lmSP_cv: Cross-validation for Sparse Adaptive Overlap Group Least...
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lmSP_cv R Documentation
Cross-validation for Sparse Adaptive Overlap Group Least Absolute Shrinkage and Selection Operator
Description
Sparse Adaptive overlap group-LASSO, or sparse adaptive group L_2-regularized regression, solves the following optimization problem
\textrm{min}_{\beta,\gamma} ~ \frac{1}{2}\|y-X\beta-Z\gamma\|_2^2 + \lambda\Big[(1-\alpha) \sum_{g=1}^G \|S_g T\beta\|_2+\alpha\Vert T_1\beta\Vert_1\Big]
to obtain a sparse coefficient vector \beta\in\mathbb{R}^p for the matrix of penalized predictors X and a coefficient vector \gamma\in\mathbb{R}^q
for the matrix of unpenalized predictors Z. For each group g, each row of
the matrix S_g\in\mathbb{R}^{n_g\times p} has non-zero entries only for those variables belonging
to that group. These values are provided by the arguments groups and group_weights (see below).
Each variable can belong to more than one group. The diagonal matrix T\in\mathbb{R}^{p\times p} contains the variable-specific weights. These values are
provided by the argument var_weights (see below). The diagonal matrix T_1\in\mathbb{R}^{p\times p} contains
the variable-specific L_1 weights. These values are provided by the argument var_weights_L1 (see below).
The regularization path is computed for the sparse adaptive overlap group-LASSO penalty at a grid of values for the regularization
parameter \lambda using the alternating direction method of multipliers (ADMM). See Boyd et al. (2011) and Lin et al. (2022)
for details on the ADMM method. The regularization is a combination of L_2 and
L_1 simultaneous constraints. Different specifications of the penalty argument lead to different models choice:
- LASSO
The classical Lasso regularization (Tibshirani, 1996) can be obtained by specifying \alpha = 1
and the matrix T_1 as the p \times p identity matrix. An adaptive version of this model (Zou, 2006) can be obtained if T_1 is
a p \times p diagonal matrix of adaptive weights. See also Hastie et al. (2015) for further details.
- GLASSO
The group-Lasso regularization (Yuan and Lin, 2006) can be obtained by specifying \alpha = 0,
non-overlapping groups in S_g and by setting the matrix T equal to the p \times p identity matrix.
An adaptive version of this model can be obtained if the matrix T is a p \times p diagonal matrix of adaptive weights. See also Hastie et al. (2015) for further details.
- spGLASSO
The sparse group-Lasso regularization (Simon et al., 2011) can be obtained by specifying \alpha\in(0,1),
non-overlapping groups in S_g and by setting the matrices T and T_1 equal to the p \times p identity matrix.
An adaptive version of this model can be obtained if the matrices T and T_1 are p \times p
diagonal matrices of adaptive weights.
- OVGLASSO
The overlap group-Lasso regularization (Jenatton et al., 2011) can be obtained by specifying
\alpha = 0, overlapping groups in S_g and by setting the matrix T equal to the p \times p identity matrix. An adaptive version of this model can be obtained if the matrix T is a p \times p
diagonal matrix of adaptive weights.
- spOVGLASSO
The sparse overlap group-Lasso regularization (Jenatton et al., 2011) can be obtained by specifying
\alpha\in(0,1), overlapping groups in S_g and by setting the matrices T and T_1 equal to the p \times p identity matrix.
An adaptive version of this model can be obtained if the matrices T and T_1 are p \times p diagonal matrices of adaptive weights.
Usage
lmSP_cv(
X,
Z = NULL,
y,
penalty = c("LASSO", "GLASSO", "spGLASSO", "OVGLASSO", "spOVGLASSO"),
groups,
group_weights = NULL,
var_weights = NULL,
var_weights_L1 = NULL,
cv.fold = 5,
standardize.data = TRUE,
intercept = FALSE,
overall.group = FALSE,
lambda = NULL,
alpha = NULL,
lambda.min.ratio = NULL,
nlambda = 30,
control = list()
)
Arguments
X
an (n\times p) matrix of penalized predictors.
