Package 'biglasso'

Title: Extending Lasso Model Fitting to Big Data
Description: Extend lasso and elastic-net model fitting for ultra high-dimensional, multi-gigabyte data sets that cannot be loaded into memory. Designed to be more memory- and computation-efficient than existing lasso-fitting packages like 'glmnet' and 'ncvreg', thus allowing the user to analyze big data analysis even on an ordinary laptop.
Authors: Yaohui Zeng [aut], Chuyi Wang [aut], Tabitha Peter [aut], Patrick Breheny [aut, cre]
Maintainer: Patrick Breheny <[email protected]>
License: GPL-3
Version: 1.6.0
Built: 2024-11-17 04:56:27 UTC
Source: https://github.com/pbreheny/biglasso

Help Index

Extending Lasso Model Fitting to Big Data

Description

Extend lasso and elastic-net linear, logistic and cox regression models for ultrahigh-dimensional, multi-gigabyte data sets that cannot be loaded into available RAM. This package utilizes memory-mapped files to store the massive data on the disk and only read those into memory whenever necessary during model fitting. Moreover, some advanced feature screening rules are proposed and implemented to accelerate the model fitting. As a result, this package is much more memory- and computation-efficient and highly scalable as compared to existing lasso-fitting packages such as glmnet and ncvreg, thus allowing for powerful big data analysis even with only an ordinary laptop.

Details

Package: biglasso
Type: Package
Version: 1.4-1
Date: 2021-01-29
License: GPL-3

Penalized regression models, in particular the lasso, have been extensively applied to analyzing high-dimensional data sets. However, due to the memory limit, existing R packages are not capable of fitting lasso models for ultrahigh-dimensional, multi-gigabyte data sets which have been increasingly seen in many areas such as genetics, biomedical imaging, genome sequencing and high-frequency finance.

This package aims to fill the gap by extending lasso model fitting to Big Data in R. Version >= 1.2-3 represents a major redesign where the source code is converted into C++ (previously in C), and new feature screening rules, as well as OpenMP parallel computing, are implemented. Some key features of biglasso are summarized as below:

  1. it utilizes memory-mapped files to store the massive data on the disk, only loading data into memory when necessary during model fitting. Consequently, it's able to seamlessly data-larger-than-RAM cases.

  2. it is built upon pathwise coordinate descent algorithm with warm start, active set cycling, and feature screening strategies, which has been proven to be one of fastest lasso solvers.

  3. in incorporates our newly developed hybrid and adaptive screening that outperform state-of-the-art screening rules such as the sequential strong rule (SSR) and the sequential EDPP rule (SEDPP) with additional 1.5x to 4x speedup.

  4. the implementation is designed to be as memory-efficient as possible by eliminating extra copies of the data created by other R packages, making it at least 2x more memory-efficient than glmnet.

  5. the underlying computation is implemented in C++, and parallel computing with OpenMP is also supported.

For more information:

Note

The input design matrix X must be a bigmemory::big.matrix() object. This can be created by the function as.big.matrix in the R package bigmemory. If the data (design matrix) is very large (e.g. 10 GB) and stored in an external file, which is often the case for big data, X can be created by calling the function setupX(). In this case, there are several restrictions about the data file:

  1. the data file must be a well-formated ASCII-file, with each row corresponding to an observation and each column a variable;

  2. the data file must contain only one single type. Current version only supports double type;

  3. the data file must contain only numeric variables. If there are categorical variables, the user needs to create dummy variables for each categorical varable (by adding additional columns).

Future versions will try to address these restrictions.

Denote the number of observations and variables be, respectively, n and p. It's worth noting that the package is more suitable for wide data (ultrahigh-dimensional, ⁠p >> n⁠) as compared to long data (⁠n >> p⁠). This is because the model fitting algorithm takes advantage of sparsity assumption of high-dimensional data. To just give the user some ideas, below are some benchmarking results of the total computing time (in seconds) for solving lasso-penalized linear regression along a sequence of 100 values of the tuning parameter. In all cases, assume 20 non-zero coefficients equal +/- 2 in the true model. (Based on Version 1.2-3, screening rule "SSR-BEDPP" is used)

