Package 'adelie' reference manual

Title:	Group Lasso and Elastic Net Solver for Generalized Linear Models
Description:	Extremely efficient procedures for fitting the entire group lasso and group elastic net regularization path for GLMs, multinomial, the Cox model and multi-task Gaussian models. Similar to the R package 'glmnet' in scope of models, and in computational speed. This package provides R bindings to the C++ code underlying the corresponding Python package 'adelie'. These bindings offer a general purpose group elastic net solver, a wide range of matrix classes that can exploit special structure to allow large-scale inputs, and an assortment of generalized linear model classes for fitting various types of data. The package includes The package is an implementation of Yang, J. and Hastie, T. (2024) <doi:10.48550/arXiv.2405.08631>.
Authors:	James Yang [aut, cph], Trevor Hastie [aut, cph, cre], Balasubramanian Narasimhan [aut]
Maintainer:	Trevor Hastie <[email protected]>
License:	MIT + file LICENSE
Version:	1.0.3
Built:	2024-10-10 05:33:01 UTC
Source:	https://github.com/jamesyang007/adelie-r

Cross-validation for grpnet

Description

Does k-fold cross-validation for grpnet

Usage

cv.grpnet(
  X,
  glm,
  n_folds = 10,
  foldid = NULL,
  min_ratio = 0.01,
  lmda_path_size = 100,
  offsets = NULL,
  progress_bar = FALSE,
  n_threads = 1,
  ...
)
cv.grpnet(
  X,
  glm,
  n_folds = 10,
  foldid = NULL,
  min_ratio = 0.01,
  lmda_path_size = 100,
  offsets = NULL,
  progress_bar = FALSE,
  n_threads = 1,
  ...
)

Arguments

`X`	Feature matrix. Either a regualr R matrix, or else an `adelie` custom matrix class, or a concatination of such.
`glm`	GLM family/response object. This is an expression that represents the family, the reponse and other arguments such as weights, if present. The choices are `glm.gaussian()`, `glm.binomial()`, `glm.poisson()`, `glm.multinomial()`, `glm.cox()`, `glm.multinomial()`, and `glm.multigaussian()`. This is a required argument, and there is no default. In the simple example below, we use `glm.gaussian(y)`.
`n_folds`	(default 10). Although `n_folds` can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is `n_folds=3`.
`foldid`	An optional vector of values between 1 and `n_folds` identifying what fold each observation is in. If supplied, `n_folds` can be missing.
`min_ratio`	Ratio between smallest and largest value of lambda. Default is 1e-2.
`lmda_path_size`	Number of values for `lambda`, if generated automatically. Default is 100.
`offsets`	Offsets, default is `NULL`. If present, this is a fixed vector or matrix corresponding to the shape of the natural parameter, and is added to the fit.
`progress_bar`	Progress bar. Default is `FALSE`.
`n_threads`	Number of threads, default `1`.
`...`	Other arguments that can be passed to `grpnet`

Details

The function runs grpnet n_folds+1 times; the first to get the lambda sequence, and then the remainder to compute the fit with each of the folds omitted. The out-of-fold deviance is accumulated, and the average deviance and standard deviation over the folds is computed. Note that cv.grpnet does NOT search for values for alpha. A specific value should be supplied, else alpha=1 is assumed by default. If users would like to cross-validate alpha as well, they should call cv.grpnet with a pre-computed vector foldid, and then use this same foldid vector in separate calls to cv.grpnet with different values of alpha. Note also that the results of cv.grpnet are random, since the folds are selected at random. Users can reduce this randomness by running cv.grpnet many times, and averaging the error curves.

Value

an object of class "cv.grpnet" is returned, which is a list with the ingredients of the cross-validation fit.

`lambda`	the values of `lambda` used in the fits.
`cvm`	The mean cross-validated deviance - a vector of length `length(lambda)`.
`cvsd`	estimate of standard error of `cvm`.
`cvup`	upper curve = `cvm+cvsd`.
`cvlo`	lower curve = `cvm-cvsd`.
`nzero`	number of non-zero coefficients at each `lambda`.
`name`	a text string indicating type of measure (for plotting purposes). Currently this is `"deviance"`
`grpnet.fit`	a fitted grpnet object for the full data.
`lambda.min`	value of `lambda` that gives minimum `cvm`.
`lambda.1se`	largest value of `lambda` such that mean deviance is within 1 standard error of the minimum.
`index`	a one column matrix with the indices of `lambda.min` and `lambda.1se` in the sequence of coefficients, fits etc.

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [email protected]

References

Yang, James and Hastie, Trevor. (2024) A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent. arXiv doi:10.48550/arXiv.2405.08631.
Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent (2010), Journal of Statistical Software, Vol. 33(1), 1-22, doi:10.18637/jss.v033.i01.
Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5), 1-13, doi:10.18637/jss.v039.i05.
Tibshirani,Robert, Bien, J., Friedman, J., Hastie, T.,Simon, N.,Taylor, J. and Tibshirani, Ryan. (2012) Strong Rules for Discarding Predictors in Lasso-type Problems, JRSSB, Vol. 74(2), 245-266, https://arxiv.org/abs/1011.2234.

Examples

set.seed(0)
n <- 100
p <- 200
X <- matrix(rnorm(n * p), n, p)
y <- X[,1] * rnorm(1) + rnorm(n)
fit <- grpnet(X, glm.gaussian(y))
print(fit)

set.seed(0)
n <- 100
p <- 200
X <- matrix(rnorm(n * p), n, p)
y <- X[,1] * rnorm(1) + rnorm(n)
fit <- grpnet(X, glm.gaussian(y))
print(fit)

Solves group elastic net via covariance method.

Description

Solves group elastic net via covariance method.