Z
an (n\times q) full column rank matrix of predictors that are not penalized.
y
a length-n response vector.
penalty
choose one from the following options: 'LASSO', for the or adaptive-Lasso penalties, 'GLASSO',
for the group-Lasso penalty, 'spGLASSO', for the sparse group-Lasso penalty, 'OVGLASSO',
for the overlap group-Lasso penalty and 'spOVGLASSO', for the sparse overlap group-Lasso penalty.
groups
either a vector of length p of consecutive integers describing the grouping of the coefficients,
or a list with two elements: the first element is a vector of length \sum_{g=1}^G n_g containing the variables belonging to each group, where n_g is the cardinality of the g-th group,
while the second element is a vector of length G containing the group lengths (see example below).
group_weights
a vector of length G containing group-specific weights. The default is square root of the group cardinality, see Yuan and Lin (2006).
var_weights
a vector of length p containing variable-specific weights. The default is a vector of ones.
var_weights_L1
a vector of length p containing variable-specific weights for the L_1 penalty. The default is a vector of ones.
cv.fold
the number of folds - default is 5.
standardize.data
logical. Should data be standardized?
intercept
logical. If it is TRUE, a column of ones is added to the design matrix.
overall.group
logical. This setting is only available for the overlap group-LASSO and the sparse overlap group-LASSO penalties, otherwise it is set to NULL. If it is TRUE, an overall group including all penalized covariates is added.
lambda
either a regularization parameter or a vector of regularization parameters. In this latter case the routine computes the whole path. If it is NULL values for lambda are provided by the routine.
alpha
the sparse overlap group-LASSO mixing parameter, with 0\leq\alpha\leq1. This setting is only available for the sparse group-LASSO and the sparse overlap group-LASSO penalties, otherwise it is set to NULL. The LASSO and group-LASSO penalties are obtained
by specifying \alpha = 1 and \alpha = 0, respectively.
lambda.min.ratio
smallest value for lambda, as a fraction of the maximum lambda value. If n>p, the default is 0.0001, and if n<p, the default is 0.01.
nlambda
the number of lambda values - default is 30.
control
a list of control parameters for the ADMM algorithm. See ‘Details’.
Value
A named list containing
- sp.coefficients
a length-p solution vector for the parameters \beta. If n_\lambda>1 then the provided vector corresponds to the minimum cross-validated MSE.
- coefficients
a length-q solution vector for the parameters \gamma. If n_\lambda>1 then the provided vector corresponds to the minimum cross-validated MSE.
It is provided only when either the matrix Z in input is not NULL or the intercept is set to TRUE.
- sp.coef.path
an (n_\lambda\times p) matrix of estimated \beta coefficients for each lambda of the provided sequence.
- coef.path
an (n_\lambda\times q) matrix of estimated \gamma coefficients for each lambda of the provided sequence.
It is provided only when either the matrix Z in input is not NULL or the intercept is set to TRUE.
- lambda
sequence of lambda.
- lambda.min
value of lambda that attains the minimum cross-validated MSE.
- mse
cross-validated mean squared error.
- min.mse
minimum value of the cross-validated MSE for the sequence of lambda.
- convergence
logical. 1 denotes achieved convergence.
- elapsedTime
elapsed time in seconds.
- iternum
number of iterations.
When you run the algorithm, output returns not only the solution, but also the iteration history recording
following fields over iterates:
- objval
objective function value
- r_norm
norm of primal residual
- s_norm
norm of dual residual
- eps_pri
feasibility tolerance for primal feasibility condition
- eps_dual
feasibility tolerance for dual feasibility condition.
Iteration stops when both r_norm and s_norm values
become smaller than eps_pri and eps_dual, respectively.
Details
The control argument is a list that can supply any of the following components:
- adaptation
logical. If it is TRUE, ADMM with adaptation is performed. The default value is TRUE. See Boyd et al. (2011) for details.
- rho
an augmented Lagrangian parameter. The default value is 1.
