has the most important influence on the truffle production, as provided by experts.

Details

This dataset has been made available by courtesy of the authors of the publication [Baragatti et al., 2019]. Meteorological data have been provided by Meteo France https://meteofrance.com (Orange meteorological station) and truffle production data are courtesy of the truffle patch.

References

Baragatti M., Grollemund P.M., Montpied P., Dupouey J.L., Gravier J., Murat C., Le Tacon F. (2019) Influence of annual climatic variations, climate changes, and sociological factors on the production of the Perigord black truffle (Tuber melanosporum Vittad.) from 1903-1904 to 1988-1989 in the Vaucluse (France), Mycorrhiza, 29(2), 113-125.

Examples

data(truffles)
summary(truffles)
plot(1:15, rainfall[1, ], type = "l", xlab = "month", ylab = "rainfall (mm)")

Cross-Validation for ridge SIR

Description

tune.ridgeSIR performs a Cross Validation for ridge SIR estimation

Usage

tune.ridgeSIR(
  x,
  y,
  listH,
  list_mu2,
  list_d,
  nfolds = 10,
  parallel = TRUE,
  ncores = NULL
)

Arguments

x

explanatory variables (numeric matrix or data frame)

y

target variable (numeric vector)

listH

list of the number of slices to be tested (numeric vector)

list_mu2

list of ridge regularization parameters to be tested (numeric vector)

list_d

list of the dimensions to be tested (numeric vector)

nfolds

number of folds for the cross validation. Default is 10

parallel

whether the computation should be performed in parallel or not. Logical. Default is FALSE

ncores

number of cores to use if parallel = TRUE. If left to NULL, all available cores minus one are used

Value

a data frame with tested parameters and corresponding CV error and estimation of R(d)

Author(s)

Victor Picheny, victor.picheny@inrae.fr
Remi Servien, remi.servien@inrae.fr
Nathalie Vialaneix, nathalie.vialaneix@inrae.fr

References

Picheny, V., Servien, R. and Villa-Vialaneix, N. (2016) Interpretable sparse SIR for digitized functional data. Statistics and Computing, 29(2), 255–267.

Examples

set.seed(1115)
tsteps <- seq(0, 1, length = 200)
nsim <- 100
simulate_bm <- function() return(c(0, cumsum(rnorm(length(tsteps)-1, sd=1))))
x <- t(replicate(nsim, simulate_bm()))
beta <- cbind(sin(tsteps*3*pi/2), sin(tsteps*5*pi/2))
y <- log(abs(x %*% beta[ ,1])) + sqrt(abs(x %*% beta[ ,2]))
y <- y + rnorm(nsim, sd = 0.1)
list_mu2 <- 10^(0:10)
listH <- c(5, 10)
list_d <- 1:4
set.seed(1129)

res_tune <- tune.ridgeSIR(x, y, listH, list_mu2, list_d, nfolds = 10, 
                          parallel = TRUE, ncores = 2)

Methods for SFCB objects

Description

Usage

Arguments

Details

Author(s)

References

See Also

Examples

Interval Sparse SIR

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Print SISIRres object

Description

Usage

Arguments

Author(s)

See Also

sparse SIR

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Print ridgeRes object

Description

Usage

Arguments

Author(s)

See Also

ridge SIR

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

sfcb

Description

Usage

Arguments

Value

Author(s)

References

Examples

Print sparseRes object

Description

Usage

Arguments

Author(s)

See Also

sparse SIR

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Dataset "Truffles"

Description

Format

Details

References

Examples