Help for package higlasso

Title:

Hierarchical Integrative Group LASSO

Version:

0.9.0

Description:

Environmental health studies are increasingly measuring multiple pollutants to characterize the joint health effects attributable to exposure mixtures. However, the underlying dose-response relationship between toxicants and health outcomes of interest may be highly nonlinear, with possible nonlinear interaction effects. Hierarchical integrative group least absolute shrinkage and selection operator (HiGLASSO), developed by Boss et al (2020) <doi:10.48550/arXiv.2003.12844>, is a general framework to identify noteworthy nonlinear main and interaction effects in the presence of group structures among a set of exposures.

License:

GPL-3

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.1.0

Depends:

R (≥ 3.5.0)

Imports:

gcdnet, gglasso, purrr, splines, Rcpp

LinkingTo:

Rcpp, RcppArmadillo

Suggests:

knitr, rmarkdown, testthat

VignetteBuilder:

knitr

NeedsCompilation:

yes

Packaged:

2020-05-15 03:12:43 UTC; alex

Author:

Alexander Rix [aut, cre], Jonathan Boss [aut]

Maintainer:

Alexander Rix <alexrix@umich.edu>

Repository:

CRAN

Date/Publication:

2020-05-25 17:40:03 UTC

Cross Validated Hierarchical Integrative Group LASSO

Description

Does k-fold cross-validation for higlasso, and returns optimal values for lambda1 and lambda2.

Usage

cv.higlasso(
  Y,
  X,
  Z,
  method = c("aenet", "gglasso"),
  lambda1 = NULL,
  lambda2 = NULL,
  nlambda1 = 10,
  nlambda2 = 10,
  lambda.min.ratio = 0.05,
  nfolds = 5,
  foldid = NULL,
  sigma = 1,
  degree = 2,
  maxit = 5000,
  tol = 1e-05
)

Arguments

Y

A length n numeric response vector

X

A n x p numeric matrix

Z

A n x m numeric matrix

method

Type of initialization to use. Possible choices are gglasso for group LASSO and aenet for adaptive elastic net. Default is aenet

lambda1

A numeric vector of main effect penalties on which to tune By default, lambda1 = NULL and higlasso generates a length nlambda1 sequence of lambda1s based off of the data and min.lambda.ratio

lambda2

A numeric vector of interaction effects penalties on which to tune. By default, lambda2 = NULL and generates a sequence (length nlambda2) of lambda2s based off of the data and min.lambda.ratio

nlambda1

The number of lambda1 values to generate. Default is 10, minimum is 2. If lambda1 != NULL, this parameter is ignored

nlambda2

The number of lambda2 values to generate. Default is 10, minimum is 2. If lambda2 != NULL, this parameter is ignored

lambda.min.ratio

Ratio that calculates min lambda from max lambda. Ignored if 'lambda1' or 'lambda2' is non NULL. Default is 0.05

nfolds

Number of folds for cross validation. Default is 10. The minimum is 3, and while the maximum is the number of observations (ie leave one out cross validation)

foldid

An optional vector of values between 1 and max(foldid) identifying what fold each observation is in. Default is NULL and cv.higlasso will automatically generate foldid based off of nfolds

sigma

Scale parameter for integrative weights. Technically a third tuning parameter but defaults to 1 for computational tractability

degree

Degree of bs basis expansion. Default is 2

maxit

Maximum number of iterations. Default is 5000

tol

Tolerance for convergence. Defaults to 1e-5

Details

There are a few things to keep in mind when using cv.higlasso

higlasso uses the strong heredity principle. That is, X_1 and X_2 must included as main effects before the interaction X_1 X_2 can be included.
While higlasso uses integrative weights to help with estimation, higlasso is more of a selection method. As a result, cv.higlasso does not output coefficient estimates, only which variables are selected.
Simulation studies suggest that higlasso is a very conservative method when it comes to selecting interactions. That is, higlasso has a low false positive rate and the identification of a nonlinear interaction is a good indicator that further investigation is worthwhile.
cv.higlasso can be slow, so it may may be beneficial to tweak some of its settings (for example, nlambda1, nlambda2, and nfolds) to get a handle on how long the method will take before running the full model.

As a side effect of the conservativeness of the method, we have found that using the 1 standard error rule results in overly sparse models, and that lambda.min generally performs better.

Value

An object of type cv.higlasso with 7 elements

lambda: An nlambda1 x nlambda2 x 2 array containing each pair (lambda1, lambda2) pair.
lambda.min: lambda pair with the lowest cross validation error
lambda.1se
cvm: cross validation error at each lambda pair. The error is calculated from the mean square error.
cvse: standard error of cvm at each lambda pair.
higlasso.fit: higlasso output from fitting the whole data.
call: The call that generated the output.