- tau.ada
an adaptation parameter greater than one. Only needed if adaptation = TRUE. The default value is 2. See Boyd et al. (2011) for details.
- mu.ada
an adaptation parameter greater than one. Only needed if adaptation = TRUE. The default value is 10. See Boyd et al. (2011) for details.
- abstol
absolute tolerance stopping criterion. The default value is sqrt(sqrt(.Machine$double.eps)).
- reltol
relative tolerance stopping criterion. The default value is sqrt(.Machine$double.eps).
- maxit
maximum number of iterations. The default value is 100.
- print.out
logical. If it is TRUE, a message about the procedure is printed. The default value is TRUE.
References
\insertRefbernardi_etal.2022fdaSP
\insertRefboyd_etal.2011fdaSP
\insertRefhastie_etal.2015fdaSP
\insertRefjenatton_etal.2011fdaSP
\insertReflin_etal.2022fdaSP
\insertRefsimon_etal.2013fdaSP
\insertRefyuan_lin.2006fdaSP
\insertRefzou.2006fdaSP
Examples
### generate sample data
set.seed(2023)
n <- 50
p <- 30
X <- matrix(rnorm(n * p), n, p)
### Example 1, LASSO penalty
beta <- apply(matrix(rnorm(p, sd = 1), p, 1), 1, fdaSP::softhresh, 1.5)
y <- X %*% beta + rnorm(n, sd = sqrt(crossprod(X %*% beta)) / 20)
### set the hyper-parameters of the ADMM algorithm
maxit <- 1000
adaptation <- TRUE
rho <- 1
reltol <- 1e-5
abstol <- 1e-5
### run cross-validation
mod_cv <- lmSP_cv(X = X, y = y, penalty = "LASSO",
standardize.data = FALSE, intercept = FALSE,
cv.fold = 5, nlambda = 30,
control = list("adaptation" = adaptation,
"rho" = rho,
"maxit" = maxit, "reltol" = reltol,
"abstol" = abstol,
"print.out" = FALSE))
### graphical presentation
plot(log(mod_cv$lambda), mod_cv$mse, type = "l", col = "blue", lwd = 2, bty = "n",
xlab = latex2exp::TeX("$\\log(\\lambda)$"), ylab = "Prediction Error",
ylim = range(mod_cv$mse - mod_cv$mse.sd, mod_cv$mse + mod_cv$mse.sd),
main = "Cross-validated Prediction Error")
fdaSP::confband(xV = log(mod_cv$lambda), yVmin = mod_cv$mse - mod_cv$mse.sd,
yVmax = mod_cv$mse + mod_cv$mse.sd)
abline(v = log(mod_cv$lambda[which(mod_cv$lambda == mod_cv$lambda.min)]),
col = "red", lwd = 1.0)
### comparison with oracle error
mod <- lmSP(X = X, y = y, penalty = "LASSO",
standardize.data = FALSE,
intercept = FALSE,
nlambda = 30,
control = list("adaptation" = adaptation,
"rho" = rho,
"maxit" = maxit, "reltol" = reltol,
"abstol" = abstol,
"print.out" = FALSE))
err_mod <- apply(mod$sp.coef.path, 1, function(x) sum((x - beta)^2))
plot(log(mod$lambda), err_mod, type = "l", col = "blue", lwd = 2,
xlab = latex2exp::TeX("$\\log(\\lambda)$"),
ylab = "Estimation Error", main = "True Estimation Error", bty = "n")
abline(v = log(mod$lambda[which(err_mod == min(err_mod))]), col = "red", lwd = 1.0)
abline(v = log(mod_cv$lambda[which(mod_cv$lambda == mod_cv$lambda.min)]),
col = "red", lwd = 1.0, lty = 2)
### Example 2, sparse group-LASSO penalty
beta <- c(rep(4, 12), rep(0, p - 13), -2)
y <- X %*% beta + rnorm(n, sd = sqrt(crossprod(X %*% beta)) / 20)
### define groups of dimension 3 each
group1 <- rep(1:10, each = 3)
### set regularization parameter grid
lam <- 10^seq(1, -2, length.out = 30)