  • For wide data case (p > n), ⁠n = 1,000⁠:

    p 1,000 10,000 100,000 1,000,000
    Size of X 9.5 MB 95 MB 950 MB 9.5 GB
    Elapsed time (s) 0.11 0.83 8.47 85.50

    %

  • For long data case (⁠n >> p⁠), ⁠p = 1,000⁠: %

    %n 1,000 10,000 100,000 1,000,000
    %Size of X 9.5 MB 95 MB 950 MB 9.5 GB
    %Elapsed time (s) 2.50 11.43 83.69 1090.62
    %

Author(s)

Yaohui Zeng, Chuyi Wang, Tabitha Peter, and Patrick Breheny

References

  • Zeng, Y., and Breheny, P. (2017). The biglasso Package: A Memory- and Computation-Efficient Solver for Lasso Model Fitting with Big Data in R. https://arxiv.org/abs/1701.05936.

  • Tibshirani, R., Bien, J., Friedman, J., Hastie, T., Simon, N., Taylor, J., and Tibshirani, R. J. (2012). Strong rules for discarding predictors in lasso-type problems. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 74(2), 245-266.

  • Wang, J., Zhou, J., Wonka, P., and Ye, J. (2013). Lasso screening rules via dual polytope projection. In Advances in Neural Information Processing Systems, pp. 1070-1078.

  • Xiang, Z. J., and Ramadge, P. J. (2012). Fast lasso screening tests based on correlations. In Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on (pp. 2137-2140). IEEE.

  • Wang, J., Zhou, J., Liu, J., Wonka, P., and Ye, J. (2014). A safe screening rule for sparse logistic regression. In Advances in Neural Information Processing Systems, pp. 1053-1061.

Examples

## Not run: 
## Example of reading data from external big data file, fit lasso model, 
## and run cross validation in parallel

# simulated design matrix, 1000 observations, 500,000 variables, ~ 5GB
# there are 10 true variables with non-zero coefficient 2.
xfname <- 'x_e3_5e5.txt' 
yfname <- 'y_e3_5e5.txt' # response vector
time <- system.time(
  X <- setupX(xfname, sep = '\t') # create backing files (.bin, .desc)
)
print(time) # ~ 7 minutes; this is just one-time operation
dim(X)

# the big.matrix then can be retrieved by its descriptor file (.desc) in any new R session. 
rm(X)
xdesc <- 'x_e3_5e5.desc' 
X <- attach.big.matrix(xdesc)
dim(X)

y <- as.matrix(read.table(yfname, header = F))
time.fit <- system.time(
  fit <- biglasso(X, y, family = 'gaussian', screen = 'Hybrid')
)
print(time.fit) # ~ 44 seconds for fitting a lasso model along the entire solution path

# cross validation in parallel
seed <- 1234
time.cvfit <- system.time(
  cvfit <- cv.biglasso(X, y, family = 'gaussian', screen = 'Hybrid', 
                       seed = seed, ncores = 4, nfolds = 10)
)
print(time.cvfit) # ~ 3 minutes for 10-fold cross validation
plot(cvfit)
summary(cvfit)

## End(Not run)

Fit lasso penalized regression path for big data

Description

Extend lasso model fitting to big data that cannot be loaded into memory. Fit solution paths for linear, logistic or Cox regression models penalized by lasso, ridge, or elastic-net over a grid of values for the regularization parameter lambda.

Usage

biglasso(
  X,
  y,
  row.idx = 1:nrow(X),
  penalty = c("lasso", "ridge", "enet"),
  family = c("gaussian", "binomial", "cox", "mgaussian"),
  alg.logistic = c("Newton", "MM"),
  screen = c("Adaptive", "SSR", "Hybrid", "None"),
  safe.thresh = 0,
  update.thresh = 1,
  ncores = 1,
  alpha = 1,
  lambda.min = ifelse(nrow(X) > ncol(X), 0.001, 0.05),
  nlambda = 100,
  lambda.log.scale = TRUE,
  lambda,
  eps = 1e-07,
  max.iter = 1000,
  dfmax = ncol(X) + 1,
  penalty.factor = rep(1, ncol(X)),
  warn = TRUE,
  output.time = FALSE,
  return.time = TRUE,
  verbose = FALSE
)