Usage

gaussian_cov(
  A,
  v,
  constraints = NULL,
  groups = NULL,
  alpha = 1,
  penalty = NULL,
  lmda_path = NULL,
  max_iters = as.integer(1e+05),
  tol = 1e-07,
  rdev_tol = 0.001,
  newton_tol = 1e-12,
  newton_max_iters = 1000,
  n_threads = 1,
  early_exit = TRUE,
  screen_rule = "pivot",
  min_ratio = 0.01,
  lmda_path_size = 100,
  max_screen_size = NULL,
  max_active_size = NULL,
  pivot_subset_ratio = 0.1,
  pivot_subset_min = 1,
  pivot_slack_ratio = 1.25,
  check_state = FALSE,
  progress_bar = TRUE,
  warm_start = NULL
)
gaussian_cov(
  A,
  v,
  constraints = NULL,
  groups = NULL,
  alpha = 1,
  penalty = NULL,
  lmda_path = NULL,
  max_iters = as.integer(1e+05),
  tol = 1e-07,
  rdev_tol = 0.001,
  newton_tol = 1e-12,
  newton_max_iters = 1000,
  n_threads = 1,
  early_exit = TRUE,
  screen_rule = "pivot",
  min_ratio = 0.01,
  lmda_path_size = 100,
  max_screen_size = NULL,
  max_active_size = NULL,
  pivot_subset_ratio = 0.1,
  pivot_subset_min = 1,
  pivot_slack_ratio = 1.25,
  check_state = FALSE,
  progress_bar = TRUE,
  warm_start = NULL
)

Arguments

`A`	Positive semi-definite matrix.
`v`	Linear term.
`constraints`	Constraints.
`groups`	Groups.
`alpha`	Elastic net parameter.
`penalty`	Penalty factor.
`lmda_path`	The regularization path.
`max_iters`	Maximum number of coordinate descents.
`tol`	Coordinate descent convergence tolerance.
`rdev_tol`	Relative percent deviance explained tolerance.
`newton_tol`	Convergence tolerance for the BCD update.
`newton_max_iters`	Maximum number of iterations for the BCD update.
`n_threads`	Number of threads.
`early_exit`	`TRUE` if the function should exit early.
`screen_rule`	Screen rule (currently the only value is the default `"pivot"`.
`min_ratio`	Ratio between largest and smallest regularization parameter, default is `0.01`.
`lmda_path_size`	Number of regularization steps in the path, default is `100`.
`max_screen_size`	Maximum number of screen groups, default is `NULL` for no maximum.
`max_active_size`	Maximum number of active groups, default is `NULL` for no maximum.
`pivot_subset_ratio`	Subset ratio of pivot rule, default is `0.1`.
`pivot_subset_min`	Minimum subset of pivot rule, default is `1`.
`pivot_slack_ratio`	Slack ratio of pivot rule, default is `1.25`.
`check_state`	Check state, default is `FALSE`.
`progress_bar`	Progress bar, default is `TRUE`.
`warm_start`	Warm start, default is `NULL` (no warm start).

Value

State of the solver.

Examples

set.seed(0)
n <- 100
p <- 200
X <- matrix(rnorm(n * p), n, p)
y <- X[,1] * rnorm(1) + rnorm(n)
A <- t(X) %*% X / n
v <- t(X) %*% y / n
state <- gaussian_cov(A, v)

set.seed(0)
n <- 100
p <- 200
X <- matrix(rnorm(n * p), n, p)
y <- X[,1] * rnorm(1) + rnorm(n)
A <- t(X) %*% X / n
v <- t(X) %*% y / n
state <- gaussian_cov(A, v)

Creates a Binomial GLM family object.

Description

A GLM family object specifies the type of model fit, provides the appropriate response object and makes sure it is represented in the right form for the model family, and allows for optional parameters such as a weight vector.

Usage

glm.binomial(y, weights = NULL, link = "logit")
glm.binomial(y, weights = NULL, link = "logit")

Arguments

`y`	Binary response vector, with values 0 or 1, or a logical vector. Alternatively, if data are represented by a two-column matrix of proportions (with row-sums = 1), then one can provide one of the columns as the response. This is useful for grouped binomial data, where each observation represents the result of `m[i]` successes out of `n[i]` trials. Then the response is provided as `y[i] = m[i]/n[i]` and the corresponding element of the weight vector as `w[i]=n[i]`. Alternatively can use `glm.multinomial()` instead.
`weights`	Observation weight vector, with default `NULL`.
`link`	The link function type, with choice `"logit"` (default) or `"probit"`).

Value

Binomial GLM object.

Author(s)

Trevor Hastie and James Yang
Maintainer: Trevor Hastie [email protected]

Examples

n <- 100
y <- rbinom(n, 1, 0.5)
obj <- glm.binomial(y)
n <- 100
y <- rbinom(n, 1, 0.5)
obj <- glm.binomial(y)

Creates a Cox GLM family object.

Description

Usage

glm.cox(
  stop,
  status,
  start = -Inf,
  weights = NULL,
  tie_method = c("efron", "breslow")
)
glm.cox(
  stop,
  status,
  start = -Inf,
  weights = NULL,
  tie_method = c("efron", "breslow")
)

Arguments

`stop`	Stop time vector.
`status`	Binary status vector of same length as `stop`, with 1 a "death", and 0 censored.
`start`	Start time vector. Default is a vector of `-Inf` of same length as `stop`.
`weights`	Observation weights, with default `NULL`.
`tie_method`	The tie-breaking method - one of `"efron"` (default) or `"breslow"`.

Value

Cox GLM object.

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [email protected]

Examples

n <- 100
start <- sample.int(20, size=n, replace=TRUE)
stop <- start + 1 + sample.int(5, size=n, replace=TRUE)
status <- rbinom(n, 1, 0.5)
obj <- glm.cox(start, stop, status)
n <- 100
start <- sample.int(20, size=n, replace=TRUE)
stop <- start + 1 + sample.int(5, size=n, replace=TRUE)
status <- rbinom(n, 1, 0.5)
obj <- glm.cox(start, stop, status)

Creates a Gaussian GLM family object.

Description

Usage

glm.gaussian(y, weights = NULL, opt = TRUE)
glm.gaussian(y, weights = NULL, opt = TRUE)

Arguments

`y`	Response vector.
`weights`	Observation weight vector, with default `NULL`.
`opt`	If `TRUE` (default), an optimized routine is run.

Value

Gaussian GLM

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [email protected]

Examples

n <- 100
y <- rnorm(n)
obj <- glm.gaussian(y)
n <- 100
y <- rnorm(n)
obj <- glm.gaussian(y)

Creates a MultiGaussian GLM family object.

Description

Usage

glm.multigaussian(y, weights = NULL, opt = TRUE)
glm.multigaussian(y, weights = NULL, opt = TRUE)

Arguments

`y`	Response matrix, with two or more columns.
`weights`	Observation weight vector, with default `NULL`.
`opt`	If `TRUE` (default), an optimized routine is run.

Value

MultiGaussian GLM object.

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [email protected]

Examples

n <- 100
K <- 5
y <- matrix(rnorm(n*K), n, K)
obj <- glm.multigaussian(y)
n <- 100
K <- 5
y <- matrix(rnorm(n*K), n, K)
obj <- glm.multigaussian(y)

Creates a Multinomial GLM family object.