### set the alpha parameter
alpha <- 0.5
### set the hyper-parameters of the ADMM algorithm
maxit <- 1000
adaptation <- TRUE
rho <- 1
reltol <- 1e-5
abstol <- 1e-5
### run cross-validation
mod_cv <- lmSP_cv(X = X, y = y, penalty = "spGLASSO",
groups = group1, cv.fold = 5,
standardize.data = FALSE, intercept = FALSE,
lambda = lam, alpha = 0.5,
control = list("adaptation" = adaptation,
"rho" = rho,
"maxit" = maxit, "reltol" = reltol,
"abstol" = abstol,
"print.out" = FALSE))
### graphical presentation
plot(log(mod_cv$lambda), mod_cv$mse, type = "l", col = "blue", lwd = 2, bty = "n",
xlab = latex2exp::TeX("$\\log(\\lambda)$"), ylab = "Prediction Error",
ylim = range(mod_cv$mse - mod_cv$mse.sd, mod_cv$mse + mod_cv$mse.sd),
main = "Cross-validated Prediction Error")
fdaSP::confband(xV = log(mod_cv$lambda), yVmin = mod_cv$mse - mod_cv$mse.sd,
yVmax = mod_cv$mse + mod_cv$mse.sd)
abline(v = log(mod_cv$lambda[which(mod_cv$lambda == mod_cv$lambda.min)]),
col = "red", lwd = 1.0)
### comparison with oracle error
mod <- lmSP(X = X, y = y,
penalty = "spGLASSO",
groups = group1,
standardize.data = FALSE,
intercept = FALSE,
lambda = lam,
alpha = 0.5,
control = list("adaptation" = adaptation, "rho" = rho,
"maxit" = maxit, "reltol" = reltol, "abstol" = abstol,
"print.out" = FALSE))
err_mod <- apply(mod$sp.coef.path, 1, function(x) sum((x - beta)^2))
plot(log(mod$lambda), err_mod, type = "l", col = "blue", lwd = 2,
xlab = latex2exp::TeX("$\\log(\\lambda)$"),
ylab = "Estimation Error", main = "True Estimation Error", bty = "n")
abline(v = log(mod$lambda[which(err_mod == min(err_mod))]), col = "red", lwd = 1.0)
abline(v = log(mod_cv$lambda[which(mod_cv$lambda == mod_cv$lambda.min)]),
col = "red", lwd = 1.0, lty = 2)
fdaSP documentation built on Oct. 6, 2023, 1:07 a.m.
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| lmSP_cv | R Documentation |
Cross-validation for Sparse Adaptive Overlap Group Least Absolute Shrinkage and Selection Operator
Description
Sparse Adaptive overlap group-LASSO, or sparse adaptive group L_2-regularized regression, solves the following optimization problem
\textrm{min}_{\beta,\gamma} ~ \frac{1}{2}\|y-X\beta-Z\gamma\|_2^2 + \lambda\Big[(1-\alpha) \sum_{g=1}^G \|S_g T\beta\|_2+\alpha\Vert T_1\beta\Vert_1\Big]
to obtain a sparse coefficient vector \beta\in\mathbb{R}^p for the matrix of penalized predictors X and a coefficient vector \gamma\in\mathbb{R}^q
for the matrix of unpenalized predictors Z. For each group g, each row of
the matrix S_g\in\mathbb{R}^{n_g\times p} has non-zero entries only for those variables belonging
to that group. These values are provided by the arguments groups and group_weights (see below).
Each variable can belong to more than one group. The diagonal matrix T\in\mathbb{R}^{p\times p} contains the variable-specific weights. These values are
provided by the argument var_weights (see below). The diagonal matrix T_1\in\mathbb{R}^{p\times p} contains
the variable-specific L_1 weights. These values are provided by the argument var_weights_L1 (see below).