Arguments

X

The design matrix, without an intercept. It must be a double type bigmemory::big.matrix() object. The function standardizes the data and includes an intercept internally by default during the model fitting.
y

The response vector for family="gaussian" or family="binomial". For family="cox", y should be a two-column matrix with columns 'time' and 'status'. The latter is a binary variable, with '1' indicating death, and '0' indicating right censored. For family="mgaussin", y should be a n*m matrix where n is the sample size and m is the number of responses.
row.idx

The integer vector of row indices of X that used for fitting the model. 1:nrow(X) by default.
penalty

The penalty to be applied to the model. Either "lasso" (the default), "ridge", or "enet" (elastic net).
family

Either "gaussian", "binomial", "cox" or "mgaussian" depending on the response.
alg.logistic

The algorithm used in logistic regression. If "Newton" then the exact hessian is used (default); if "MM" then a majorization-minimization algorithm is used to set an upper-bound on the hessian matrix. This can be faster, particularly in data-larger-than-RAM case.
screen

The feature screening rule used at each lambda that discards features to speed up computation: "SSR" (default if penalty="ridge" or penalty="enet" )is the sequential strong rule; "Hybrid" is our newly proposed hybrid screening rules which combine the strong rule with a safe rule. "Adaptive" (default for penalty="lasso" without penalty.factor) is our newly proposed adaptive rules which reuse screening reference for multiple lambda values. Note that: (1) for linear regression with elastic net penalty, both "SSR" and "Hybrid" are applicable since version 1.3-0; (2) only "SSR" is applicable to elastic-net-penalized logistic regression or cox regression; (3) active set cycling strategy is incorporated with these screening rules.
safe.thresh

the threshold value between 0 and 1 that controls when to stop safe test. For example, 0.01 means to stop safe test at next lambda iteration if the number of features rejected by safe test at current lambda iteration is not larger than 1\ to always turn off safe test, whereas 0 (default) means to turn off safe test if the number of features rejected by safe test is 0 at current lambda.
update.thresh

the non negative threshold value that controls how often to update the reference of safe rules for "Adaptive" methods. Smaller value means updating more often.
ncores

The number of OpenMP threads used for parallel computing.
alpha

The elastic-net mixing parameter that controls the relative contribution from the lasso (l1) and the ridge (l2) penalty. The penalty is defined as

α∣∣β∣∣1+(1−α)/2∣∣β∣∣22.\alpha||\beta||_1 + (1-\alpha)/2||\beta||_2^2.

alpha=1 is the lasso penalty, alpha=0 the ridge penalty, alpha in between 0 and 1 is the elastic-net ("enet") penalty.
lambda.min

The smallest value for lambda, as a fraction of lambda.max. Default is .001 if the number of observations is larger than the number of covariates and .05 otherwise.
nlambda

The number of lambda values. Default is 100.
lambda.log.scale

Whether compute the grid values of lambda on log scale (default) or linear scale.
lambda

A user-specified sequence of lambda values. By default, a sequence of values of length nlambda is computed, equally spaced on the log scale.
eps

Convergence threshold for inner coordinate descent. The algorithm iterates until the maximum change in the objective after any coefficient update is less than eps times the null deviance. Default value is 1e-7.
max.iter

Maximum number of iterations. Default is 1000.
dfmax

Upper bound for the number of nonzero coefficients. Default is no upper bound. However, for large data sets, computational burden may be heavy for models with a large number of nonzero coefficients.
penalty.factor

A multiplicative factor for the penalty applied to each coefficient. If supplied, penalty.factor must be a numeric vector of length equal to the number of columns of X. The purpose of penalty.factor is to apply differential penalization if some coefficients are thought to be more likely than others to be in the model. Current package doesn't allow unpenalized coefficients. That ispenalty.factor cannot be 0. penalty.factor is only supported for "SSR" screen.
warn

Return warning messages for failures to converge and model saturation? Default is TRUE.
output.time

Whether to print out the start and end time of the model fitting. Default is FALSE.
return.time

Whether to return the computing time of the model fitting. Default is TRUE.
verbose

Whether to output the timing of each lambda iteration. Default is FALSE.