Description

Usage

glm.multinomial(y, weights = NULL)
glm.multinomial(y, weights = NULL)

Arguments

`y`	Response matrix with `K>1` columns, and row sums equal to 1. This can either be a "one-hot" encoded version of a K-category factor variable, or else a matrix of proportions. This is useful for grouped multinomial data, where column `y[i, k]` represents the proportion of outcomes in category k in `n[i]` trials. Then the corresponding element of the weight vector is `w[i]=n[i]`.
`weights`	Observation weights.

Value

Multinomial GLM object.

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [email protected]

Examples

n <- 100
K <- 5
y <- t(rmultinom(n, 1, rep(1/K, K)))
obj <- glm.multinomial(y)
n <- 100
K <- 5
y <- t(rmultinom(n, 1, rep(1/K, K)))
obj <- glm.multinomial(y)

Creates a Poisson GLM family object.

Description

Usage

glm.poisson(y, weights = NULL)
glm.poisson(y, weights = NULL)

Arguments

`y`	Response vector of non-negative counts.
`weights`	Observation weight vector, with default `NULL`.

Value

Poisson GLM object.

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [email protected]

Examples

n <- 100
y <- rpois(n, 1)
obj <- glm.poisson(y)
n <- 100
y <- rpois(n, 1)
obj <- glm.poisson(y)

fit a GLM with group lasso or group elastic-net regularization

Description

Computes a group elastic-net regularization path for a variety of GLM and other families, including the Cox model. This function extends the abilities of the glmnet package to allow for grouped regularization. The code is very efficient (core routines are written in C++), and allows for specialized matrix classes.

Usage

grpnet(
  X,
  glm,
  constraints = NULL,
  groups = NULL,
  alpha = 1,
  penalty = NULL,
  offsets = NULL,
  lambda = NULL,
  standardize = TRUE,
  irls_max_iters = as.integer(10000),
  irls_tol = 1e-07,
  max_iters = as.integer(1e+05),
  tol = 1e-07,
  adev_tol = 0.9,
  ddev_tol = 0,
  newton_tol = 1e-12,
  newton_max_iters = 1000,
  n_threads = 1,
  early_exit = TRUE,
  intercept = TRUE,
  screen_rule = c("pivot", "strong"),
  min_ratio = 0.01,
  lmda_path_size = 100,
  max_screen_size = NULL,
  max_active_size = NULL,
  pivot_subset_ratio = 0.1,
  pivot_subset_min = 1,
  pivot_slack_ratio = 1.25,
  check_state = FALSE,
  progress_bar = FALSE,
  warm_start = NULL
)
grpnet(
  X,
  glm,
  constraints = NULL,
  groups = NULL,
  alpha = 1,
  penalty = NULL,
  offsets = NULL,
  lambda = NULL,
  standardize = TRUE,
  irls_max_iters = as.integer(10000),
  irls_tol = 1e-07,
  max_iters = as.integer(1e+05),
  tol = 1e-07,
  adev_tol = 0.9,
  ddev_tol = 0,
  newton_tol = 1e-12,
  newton_max_iters = 1000,
  n_threads = 1,
  early_exit = TRUE,
  intercept = TRUE,
  screen_rule = c("pivot", "strong"),
  min_ratio = 0.01,
  lmda_path_size = 100,
  max_screen_size = NULL,
  max_active_size = NULL,
  pivot_subset_ratio = 0.1,
  pivot_subset_min = 1,
  pivot_slack_ratio = 1.25,
  check_state = FALSE,
  progress_bar = FALSE,
  warm_start = NULL
)

Arguments

`X`	Feature matrix. Either a regualr R matrix, or else an `adelie` custom matrix class, or a concatination of such.
`glm`	GLM family/response object. This is an expression that represents the family, the reponse and other arguments such as weights, if present. The choices are `glm.gaussian()`, `glm.binomial()`, `glm.poisson()`, `glm.multinomial()`, `glm.cox()`, `glm.multinomial()`, and `glm.multigaussian()`. This is a required argument, and there is no default. In the simple example below, we use `glm.gaussian(y)`.
`constraints`	Constraints on the parameters. Currently these are ignored.
`groups`	This is an ordered vector of integers that represents the groupings, with each entry indicating where a group begins. The entries refer to column numbers in the feature matrix. If there are `p` features, the default is `1:p` (no groups). (Note that in the output of `grpnet` this vector might be shifted to start from 0, since internally `adelie` uses zero-based indexing.)
`alpha`	The elasticnet mixing parameter, with $0\le\alpha\le 1$ . The penalty is defined as $(1-\alpha)/2\sum_j\|\|\beta_j\|\|_2^2+\alpha\sum_j\|\|\beta_j\|\|_2,$ where thte sum is over groups. `alpha=1` is pure group lasso penalty, and `alpha=0` the pure ridge penalty.
`penalty`	Separate penalty factors can be applied to each group of coefficients. This is a number that multiplies `lambda` to allow differential shrinkage for groups. Can be 0 for some groups, which implies no shrinkage, and that group is always included in the model. Default is square-root of group sizes for each group.
`offsets`	Offsets, default is `NULL`. If present, this is a fixed vector or matrix corresponding to the shape of the natural parameter, and is added to the fit.
`lambda`	A user supplied `lambda` sequence. Typical usage is to have the program compute its own `lambda` sequence based on `lmda_path_size` and `min_ratio`.
`standardize`	If `TRUE` (the default), the columns of `X` are standardized before the fit is computed. This is good practice if the features are a mixed bag, because it has an impact on the penalty. The regularization path is computed using the standardized features, and the standardization information is saved on the object for making future predictions.
`irls_max_iters`	Maximum number of IRLS iterations, default is `1e4`.
`irls_tol`	IRLS convergence tolerance, default is `1e-7`.
`max_iters`	Maximum total number of coordinate descent iterations, default is `1e5`.
`tol`	Coordinate descent convergence tolerance, default `1e-7`.
`adev_tol`	Fraction deviance explained tolerance, default `0.9`. This can be seen as a limit on overfitting the training data.
`ddev_tol`	Difference in fraction deviance explained tolerance, default `0`. If a step in the path changes the deviance by this amount or less, the algorithm truncates the path.
`newton_tol`	Convergence tolerance for the BCD update, default `1e-12`. This parameter controls the iterations in each block-coordinate step to establish the block solution.
`newton_max_iters`	Maximum number of iterations for the BCD update, default `1000`.
`n_threads`	Number of threads, default `1`.
`early_exit`	`TRUE` if the function should be allowed to exit early.
`intercept`	Default `TRUE` to include an unpenalized intercept.
`screen_rule`	Screen rule, with default `"pivot"`. Other option is `"strong"`. (an empirical improvement over `"strong"`, the other option.)
`min_ratio`	Ratio between smallest and largest value of lambda. Default is 1e-2.
`lmda_path_size`	Number of values for `lambda`, if generated automatically. Default is 100.
`max_screen_size`	Maximum number of screen groups. Default is `NULL`.
`max_active_size`	Maximum number of active groups. Default is `NULL`.
`pivot_subset_ratio`	Subset ratio of pivot rule. Default is `0.1`. Users not expected to fiddle with this.
`pivot_subset_min`	Minimum subset of pivot rule. Defaults is `1`. Users not expected to fiddle with this.
`pivot_slack_ratio`	Slack ratio of pivot rule, default is `1.25`. Users not expected to fiddle with this. See reference for details.
`check_state`	Check state. Internal parameter, with default `FALSE`.
`progress_bar`	Progress bar. Default is `FALSE`.
`warm_start`	Warm start (default is `NULL`). Internal parameter.