The regularization path is computed for the sparse adaptive overlap group-LASSO penalty at a grid of values for the regularization
parameter \lambda using the alternating direction method of multipliers (ADMM). See Boyd et al. (2011) and Lin et al. (2022)
for details on the ADMM method. The regularization is a combination of L_2 and
L_1 simultaneous constraints. Different specifications of the penalty argument lead to different models choice:
- LASSO
The classical Lasso regularization (Tibshirani, 1996) can be obtained by specifying
\alpha = 1and the matrixT_1as thep \times pidentity matrix. An adaptive version of this model (Zou, 2006) can be obtained ifT_1is ap \times pdiagonal matrix of adaptive weights. See also Hastie et al. (2015) for further details.- GLASSO
The group-Lasso regularization (Yuan and Lin, 2006) can be obtained by specifying
\alpha = 0, non-overlapping groups inS_gand by setting the matrixTequal to thep \times pidentity matrix. An adaptive version of this model can be obtained if the matrixTis ap \times pdiagonal matrix of adaptive weights. See also Hastie et al. (2015) for further details.- spGLASSO
The sparse group-Lasso regularization (Simon et al., 2011) can be obtained by specifying
\alpha\in(0,1), non-overlapping groups inS_gand by setting the matricesTandT_1equal to thep \times pidentity matrix. An adaptive version of this model can be obtained if the matricesTandT_1arep \times pdiagonal matrices of adaptive weights.- OVGLASSO
The overlap group-Lasso regularization (Jenatton et al., 2011) can be obtained by specifying
\alpha = 0, overlapping groups inS_gand by setting the matrixTequal to thep \times pidentity matrix. An adaptive version of this model can be obtained if the matrixTis ap \times pdiagonal matrix of adaptive weights.- spOVGLASSO
The sparse overlap group-Lasso regularization (Jenatton et al., 2011) can be obtained by specifying
\alpha\in(0,1), overlapping groups inS_gand by setting the matricesTandT_1equal to thep \times pidentity matrix. An adaptive version of this model can be obtained if the matricesTandT_1arep \times pdiagonal matrices of adaptive weights.
Usage
lmSP_cv(
X,
Z = NULL,
y,
penalty = c("LASSO", "GLASSO", "spGLASSO", "OVGLASSO", "spOVGLASSO"),
groups,
group_weights = NULL,
var_weights = NULL,
var_weights_L1 = NULL,
cv.fold = 5,
standardize.data = TRUE,
intercept = FALSE,
overall.group = FALSE,
lambda = NULL,
alpha = NULL,
lambda.min.ratio = NULL,
nlambda = 30,
control = list()
)
Arguments
X |
an |
Z |
an |
y |
a length- |
penalty |
choose one from the following options: 'LASSO', for the or adaptive-Lasso penalties, 'GLASSO', for the group-Lasso penalty, 'spGLASSO', for the sparse group-Lasso penalty, 'OVGLASSO', for the overlap group-Lasso penalty and 'spOVGLASSO', for the sparse overlap group-Lasso penalty. |
groups |
either a vector of length |
group_weights |
a vector of length |
var_weights |
a vector of length |
var_weights_L1 |
a vector of length |
cv.fold |
the number of folds - default is 5. |
standardize.data |
logical. Should data be standardized? |
intercept |
logical. If it is TRUE, a column of ones is added to the design matrix. |
overall.group |
logical. This setting is only available for the overlap group-LASSO and the sparse overlap group-LASSO penalties, otherwise it is set to NULL. If it is TRUE, an overall group including all penalized covariates is added. |
lambda |
either a regularization parameter or a vector of regularization parameters. In this latter case the routine computes the whole path. If it is NULL values for lambda are provided by the routine. |
alpha |
the sparse overlap group-LASSO mixing parameter, with |
lambda.min.ratio |
smallest value for lambda, as a fraction of the maximum lambda value. If |
nlambda |
the number of lambda values - default is 30. |
control |
a list of control parameters for the ADMM algorithm. See ‘Details’. |
Value
A named list containing
- sp.coefficients
a length-
psolution vector for the parameters\beta. Ifn_\lambda>1then the provided vector corresponds to the minimum cross-validated MSE.- coefficients
a length-
qsolution vector for the parameters\gamma. Ifn_\lambda>1then the provided vector corresponds to the minimum cross-validated MSE. It is provided only when either the matrixZin input is not NULL or the intercept is set to TRUE.- sp.coef.path
an
(n_\lambda\times p)matrix of estimated\betacoefficients for each lambda of the provided sequence.- coef.path
an
(n_\lambda\times q)matrix of estimated\gammacoefficients for each lambda of the provided sequence. It is provided only when either the matrixZin input is not NULL or the intercept is set to TRUE.- lambda
sequence of lambda.