Author(s)

Alexander Rix

References

A Hierarchical Integrative Group LASSO (HiGLASSO) Framework for Analyzing Environmental Mixtures. Jonathan Boss, Alexander Rix, Yin-Hsiu Chen, Naveen N. Narisetty, Zhenke Wu, Kelly K. Ferguson, Thomas F. McElrath, John D. Meeker, Bhramar Mukherjee. 2020. arXiv:2003.12844

Examples

library(higlasso)

X <- as.matrix(higlasso.df[, paste0("V", 1:7)])
Y <- higlasso.df$Y
Z <- matrix(1, nrow(X))


# This can take a bit of time

fit <- cv.higlasso(Y, X, Z)

print(fit)

Hierarchical Integrative Group LASSO

Description

HiGLASSO is a regularization based selection method designed to detect non-linear interactions between variables, particularly exposures in environmental health studies.

Usage

higlasso(
  Y,
  X,
  Z,
  method = c("aenet", "gglasso"),
  lambda1 = NULL,
  lambda2 = NULL,
  nlambda1 = 10,
  nlambda2 = 10,
  lambda.min.ratio = 0.05,
  sigma = 1,
  degree = 2,
  maxit = 5000,
  tol = 1e-05
)

Arguments

Y

A length n numeric response vector

X

A n x p numeric matrix of covariates to basis expand

Z

A n x m numeric matrix of non basis expanded and non regularized covariates

method

Type of initialization to use. Possible choices are gglasso for group LASSO and aenet for adaptive elastic net. Default is aenet

lambda1

A numeric vector of main effect penalties on which to tune By default, lambda1 = NULL and higlasso generates a length nlambda1 sequence of lambda1s based off of the data and min.lambda.ratio

lambda2

A numeric vector of interaction effects penalties on which to tune. By default, lambda2 = NULL and generates a sequence (length nlambda2) of lambda2s based off of the data and min.lambda.ratio

nlambda1

The number of lambda1 values to generate. Default is 10, minimum is 2. If lambda1 != NULL, this parameter is ignored

nlambda2

The number of lambda2 values to generate. Default is 10, minimum is 2. If lambda2 != NULL, this parameter is ignored

lambda.min.ratio

Ratio that calculates min lambda from max lambda. Ignored if 'lambda1' or 'lambda2' is non NULL. Default is 0.05

sigma

Scale parameter for integrative weights. Technically a third tuning parameter but defaults to 1 for computational tractability

degree

Degree of bs basis expansion. Default is 2

maxit

Maximum number of iterations. Default is 5000

tol

Tolerance for convergence. Default is 1e-5

Details

There are a few things to keep in mind when using higlasso

higlasso uses the strong heredity principle. That is, X_1 and X_2 must included as main effects before the interaction X_1 X_2 can be included.
While higlasso uses integrative weights to help with estimation, higlasso is more of a selection method. As a result, higlasso does not output coefficient estimates, only which variables are selected.
Simulation studies suggest that higlasso is a very conservative method when it comes to selecting interactions. That is, higlasso has a low false positive rate and the identification of a nonlinear interaction is a good indicator that further investigation is worthwhile.
higlasso can be slow, so it may may be beneficial to tweak some of its settings (for example, nlambda1 and nlambda2) to get a handle on how long the method will take before running the full model.

Value

An object of type "higlasso" with 4 elements:

lambda: An nlambda1 x nlambda2 x 2 array containing each pair (lambda1, lambda2) pair.
selected: An nlambda1 x nlambda2 x ncol(X) array containing higlasso's selections for each lambda pair.
df: The number of nonzero selections for each lambda pair.
call: The call that generated the output.

Author(s)

Alexander Rix

References

Examples

library(higlasso)

X <- as.matrix(higlasso.df[, paste0("V", 1:7)])
Y <- higlasso.df$Y
Z <- matrix(1, nrow(X))


# This can take a bit of time
higlasso.fit <- higlasso(Y, X, Z)

Synthetic Example Data For Higlasso

Description

This synthetic data is taken from the linear interaction simulations from the higlasso paper. The data generating model is:

Y = X_1 + X_2 + X_3 + X_4 + X_5 + X_1 X_2 + X_1 X_3 + X_2 X_3

+ X_1 X_4 + X_2 X_4 + X_3 X_4 + X_1 X_5

+ X_2 X_5 + X_3 X_5 + X_4 X_5 + \epsilon

Usage

higlasso.df

Format

A data.frame with 500 observations on 11 variables:

Y: Continuous response.
X1-X10: Covariates.

Print CV HiGLASSO Objects

Description

print.cv.higlasso prints a fitted "cv.higlaso" object and returns it invisibly.

Usage

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

Arguments

x

An object of type "cv.higlasso" to print

...

Further arguments passed to or from other methods

Value

The original input, x (invisibly).