Details

The objective function for linear regression or multiple responses linear regression (family = "gaussian" or family = "mgaussian") is

12nRSS+λ∗penalty,\frac{1}{2n}\textrm{RSS} + \lambda*\textrm{penalty},

where for family = "mgaussian"), a group-lasso type penalty is applied. For logistic regression (family = "binomial") it is

−1nloglike+λ∗penalty,-\frac{1}{n} loglike + \lambda*\textrm{penalty},

, for cox regression, breslow approximation for ties is applied.

Several advanced feature screening rules are implemented. For lasso-penalized linear regression, all the options of screen are applicable. Our proposal adaptive rule - "Adaptive" - achieves highest speedup so it's the recommended one, especially for ultrahigh-dimensional large-scale data sets. For cox regression and/or the elastic net penalty, only "SSR" is applicable for now. More efficient rules are under development.

Value

An object with S3 class "biglasso" for ⁠"gaussian", "binomial", "cox"⁠ families, or an object with S3 class "mbiglasso" for "mgaussian" family, with following variables.

beta

The fitted matrix of coefficients, store in sparse matrix representation. The number of rows is equal to the number of coefficients, whereas the number of columns is equal to nlambda. For "mgaussian" family with m responses, it is a list of m such matrices.
iter

A vector of length nlambda containing the number of iterations until convergence at each value of lambda.
lambda

The sequence of regularization parameter values in the path.
penalty

Same as above.
family

Same as above.
alpha

Same as above.
loss

A vector containing either the residual sum of squares (for ⁠"gaussian", "mgaussian"⁠) or negative log-likelihood (for ⁠"binomial", "cox"⁠) of the fitted model at each value of lambda.
penalty.factor

Same as above.
n

The number of observations used in the model fitting. It's equal to length(row.idx).
center

The sample mean vector of the variables, i.e., column mean of the sub-matrix of X used for model fitting.
scale

The sample standard deviation of the variables, i.e., column standard deviation of the sub-matrix of X used for model fitting.
y

The response vector used in the model fitting. Depending on row.idx, it could be a subset of the raw input of the response vector y.
screen

Same as above.
col.idx

The indices of features that have 'scale' value greater than 1e-6. Features with 'scale' less than 1e-6 are removed from model fitting.
rejections

The number of features rejected at each value of lambda.
safe_rejections

The number of features rejected by safe rules at each value of lambda.

Author(s)

Yaohui Zeng, Chuyi Wang and Patrick Breheny

See Also

biglasso-package, setupX(), cv.biglasso(), plot.biglasso(), ncvreg::ncvreg()

Examples

## Linear regression
data(colon)
X <- colon$X
y <- colon$y
X.bm <- as.big.matrix(X)
# lasso, default
par(mfrow=c(1,2))
fit.lasso <- biglasso(X.bm, y, family = 'gaussian')
plot(fit.lasso, log.l = TRUE, main = 'lasso')
# elastic net
fit.enet <- biglasso(X.bm, y, penalty = 'enet', alpha = 0.5, family = 'gaussian')
plot(fit.enet, log.l = TRUE, main = 'elastic net, alpha = 0.5')

## Logistic regression
data(colon)
X <- colon$X
y <- colon$y
X.bm <- as.big.matrix(X)
# lasso, default
par(mfrow = c(1, 2))
fit.bin.lasso <- biglasso(X.bm, y, penalty = 'lasso', family = "binomial")
plot(fit.bin.lasso, log.l = TRUE, main = 'lasso')
# elastic net
fit.bin.enet <- biglasso(X.bm, y, penalty = 'enet', alpha = 0.5, family = "binomial")
plot(fit.bin.enet, log.l = TRUE, main = 'elastic net, alpha = 0.5')

## Cox regression
set.seed(10101)
N <- 1000; p <- 30; nzc <- p/3
X <- matrix(rnorm(N * p), N, p)
beta <- rnorm(nzc)
fx <- X[, seq(nzc)] %*% beta/3
hx <- exp(fx)
ty <- rexp(N, hx)
tcens <- rbinom(n = N, prob = 0.3, size = 1)  # censoring indicator
y <- cbind(time = ty, status = 1 - tcens)  # y <- Surv(ty, 1 - tcens) with library(survival)
X.bm <- as.big.matrix(X)
fit <- biglasso(X.bm, y, family = "cox")
plot(fit, main = "cox")