- lambda.min
value of lambda that attains the minimum cross-validated MSE.
- mse
cross-validated mean squared error.
- min.mse
minimum value of the cross-validated MSE for the sequence of lambda.
- convergence
logical. 1 denotes achieved convergence.
- elapsedTime
elapsed time in seconds.
- iternum
number of iterations.
When you run the algorithm, output returns not only the solution, but also the iteration history recording following fields over iterates:
- objval
objective function value
- r_norm
norm of primal residual
- s_norm
norm of dual residual
- eps_pri
feasibility tolerance for primal feasibility condition
- eps_dual
feasibility tolerance for dual feasibility condition.
Iteration stops when both r_norm and s_norm values
become smaller than eps_pri and eps_dual, respectively.
Details
The control argument is a list that can supply any of the following components:
- adaptation
logical. If it is TRUE, ADMM with adaptation is performed. The default value is TRUE. See Boyd et al. (2011) for details.
- rho
an augmented Lagrangian parameter. The default value is 1.
- tau.ada
an adaptation parameter greater than one. Only needed if adaptation = TRUE. The default value is 2. See Boyd et al. (2011) for details.
- mu.ada
an adaptation parameter greater than one. Only needed if adaptation = TRUE. The default value is 10. See Boyd et al. (2011) for details.
- abstol
absolute tolerance stopping criterion. The default value is sqrt(sqrt(.Machine$double.eps)).
- reltol
relative tolerance stopping criterion. The default value is sqrt(.Machine$double.eps).
- maxit
maximum number of iterations. The default value is 100.
- print.out
logical. If it is TRUE, a message about the procedure is printed. The default value is TRUE.
References
\insertRefbernardi_etal.2022fdaSP
\insertRefboyd_etal.2011fdaSP
\insertRefhastie_etal.2015fdaSP
\insertRefjenatton_etal.2011fdaSP
\insertReflin_etal.2022fdaSP
\insertRefsimon_etal.2013fdaSP
\insertRefyuan_lin.2006fdaSP
\insertRefzou.2006fdaSP
Examples
### generate sample data
set.seed(2023)
n <- 50
p <- 30
X <- matrix(rnorm(n * p), n, p)
### Example 1, LASSO penalty
beta <- apply(matrix(rnorm(p, sd = 1), p, 1), 1, fdaSP::softhresh, 1.5)
y <- X %*% beta + rnorm(n, sd = sqrt(crossprod(X %*% beta)) / 20)
### set the hyper-parameters of the ADMM algorithm
maxit <- 1000
adaptation <- TRUE
rho <- 1
reltol <- 1e-5
abstol <- 1e-5
### run cross-validation
mod_cv <- lmSP_cv(X = X, y = y, penalty = "LASSO",
standardize.data = FALSE, intercept = FALSE,
cv.fold = 5, nlambda = 30,
control = list("adaptation" = adaptation,
"rho" = rho,
"maxit" = maxit, "reltol" = reltol,
"abstol" = abstol,
"print.out" = FALSE))
### graphical presentation
plot(log(mod_cv$lambda), mod_cv$mse, type = "l", col = "blue", lwd = 2, bty = "n",
xlab = latex2exp::TeX("$\\log(\\lambda)$"), ylab = "Prediction Error",
ylim = range(mod_cv$mse - mod_cv$mse.sd, mod_cv$mse + mod_cv$mse.sd),
main = "Cross-validated Prediction Error")