Value

A list of class "grpnet". This has a main component called state which represents the fitted path, and a few extra useful components such as the call, the family name, and group_sizes. Users typically use methods like predict(), print(), plot() etc to examine the object.

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [email protected]

References

Examples

set.seed(0)
n <- 100
p <- 200
X <- matrix(rnorm(n * p), n, p)
y <- X[,1] * rnorm(1) + rnorm(n)
fit <- grpnet(X, glm.gaussian(y))
print(fit)

set.seed(0)
n <- 100
p <- 200
X <- matrix(rnorm(n * p), n, p)
y <- X[,1] * rnorm(1) + rnorm(n)
fit <- grpnet(X, glm.gaussian(y))
print(fit)

IO handler for SNP phased, ancestry matrix.

Description

IO handler for SNP phased, ancestry matrix.

Usage

io.snp_phased_ancestry(filename, read_mode = "file")
io.snp_phased_ancestry(filename, read_mode = "file")

Arguments

`filename`	File name.
`read_mode`	Reading mode.

Value

IO handler for SNP phased, ancestry data.

Examples

n <- 123
s <- 423
A <- 8
filename <- paste(tempdir(), "snp_phased_ancestry_dummy.snpdat", sep="/")
handle <- io.snp_phased_ancestry(filename)
calldata <- matrix(
    as.integer(sample.int(
        2, n * s * 2,
        replace=TRUE,
        prob=c(0.7, 0.3)
    ) - 1),
    n, s * 2
)
ancestries <- matrix(
    as.integer(sample.int(
        A, n * s * 2,
        replace=TRUE,
        prob=rep_len(1/A, A)
    ) - 1),
    n, s * 2
)
handle$write(calldata, ancestries, A, 1)
handle$read()
file.remove(filename)
n <- 123
s <- 423
A <- 8
filename <- paste(tempdir(), "snp_phased_ancestry_dummy.snpdat", sep="/")
handle <- io.snp_phased_ancestry(filename)
calldata <- matrix(
    as.integer(sample.int(
        2, n * s * 2,
        replace=TRUE,
        prob=c(0.7, 0.3)
    ) - 1),
    n, s * 2
)
ancestries <- matrix(
    as.integer(sample.int(
        A, n * s * 2,
        replace=TRUE,
        prob=rep_len(1/A, A)
    ) - 1),
    n, s * 2
)
handle$write(calldata, ancestries, A, 1)
handle$read()
file.remove(filename)

IO handler for SNP unphased matrix.

Description

IO handler for SNP unphased matrix.

Usage

io.snp_unphased(filename, read_mode = "file")
io.snp_unphased(filename, read_mode = "file")

Arguments

`filename`	File name.
`read_mode`	Reading mode.

Value

IO handler for SNP unphased data.

Examples

n <- 123
s <- 423
filename <- paste(tempdir(), "snp_unphased_dummy.snpdat", sep="/")
handle <- io.snp_unphased(filename)
mat <- matrix(
    as.integer(sample.int(
        3, n * s, 
        replace=TRUE, 
        prob=c(0.7, 0.2, 0.1)
    ) - 1),
    n, s
)
impute <- double(s)
handle$write(mat, "mean", impute, 1)
handle$read()
file.remove(filename)
n <- 123
s <- 423
filename <- paste(tempdir(), "snp_unphased_dummy.snpdat", sep="/")
handle <- io.snp_unphased(filename)
mat <- matrix(
    as.integer(sample.int(
        3, n * s, 
        replace=TRUE, 
        prob=c(0.7, 0.2, 0.1)
    ) - 1),
    n, s
)
impute <- double(s)
handle$write(mat, "mean", impute, 1)
handle$read()
file.remove(filename)

Creates a block-diagonal matrix.

Description

Creates a block-diagonal matrix.

Usage

matrix.block_diag(mats, n_threads = 1)
matrix.block_diag(mats, n_threads = 1)

Arguments

`mats`	List of matrices.
`n_threads`	Number of threads.

Value

Block-diagonal matrix.

Author(s)

Trevor Hastie and James Yang
Maintainer: Trevor Hastie [email protected]

Examples

n <- 100
ps <- c(10, 20, 30)
mats <- lapply(ps, function(p) {
    X <- matrix(rnorm(n * p), n, p)
    matrix.dense(t(X) %*% X, method="cov")
})
out <- matrix.block_diag(mats)
n <- 100
ps <- c(10, 20, 30)
mats <- lapply(ps, function(p) {
    X <- matrix(rnorm(n * p), n, p)
    matrix.dense(t(X) %*% X, method="cov")
})
out <- matrix.block_diag(mats)

Creates a concatenation of the matrices.

Description

Creates a concatenation of the matrices.

Usage

matrix.concatenate(mats, axis = 2, n_threads = 1)
matrix.concatenate(mats, axis = 2, n_threads = 1)

Arguments

`mats`	List of matrices.
`axis`	The axis along which the matrices will be joined. With axis = 2 (default) this function is equivalent to `cbind()` and axis = 1 is equivalent to `rbind()`.
`n_threads`	Number of threads.

Value

Concatenation of matrices. The object is an S4 class with methods for efficient computation in C++ by adelie. Note that for the object itself axis is represented with base 0 (so 1 less than the argument here).