## Multiple responses linear regression
set.seed(10101)
n=300; p=300; m=5; s=10; b=1
x = matrix(rnorm(n * p), n, p)
beta = matrix(seq(from=-b,to=b,length.out=s*m),s,m)
y = x[,1:s] %*% beta + matrix(rnorm(n*m,0,1),n,m)
x.bm = as.big.matrix(x)
fit = biglasso(x.bm, y, family = "mgaussian")
plot(fit, main = "mgaussian")

Direct interface to biglasso fitting, no preprocessing

Description

This function is intended for users who know exactly what they're doing and want complete control over the fitting process. It

  • does NOT add an intercept

  • does NOT standardize the design matrix

  • does NOT set up a path for lambda (the lasso tuning parameter) all of the above are critical steps in data analysis. However, a direct API has been provided for use in situations where the lasso fitting process is an internal component of a more complicated algorithm and standardization must be handled externally.

Usage

biglasso_fit(
  X,
  y,
  r,
  init = rep(0, ncol(X)),
  xtx,
  penalty = "lasso",
  lambda,
  alpha = 1,
  gamma,
  ncores = 1,
  max.iter = 1000,
  eps = 1e-05,
  dfmax = ncol(X) + 1,
  penalty.factor = rep(1, ncol(X)),
  warn = TRUE,
  output.time = FALSE,
  return.time = TRUE
)

Arguments

X

The design matrix, without an intercept. It must be a double type bigmemory::big.matrix() object.
y

The response vector
r

Residuals (length n vector) corresponding to init. WARNING: If you supply an incorrect value of r, the solution will be incorrect.
init

Initial values for beta. Default: zero (length p vector)
xtx

X scales: the jth element should equal crossprod(X[,j])/n. In particular, if X is standardized, one should pass xtx = rep(1, p). WARNING: If you supply an incorrect value of xtx, the solution will be incorrect. (length p vector)
penalty

String specifying which penalty to use. Default is 'lasso', Other options are 'SCAD' and 'MCP' (the latter are non-convex)
lambda

A single value for the lasso tuning parameter.
alpha

The elastic-net mixing parameter that controls the relative contribution from the lasso (l1) and the ridge (l2) penalty. The penalty is defined as:

α∣∣β∣∣1+(1−α)/2∣∣β∣∣22.\alpha||\beta||_1 + (1-\alpha)/2||\beta||_2^2.

alpha=1 is the lasso penalty, alpha=0 the ridge penalty, alpha in between 0 and 1 is the elastic-net ("enet") penalty.
gamma

Tuning parameter value for nonconvex penalty. Defaults are 3.7 for penalty = 'SCAD' and 3 for penalty = 'MCP'
ncores

The number of OpenMP threads used for parallel computing.
max.iter

Maximum number of iterations. Default is 1000.
eps

Convergence threshold for inner coordinate descent. The algorithm iterates until the maximum change in the objective after any coefficient update is less than eps times the null deviance. Default value is 1e-7.
dfmax

Upper bound for the number of nonzero coefficients. Default is no upper bound. However, for large data sets, computational burden may be heavy for models with a large number of nonzero coefficients.
penalty.factor

A multiplicative factor for the penalty applied to each coefficient. If supplied, penalty.factor must be a numeric vector of length equal to the number of columns of X.
warn

Return warning messages for failures to converge and model saturation? Default is TRUE.
output.time

Whether to print out the start and end time of the model fitting. Default is FALSE.
return.time

Whether to return the computing time of the model fitting. Default is TRUE.

Details

Note:

  • Hybrid safe-strong rules are turned off for biglasso_fit(), as these rely on standardization

  • Currently, the function only works with linear regression (family = 'gaussian').

Value

An object with S3 class "biglasso" with following variables.

beta

The vector of estimated coefficients
iter

A vector of length nlambda containing the number of iterations until convergence
resid

Vector of residuals calculated from estimated coefficients.
lambda

The sequence of regularization parameter values in the path.
alpha

Same as in biglasso()
loss

A vector containing either the residual sum of squares of the fitted model at each value of lambda.
penalty.factor

Same as in biglasso().
n

The number of observations used in the model fitting.
y

The response vector used in the model fitting.