fdaSP::confband(xV = log(mod_cv$lambda), yVmin = mod_cv$mse - mod_cv$mse.sd,
yVmax = mod_cv$mse + mod_cv$mse.sd)
abline(v = log(mod_cv$lambda[which(mod_cv$lambda == mod_cv$lambda.min)]),
col = "red", lwd = 1.0)
### comparison with oracle error
mod <- lmSP(X = X, y = y, penalty = "LASSO",
standardize.data = FALSE,
intercept = FALSE,
nlambda = 30,
control = list("adaptation" = adaptation,
"rho" = rho,
"maxit" = maxit, "reltol" = reltol,
"abstol" = abstol,
"print.out" = FALSE))
err_mod <- apply(mod$sp.coef.path, 1, function(x) sum((x - beta)^2))
plot(log(mod$lambda), err_mod, type = "l", col = "blue", lwd = 2,
xlab = latex2exp::TeX("$\\log(\\lambda)$"),
ylab = "Estimation Error", main = "True Estimation Error", bty = "n")
abline(v = log(mod$lambda[which(err_mod == min(err_mod))]), col = "red", lwd = 1.0)
abline(v = log(mod_cv$lambda[which(mod_cv$lambda == mod_cv$lambda.min)]),
col = "red", lwd = 1.0, lty = 2)
### Example 2, sparse group-LASSO penalty
beta <- c(rep(4, 12), rep(0, p - 13), -2)
y <- X %*% beta + rnorm(n, sd = sqrt(crossprod(X %*% beta)) / 20)
### define groups of dimension 3 each
group1 <- rep(1:10, each = 3)
### set regularization parameter grid
lam <- 10^seq(1, -2, length.out = 30)
### set the alpha parameter
alpha <- 0.5
### set the hyper-parameters of the ADMM algorithm
maxit <- 1000
adaptation <- TRUE
rho <- 1
reltol <- 1e-5
abstol <- 1e-5
### run cross-validation
mod_cv <- lmSP_cv(X = X, y = y, penalty = "spGLASSO",
groups = group1, cv.fold = 5,
standardize.data = FALSE, intercept = FALSE,
lambda = lam, alpha = 0.5,
control = list("adaptation" = adaptation,
"rho" = rho,
"maxit" = maxit, "reltol" = reltol,
"abstol" = abstol,
"print.out" = FALSE))
### graphical presentation
plot(log(mod_cv$lambda), mod_cv$mse, type = "l", col = "blue", lwd = 2, bty = "n",
xlab = latex2exp::TeX("$\\log(\\lambda)$"), ylab = "Prediction Error",
ylim = range(mod_cv$mse - mod_cv$mse.sd, mod_cv$mse + mod_cv$mse.sd),
main = "Cross-validated Prediction Error")
fdaSP::confband(xV = log(mod_cv$lambda), yVmin = mod_cv$mse - mod_cv$mse.sd,
yVmax = mod_cv$mse + mod_cv$mse.sd)
abline(v = log(mod_cv$lambda[which(mod_cv$lambda == mod_cv$lambda.min)]),
col = "red", lwd = 1.0)
### comparison with oracle error
mod <- lmSP(X = X, y = y,
penalty = "spGLASSO",
groups = group1,
standardize.data = FALSE,
intercept = FALSE,
lambda = lam,
alpha = 0.5,
control = list("adaptation" = adaptation, "rho" = rho,
"maxit" = maxit, "reltol" = reltol, "abstol" = abstol,
"print.out" = FALSE))
err_mod <- apply(mod$sp.coef.path, 1, function(x) sum((x - beta)^2))
plot(log(mod$lambda), err_mod, type = "l", col = "blue", lwd = 2,
xlab = latex2exp::TeX("$\\log(\\lambda)$"),
ylab = "Estimation Error", main = "True Estimation Error", bty = "n")
abline(v = log(mod$lambda[which(err_mod == min(err_mod))]), col = "red", lwd = 1.0)
abline(v = log(mod_cv$lambda[which(mod_cv$lambda == mod_cv$lambda.min)]),
col = "red", lwd = 1.0, lty = 2)
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