Author(s)

Trevor Hastie and James Yang
Maintainer: Trevor Hastie [email protected]

Examples

n <- 100
ps <- c(10, 20, 30)
ps <- c(10, 20, 30)
n <- 100
mats <- lapply(ps, function(p) {
    matrix.dense(matrix(rnorm(n * p), n, p))
})
out <- matrix.concatenate(mats, axis=2)
n <- 100
ps <- c(10, 20, 30)
ps <- c(10, 20, 30)
n <- 100
mats <- lapply(ps, function(p) {
    matrix.dense(matrix(rnorm(n * p), n, p))
})
out <- matrix.concatenate(mats, axis=2)

Creates a dense matrix object.

Description

Creates a dense matrix object.

Usage

matrix.dense(mat, method = c("naive", "cov"), n_threads = 1)
matrix.dense(mat, method = c("naive", "cov"), n_threads = 1)

Arguments

`mat`	The dense matrix.
`method`	Method type, with default `method="naive"`. If `method="cov"`, the matrix is used with the solver `gaussian_cov()`. Used for `glm.gaussian()` and `glm.multigaussian()` families. Generally "naive" is used for wide matrices, and "cov" for tall matrices.
`n_threads`	Number of threads.

Value

Dense matrix. The object is an S4 class with methods for efficient computation by adelie.

Author(s)

Trevor Hastie and James Yang
Maintainer: Trevor Hastie [email protected]

Examples

n <- 100
p <- 20
X_dense <- matrix(rnorm(n * p), n, p)
out <- matrix.dense(X_dense, method="naive")
A_dense <- t(X_dense) %*% X_dense
out <- matrix.dense(A_dense, method="cov")
n <- 100
p <- 20
X_dense <- matrix(rnorm(n * p), n, p)
out <- matrix.dense(X_dense, method="naive")
A_dense <- t(X_dense) %*% X_dense
out <- matrix.dense(A_dense, method="cov")

Creates an eager covariance matrix.

Description

Creates an eager covariance matrix.

Usage

matrix.eager_cov(mat, n_threads = 1)
matrix.eager_cov(mat, n_threads = 1)

Arguments

`mat`	A dense matrix to be used with the `gaussian_cov()` solver.
`n_threads`	Number of threads.

Value

The dense covariance matrix. This matrix is exactly t(mat)%*%mat, computed with some efficiency.

Examples

n <- 100
p <- 20
mat <- matrix(rnorm(n * p), n, p)
out <- matrix.eager_cov(mat)
n <- 100
p <- 20
mat <- matrix(rnorm(n * p), n, p)
out <- matrix.eager_cov(mat)

Creates a matrix with pairwise interactions.

Description

Creates a matrix with pairwise interactions.

Usage

matrix.interaction(
  mat,
  intr_keys = NULL,
  intr_values,
  levels = NULL,
  n_threads = 1
)
matrix.interaction(
  mat,
  intr_keys = NULL,
  intr_values,
  levels = NULL,
  n_threads = 1
)

Arguments

`mat`	The dense matrix, which can include factors with levels coded as non-negative integers.
`intr_keys`	List of feature indices. This is a list of all features with which interactions can be formed. Default is `1:p` where `p` is the number of columns in `mat`.
`intr_values`	List of integer vectors of feature indices. For each of the `m <= p` indices listed in `intr_keys`, there is a vector of indices indicating which columns are candidates for interaction with that feature. If a list is `list(NULL)`, that means all other features are candidates for interactions. The default is a list of length `m` where each element is `list(NULL)`; that is `rep(list(NULL), m`.
`levels`	Number of levels for each of the columns of `mat`, with `1` representing a quantitative feature. A factor with `K` levels should be represented by the numbers `0,1,...,K-1`.
`n_threads`	Number of threads.

Value

Pairwise interaction matrix. Logic is used to avoid repetitions. For each factor variable, the column is one-hot-encoded to form a basis for that feature. The object is an S4 class with methods for efficient computation by adelie. Note that some of the arguments are transformed to C++ base 0 for internal use, and if the object is examined, it will reflect that.

Author(s)

Trevor Hastie and James Yang
Maintainer: Trevor Hastie [email protected]

Examples

n <- 10
p <- 20
X_dense <- matrix(rnorm(n * p), n, p)
X_dense[,1] <- rbinom(n, 4, 0.5)
intr_keys <- c(1, 2)
intr_values <- list(NULL, c(1, 3))
levels <- c(c(5), rep(1, p-1))
out <- matrix.interaction(X_dense, intr_keys, intr_values, levels)
n <- 10
p <- 20
X_dense <- matrix(rnorm(n * p), n, p)
X_dense[,1] <- rbinom(n, 4, 0.5)
intr_keys <- c(1, 2)
intr_values <- list(NULL, c(1, 3))
levels <- c(c(5), rep(1, p-1))
out <- matrix.interaction(X_dense, intr_keys, intr_values, levels)

Creates a Kronecker product with an identity matrix.

Description

Creates a Kronecker product with an identity matrix.

Usage

matrix.kronecker_eye(mat, K = 1, n_threads = 1)
matrix.kronecker_eye(mat, K = 1, n_threads = 1)

Arguments

`mat`	The matrix to view as a Kronecker product.
`K`	Dimension of the identity matrix (default is 1, which does essentially nothing).
`n_threads`	Number of threads.

Value

Kronecker product with identity matrix. If mat is n x p, the the resulting matrix will be nK x np. The object is an S4 class with methods for efficient computation by adelie.

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [email protected]

Examples

n <- 100
p <- 20
K <- 2
mat <- matrix(rnorm(n * p), n, p)
out <- matrix.kronecker_eye(mat, K)
mat <- matrix.dense(mat)
out <- matrix.kronecker_eye(mat, K)
n <- 100
p <- 20
K <- 2
mat <- matrix(rnorm(n * p), n, p)
out <- matrix.kronecker_eye(mat, K)
mat <- matrix.dense(mat)
out <- matrix.kronecker_eye(mat, K)

Creates a lazy covariance matrix.

Description

Creates a lazy covariance matrix.

Usage

matrix.lazy_cov(mat, n_threads = 1)
matrix.lazy_cov(mat, n_threads = 1)

Arguments

`mat`	A dense data matrix to be used with the `gaussian_cov()` solver.
`n_threads`	Number of threads.

Value

Lazy covariance matrix. This is essentially the same matrix, but with a setup to create covariance terms as needed on the fly. The object is an S4 class with methods for efficient computation by adelie.