Author(s)

Tabitha Peter and Patrick Breheny

Examples

data(Prostate)
X <- cbind(1, Prostate$X)
xtx <- apply(X, 2, crossprod)/nrow(X)
y <- Prostate$y
X.bm <- as.big.matrix(X)
init <- rep(0, ncol(X))
fit <- biglasso_fit(X = X.bm, y = y, r=y, init = init, xtx = xtx,
  lambda = 0.1, penalty.factor=c(0, rep(1, ncol(X)-1)), max.iter = 10000)
fit$beta
  
fit <- biglasso_fit(X = X.bm, y = y, r=y, init = init, xtx = xtx, penalty='MCP',
  lambda = 0.1, penalty.factor=c(0, rep(1, ncol(X)-1)), max.iter = 10000)
fit$beta

Direct interface to biglasso fitting, no preprocessing, path version

Description

This function is intended for users who know exactly what they're doing and want complete control over the fitting process. It

  • does NOT add an intercept

  • does NOT standardize the design matrix both of the above are critical steps in data analysis. However, a direct API has been provided for use in situations where the lasso fitting process is an internal component of a more complicated algorithm and standardization must be handled externally.

Usage

biglasso_path(
  X,
  y,
  r,
  init = rep(0, ncol(X)),
  xtx,
  penalty = "lasso",
  lambda,
  alpha = 1,
  gamma,
  ncores = 1,
  max.iter = 1000,
  eps = 1e-05,
  dfmax = ncol(X) + 1,
  penalty.factor = rep(1, ncol(X)),
  warn = TRUE,
  output.time = FALSE,
  return.time = TRUE
)

Arguments

X

The design matrix, without an intercept. It must be a double type bigmemory::big.matrix() object.
y

The response vector
r

Residuals (length n vector) corresponding to init. WARNING: If you supply an incorrect value of r, the solution will be incorrect.
init

Initial values for beta. Default: zero (length p vector)
xtx

X scales: the jth element should equal crossprod(X[,j])/n. In particular, if X is standardized, one should pass xtx = rep(1, p). WARNING: If you supply an incorrect value of xtx, the solution will be incorrect. (length p vector)
penalty

String specifying which penalty to use. Default is 'lasso', Other options are 'SCAD' and 'MCP' (the latter are non-convex)
lambda

A vector of numeric values the lasso tuning parameter.
alpha

The elastic-net mixing parameter that controls the relative contribution from the lasso (l1) and the ridge (l2) penalty. The penalty is defined as:

α∣∣β∣∣1+(1−α)/2∣∣β∣∣22.\alpha||\beta||_1 + (1-\alpha)/2||\beta||_2^2.

alpha=1 is the lasso penalty, alpha=0 the ridge penalty, alpha in between 0 and 1 is the elastic-net ("enet") penalty.
gamma

Tuning parameter value for nonconvex penalty. Defaults are 3.7 for penalty = 'SCAD' and 3 for penalty = 'MCP'
ncores

The number of OpenMP threads used for parallel computing.
max.iter

Maximum number of iterations. Default is 1000.
eps

Convergence threshold for inner coordinate descent. The algorithm iterates until the maximum change in the objective after any coefficient update is less than eps times the null deviance. Default value is 1e-7.
dfmax

Upper bound for the number of nonzero coefficients. Default is no upper bound. However, for large data sets, computational burden may be heavy for models with a large number of nonzero coefficients.
penalty.factor

A multiplicative factor for the penalty applied to each coefficient. If supplied, penalty.factor must be a numeric vector of length equal to the number of columns of X.
warn

Return warning messages for failures to converge and model saturation? Default is TRUE.
output.time

Whether to print out the start and end time of the model fitting. Default is FALSE.
return.time

Whether to return the computing time of the model fitting. Default is TRUE.

Details

biglasso_path() works identically to biglasso_fit() except it offers the additional option of fitting models across a path of tuning parameter values.

Note:

  • Hybrid safe-strong rules are turned off for biglasso_fit(), as these rely on standardization

  • Currently, the function only works with linear regression (family = 'gaussian').