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [email protected]

Examples

n <- 100
p <- 20
mat <- matrix(rnorm(n * p), n, p)
out <- matrix.lazy_cov(mat)
n <- 100
p <- 20
mat <- matrix(rnorm(n * p), n, p)
out <- matrix.lazy_cov(mat)

Creates a one-hot encoded matrix.

Description

Creates a one-hot encoded matrix.

Usage

matrix.one_hot(mat, levels = NULL, n_threads = 1)
matrix.one_hot(mat, levels = NULL, n_threads = 1)

Arguments

`mat`	A dense matrix, which can include factors with levels coded as non-negative integers.
`levels`	Number of levels for each of the columns of `mat`, with `1` representing a quantitative feature. A factor with `K` levels should be represented by the numbers `0,1,...,K-1`.
`n_threads`	Number of threads.

Value

One-hot encoded matrix. All the factor columns, with levels>1, are replaced by a collection of one-hot encoded versions (dummy matrices). The resulting matrix has sum(levels) columns. The object is an S4 class with methods for efficient computation by adelie. Note that some of the arguments are transformed to C++ base 0 for internal use, and if the object is examined, it will reflect that.

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [email protected]

Examples

n <- 100
p <- 20
mat <- matrix(rnorm(n * p), n, p)
out <- matrix.one_hot(mat)
n <- 100
p <- 20
mat <- matrix(rnorm(n * p), n, p)
out <- matrix.one_hot(mat)

Creates a SNP phased, ancestry matrix.

Description

Creates a SNP phased, ancestry matrix.

Usage

matrix.snp_phased_ancestry(io, n_threads = 1)
matrix.snp_phased_ancestry(io, n_threads = 1)

Arguments

`io`	IO handler.
`n_threads`	Number of threads.

Value

SNP phased, ancestry matrix.

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [email protected]

Examples

n <- 123
s <- 423
A <- 8
filename <- paste(tempdir(), "snp_phased_ancestry_dummy.snpdat", sep="/")
handle <- io.snp_phased_ancestry(filename)
calldata <- matrix(
    as.integer(sample.int(
        2, n * s * 2,
        replace=TRUE,
        prob=c(0.7, 0.3)
    ) - 1),
    n, s * 2
)
ancestries <- matrix(
    as.integer(sample.int(
        A, n * s * 2,
        replace=TRUE,
        prob=rep_len(1/A, A)
    ) - 1),
    n, s * 2
)
handle$write(calldata, ancestries, A, 1)
out <- matrix.snp_phased_ancestry(handle)
file.remove(filename)
n <- 123
s <- 423
A <- 8
filename <- paste(tempdir(), "snp_phased_ancestry_dummy.snpdat", sep="/")
handle <- io.snp_phased_ancestry(filename)
calldata <- matrix(
    as.integer(sample.int(
        2, n * s * 2,
        replace=TRUE,
        prob=c(0.7, 0.3)
    ) - 1),
    n, s * 2
)
ancestries <- matrix(
    as.integer(sample.int(
        A, n * s * 2,
        replace=TRUE,
        prob=rep_len(1/A, A)
    ) - 1),
    n, s * 2
)
handle$write(calldata, ancestries, A, 1)
out <- matrix.snp_phased_ancestry(handle)
file.remove(filename)

Creates a SNP unphased matrix.

Description

Creates a SNP unphased matrix.

Usage

matrix.snp_unphased(io, n_threads = 1)
matrix.snp_unphased(io, n_threads = 1)

Arguments

`io`	IO handler.
`n_threads`	Number of threads.

Value

SNP unphased matrix.

Examples

n <- 123
s <- 423
filename <- paste(tempdir(), "snp_unphased_dummy.snpdat", sep="/")
handle <- io.snp_unphased(filename)
mat <- matrix(
    as.integer(sample.int(
        3, n * s,
        replace=TRUE,
        prob=c(0.7, 0.2, 0.1)
    ) - 1),
    n, s
)
impute <- double(s)
handle$write(mat, "mean", impute, 1)
out <- matrix.snp_unphased(handle)
file.remove(filename)
n <- 123
s <- 423
filename <- paste(tempdir(), "snp_unphased_dummy.snpdat", sep="/")
handle <- io.snp_unphased(filename)
mat <- matrix(
    as.integer(sample.int(
        3, n * s,
        replace=TRUE,
        prob=c(0.7, 0.2, 0.1)
    ) - 1),
    n, s
)
impute <- double(s)
handle$write(mat, "mean", impute, 1)
out <- matrix.snp_unphased(handle)
file.remove(filename)

Creates a sparse matrix object.

Description

Creates a sparse matrix object.

Usage

matrix.sparse(mat, method = c("naive", "cov"), n_threads = 1)
matrix.sparse(mat, method = c("naive", "cov"), n_threads = 1)

Arguments

`mat`	A sparse matrix.
`method`	Method type, with default `method="naive"`. If `method="cov"`, the matrix is used with the solver `gaussian_cov()`. Used for `glm.gaussian()` and `glm.multigaussian()` families. Generally "naive" is used for wide matrices, and "cov" for tall matrices.
`n_threads`	Number of threads.

Value

Sparse matrix object. The object is an S4 class with methods for efficient computation by adelie.

Examples

n <- 100
p <- 20
X_dense <- matrix(rnorm(n * p), n, p)
X_sp <- as(X_dense, "dgCMatrix")
out <- matrix.sparse(X_sp, method="naive")
A_dense <- t(X_dense) %*% X_dense
A_sp <- as(A_dense, "dgCMatrix")
out <- matrix.sparse(A_sp, method="cov")
n <- 100
p <- 20
X_dense <- matrix(rnorm(n * p), n, p)
X_sp <- as(X_dense, "dgCMatrix")
out <- matrix.sparse(X_sp, method="naive")
A_dense <- t(X_dense) %*% X_dense
A_sp <- as(A_dense, "dgCMatrix")
out <- matrix.sparse(A_sp, method="cov")

Creates a standardized matrix.

Description

Creates a standardized matrix.

Usage

matrix.standardize(
  mat,
  centers = NULL,
  scales = NULL,
  weights = NULL,
  ddof = 0,
  n_threads = 1
)
matrix.standardize(
  mat,
  centers = NULL,
  scales = NULL,
  weights = NULL,
  ddof = 0,
  n_threads = 1
)

Arguments

`mat`	An `adelie` matrix.
`centers`	The center values. Default is to use the column means.
`scales`	The scale values. Default is to use the sample standard deviations.
`weights`	Observation weight vector, which defaults to 1/n per observation.
`ddof`	Degrees of freedom for standard deviations, with default 0 (1/n). The alternative is 1 leading to 1/(n-1).
`n_threads`	Number of threads.

Value

Standardized matrix. The object is an S4 class with methods for efficient computation by adelie.

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [email protected]

Examples

n <- 100
p <- 20
X <- matrix(rnorm(n * p), n, p)
out <- matrix.standardize(matrix.dense(X))
n <- 100
p <- 20
X <- matrix(rnorm(n * p), n, p)
out <- matrix.standardize(matrix.dense(X))

Creates a subset of the matrix along an axis.

Description

Creates a subset of the matrix along an axis.

Usage

matrix.subset(mat, indices, axis = 1, n_threads = 1)
matrix.subset(mat, indices, axis = 1, n_threads = 1)

Arguments

`mat`	The `adelie` matrix to subset.
`indices`	Vector of indices to subset the matrix.
`axis`	The axis along which to subset (2 is columns, 1 is rows).
`n_threads`	Number of threads.

Value

Matrix subsetted along the appropriate axis. The object is an S4 class with methods for efficient computation by adelie.

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [email protected]

Examples

n <- 100
p <- 20
X <- matrix.dense(matrix(rnorm(n * p), n, p))
indices <- c(1, 3, 10)
out <- matrix.subset(X, indices, axis=1)
out <- matrix.subset(X, indices, axis=2)
n <- 100
p <- 20
X <- matrix.dense(matrix(rnorm(n * p), n, p))
indices <- c(1, 3, 10)
out <- matrix.subset(X, indices, axis=1)
out <- matrix.subset(X, indices, axis=2)

plot the cross-validation curve produced by cv.grpnet

Description

Plots the cross-validation curve, and upper and lower standard deviation curves, as a function of the lambda values used.

Usage

## S3 method for class 'cv.grpnet'
plot(x, sign.lambda = -1, ...)
## S3 method for class 'cv.grpnet'
plot(x, sign.lambda = -1, ...)

Arguments

`x`	fitted `"cv.grpnet"` object
`sign.lambda`	Either plot against `log(lambda)` or its negative (default) if `sign.lambda=-1`
`...`	Other graphical parameters

Details

A plot is produced, and nothing is returned.

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [email protected]

References

Examples


set.seed(1010)
n = 1000
p = 100
nzc = trunc(p/10)
x = matrix(rnorm(n * p), n, p)
beta = rnorm(nzc)
fx = (x[, seq(nzc)] %*% beta)
eps = rnorm(n) * 5
y = drop(fx + eps)
px = exp(fx)
px = px/(1 + px)
ly = rbinom(n = length(px), prob = px, size = 1)
cvob1 = cv.grpnet(x, glm.gaussian(y))
plot(cvob1)
title("Gaussian Family", line = 2.5)
frame()
set.seed(1011)
cvob2 = cv.grpnet(x, glm.binomial(ly))
plot(cvob2)
title("Binomial Family", line = 2.5)

set.seed(1010)
n = 1000
p = 100
nzc = trunc(p/10)
x = matrix(rnorm(n * p), n, p)
beta = rnorm(nzc)
fx = (x[, seq(nzc)] %*% beta)
eps = rnorm(n) * 5
y = drop(fx + eps)
px = exp(fx)
px = px/(1 + px)
ly = rbinom(n = length(px), prob = px, size = 1)
cvob1 = cv.grpnet(x, glm.gaussian(y))
plot(cvob1)
title("Gaussian Family", line = 2.5)
frame()
set.seed(1011)
cvob2 = cv.grpnet(x, glm.binomial(ly))
plot(cvob2)
title("Binomial Family", line = 2.5)

plot coefficients from a "grpnet" object

Description

Produces a coefficient profile plot of the coefficient paths for a fitted "grpnet" object.

Usage

## S3 method for class 'grpnet'
plot(x, sign.lambda = -1, glm.name = TRUE, ...)
## S3 method for class 'grpnet'
plot(x, sign.lambda = -1, glm.name = TRUE, ...)

Arguments

`x`	fitted `"grpnet"` model
`sign.lambda`	This determines whether we plot against `log(lambda)` or its negative. values are `-1`(default) or `1`
`glm.name`	This is a logical (default `TRUE`), and causes the glm name of the model to be included in the plot.
`...`	Other graphical parameters to plot

Details

A coefficient profile plot is produced. If x is a multinomial or multigaussian model, the 2norm of the vector of coefficients is plotted.

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [email protected]

References

Yang, James and Hastie, Trevor. (2024) A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent. arXiv doi:10.48550/arXiv.2405.08631.

Examples

x=matrix(rnorm(100*20),100,20)
y=rnorm(100)
fit1=grpnet(x,glm.gaussian(y))
plot(fit1)
g4=diag(4)[sample(1:4,100,replace=TRUE),]
fit2=grpnet(x,glm.multinomial(g4))
plot(fit2,lwd=3)
fit3=grpnet(x,glm.gaussian(y),groups=c(1,5,9,13,17))
plot(fit3)
x=matrix(rnorm(100*20),100,20)
y=rnorm(100)
fit1=grpnet(x,glm.gaussian(y))
plot(fit1)
g4=diag(4)[sample(1:4,100,replace=TRUE),]
fit2=grpnet(x,glm.multinomial(g4))
plot(fit2,lwd=3)
fit3=grpnet(x,glm.gaussian(y),groups=c(1,5,9,13,17))
plot(fit3)

make predictions from a "cv.grpnet" object.

Description

This function makes predictions from a cross-validated grpnet model, using the stored "grpnet.fit" object, and the optimal value chosen for lambda.

Usage

## S3 method for class 'cv.grpnet'
predict(object, newx, lambda = c("lambda.1se", "lambda.min"), ...)
## S3 method for class 'cv.grpnet'
predict(object, newx, lambda = c("lambda.1se", "lambda.min"), ...)

Arguments

`object`	Fitted `"cv.grpnet"`.
`newx`	Matrix of new values for `x` at which predictions are to be made. Can be a matrix, a sparse matrix as in `Matrix` package, or else any of the matrix forms allowable in the `adelie` package. This argument is not used for `type="coefficients"`.
`lambda`	Value(s) of the penalty parameter `lambda` at which predictions are required. Default is the value `lambda="lambda.1se"` stored on the CV `object`. Alternatively `lambda="lambda.min"` can be used. If `lambda` is numeric, it is taken as the value(s) of `lambda` to be used.
`...`	Not used. Other arguments to predict.

Details

This function makes it easier to use the results of cross-validation to make a prediction.

Value

The object returned depends on the arguments.

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [email protected]

References

Yang, James and Hastie, Trevor. (2024) A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent. arXiv doi:10.48550/arXiv.2405.08631.

Examples


x = matrix(rnorm(100 * 20), 100, 20)
y = rnorm(100)
cv.fit = cv.grpnet(x, glm.gaussian(y))
predict(cv.fit, newx = x[1:5, ])
coef(cv.fit)
coef(cv.fit, lambda = "lambda.min")
predict(cv.fit, newx = x[1:5, ], lambda = c(0.001, 0.002))

x = matrix(rnorm(100 * 20), 100, 20)
y = rnorm(100)
cv.fit = cv.grpnet(x, glm.gaussian(y))
predict(cv.fit, newx = x[1:5, ])
coef(cv.fit)
coef(cv.fit, lambda = "lambda.min")
predict(cv.fit, newx = x[1:5, ], lambda = c(0.001, 0.002))

make predictions from a "grpnet" object.

Description

Similar to other predict methods, this functions predicts linear predictors, coefficients and more from a fitted "grpnet" object.

Usage

## S3 method for class 'grpnet'
predict(
  object,
  newx,
  lambda = NULL,
  type = c("link", "response", "coefficients"),
  newoffsets = NULL,
  n_threads = 1,
  ...
)

## S3 method for class 'grpnet'
coef(object, lambda = NULL, ...)
## S3 method for class 'grpnet'
predict(
  object,
  newx,
  lambda = NULL,
  type = c("link", "response", "coefficients"),
  newoffsets = NULL,
  n_threads = 1,
  ...
)

## S3 method for class 'grpnet'
coef(object, lambda = NULL, ...)

Arguments

`object`	Fitted `"grpnet"` model.
`newx`	Matrix of new values for `x` at which predictions are to be made. Can be a matrix, a sparse matrix as in `Matrix` package, or else any of the matrix forms allowable in the `adelie` package. The number of columns must match that of the input matrix used in fitting `object`. If the model object was fit with `standardize=TRUE`, the saved centers and scaling will be applied to this matrix. This argument is not used for `type="coefficients"`
`lambda`	Value(s) of the penalty parameter `lambda` at which predictions are required. Default is the entire sequence used to create the model. If values of `lambda` are supplied, the function uses linear interpolation to make predictions for values of `lambda` that do not coincide with those used in the fitting algorithm.
`type`	Type of prediction required. Type `"link"` is the default, and gives the linear predictors. Type `"response"` applies the inverse link to these predictions. Type `"coefficients"` extracts the coefficients, intercepts and the active-set sizes.
`newoffsets`	If an offset is used in the fit, then one must be supplied for making predictions (except for `type="coefficients"`.
`n_threads`	Number of threads, default `1`.
`...`	Currently ignored.

Details

The shape of the objects returned are different for "multinomial" and "multigaussian" objects coef(...) is equivalent to predict(type="coefficients",...)

Value

The object returned depends on type.

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [email protected]

References

Examples

set.seed(0)
n <- 100
p <- 200
X <- matrix(rnorm(n * p), n, p)
y <- X[,1] * rnorm(1) + rnorm(n)
fit <- grpnet(X, glm.gaussian(y))
coef(fit)
predict(fit,newx = X[1:5,])
set.seed(0)
n <- 100
p <- 200
X <- matrix(rnorm(n * p), n, p)
y <- X[,1] * rnorm(1) + rnorm(n)
fit <- grpnet(X, glm.gaussian(y))
coef(fit)
predict(fit,newx = X[1:5,])

print a cross-validated grpnet object

Description

Print a summary of the results of cross-validation for a grpnet model.

Usage

## S3 method for class 'cv.grpnet'
print(x, digits = max(3, getOption("digits") - 3), ...)
## S3 method for class 'cv.grpnet'
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

`x`	fitted 'cv.grpnet' object
`digits`	significant digits in printout
`...`	additional print arguments

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [email protected]

References

Yang, James and Hastie, Trevor. (2024) A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent. arXiv doi:10.48550/arXiv.2405.08631.

Examples


x = matrix(rnorm(100 * 20), 100, 20)
y = rnorm(100)
fit1 = cv.grpnet(x, glm.gaussian(y))
print(fit1)
x = matrix(rnorm(100 * 20), 100, 20)
y = rnorm(100)
fit1 = cv.grpnet(x, glm.gaussian(y))
print(fit1)

print a grpnet object

Description

Print a summary of the grpnet path at each step along the path.

Usage

## S3 method for class 'grpnet'
print(x, digits = max(3, getOption("digits") - 3), ...)
## S3 method for class 'grpnet'
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

`x`	fitted grpnet object
`digits`	significant digits in printout
`...`	additional print arguments

Details

The call that produced the object x is printed, followed by a three-column matrix with columns Df, ⁠%Dev⁠ and Lambda. The Df column is the number of nonzero coefficients (Df is a reasonable name only for lasso fits). ⁠%Dev⁠ is the percent deviance explained (relative to the null deviance).

Value

The matrix above is silently returned

References

Yang, James and Hastie, Trevor. (2024) A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent. arXiv doi:10.48550/arXiv.2405.08631.

Examples


x = matrix(rnorm(100 * 20), 100, 20)
y = rnorm(100)
fit1 = grpnet(x, glm.gaussian(y))
print(fit1)
x = matrix(rnorm(100 * 20), 100, 20)
y = rnorm(100)
fit1 = grpnet(x, glm.gaussian(y))
print(fit1)

Set configuration settings.

Description

Set configuration settings.

Usage

set_configs(name, value = NULL)
set_configs(name, value = NULL)

Arguments

`name`	Configuration variable name.
`value`	Value to assign to the configuration variable.

Value

Assigned value.

Examples

set_configs("hessian_min", 1e-6)
set_configs("hessian_min")
set_configs("hessian_min", 1e-6)
set_configs("hessian_min")

Package 'adelie'

Help Index

Cross-validation for grpnet

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Solves group elastic net via covariance method.

Description

Usage

Arguments

Value

Examples

Creates a Binomial GLM family object.

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Creates a Cox GLM family object.

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Creates a Gaussian GLM family object.

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Creates a MultiGaussian GLM family object.

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Creates a Multinomial GLM family object.

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Creates a Poisson GLM family object.

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

fit a GLM with group lasso or group elastic-net regularization

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

IO handler for SNP phased, ancestry matrix.

Description

Usage

Arguments

Value

Examples