Value

An object with S3 class "biglasso" with following variables.

beta

A sparse matrix where rows are estimates a given coefficient across all values of lambda
iter

A vector of length nlambda containing the number of iterations until convergence
resid

Vector of residuals calculated from estimated coefficients.
lambda

The sequence of regularization parameter values in the path.
alpha

Same as in biglasso()
loss

A vector containing either the residual sum of squares of the fitted model at each value of lambda.
penalty.factor

Same as in biglasso().
n

The number of observations used in the model fitting.
y

The response vector used in the model fitting.

Author(s)

Tabitha Peter and Patrick Breheny

Examples

data(Prostate)
X <- cbind(1, Prostate$X)
xtx <- apply(X, 2, crossprod)/nrow(X)
y <- Prostate$y
X.bm <- as.big.matrix(X)
init <- rep(0, ncol(X))
fit <- biglasso_path(X = X.bm, y = y, r = y, init = init, xtx = xtx,
  lambda = c(0.5, 0.1, 0.05, 0.01, 0.001), 
  penalty.factor=c(0, rep(1, ncol(X)-1)), max.iter=2000)
fit$beta
  
fit <- biglasso_path(X = X.bm, y = y, r = y, init = init, xtx = xtx,
  lambda = c(0.5, 0.1, 0.05, 0.01, 0.001), penalty='MCP',
  penalty.factor=c(0, rep(1, ncol(X)-1)), max.iter = 2000)
fit$beta

Gene expression data from colon-cancer patients

Description

The data file contains gene expression data of 62 samples (40 tumor samples, 22 normal samples) from colon-cancer patients analyzed with an Affymetrix oligonucleotide Hum6000 array.

Format

A list of 2 variables included in colon:

  • X: a 62-by-2000 matrix that records the gene expression data. Used as design matrix.

  • y: a binary vector of length 62 recording the sample status: 1 = tumor; 0 = normal. Used as response vector.

Source

The raw data can be found on Bioconductor: https://bioconductor.org/packages/release/data/experiment/html/colonCA.html.

References

Examples

data(colon)
X <- colon$X
y <- colon$y
str(X)
dim(X)
X.bm <- as.big.matrix(X, backingfile = "") # convert to big.matrix object
str(X.bm)
dim(X.bm)

Cross-validation for biglasso

Description

Perform k-fold cross validation for penalized regression models over a grid of values for the regularization parameter lambda.

Usage

cv.biglasso(
  X,
  y,
  row.idx = 1:nrow(X),
  family = c("gaussian", "binomial", "cox", "mgaussian"),
  eval.metric = c("default", "MAPE", "auc", "class"),
  ncores = parallel::detectCores(),
  ...,
  nfolds = 5,
  seed,
  cv.ind,
  trace = FALSE,
  grouped = TRUE
)

Arguments

X

The design matrix, without an intercept, as in biglasso().
y

The response vector, as in biglasso.
row.idx

The integer vector of row indices of X that used for fitting the model. as in biglasso.
family

Either "gaussian", "binomial", "cox" or "mgaussian" depending on the response. "cox" and "mgaussian" are not supported yet.
eval.metric

The evaluation metric for the cross-validated error and for choosing optimal lambda. "default" for linear regression is MSE (mean squared error), for logistic regression is binomial deviance. "MAPE", for linear regression only, is the Mean Absolute Percentage Error. "auc", for binary classification, is the area under the receiver operating characteristic curve (ROC). "class", for binary classification, gives the misclassification error.
ncores

The number of cores to use for parallel execution of the cross-validation folds, run on a cluster created by the parallel package. (This is also supplied to the ncores argument in biglasso(), which is the number of OpenMP threads, but only for the first call of biglasso() that is run on the entire data. The individual calls of biglasso() for the CV folds are run without the ncores argument.)
...

Additional arguments to biglasso.
nfolds

The number of cross-validation folds. Default is 5.
seed

The seed of the random number generator in order to obtain reproducible results.
cv.ind

Which fold each observation belongs to. By default the observations are randomly assigned by cv.biglasso.
trace

If set to TRUE, cv.biglasso will inform the user of its progress by announcing the beginning of each CV fold. Default is FALSE.
grouped

Whether to calculate CV standard error (cvse) over CV folds (TRUE), or over all cross-validated predictions. Ignored when eval.metric is 'auc'.

Details

The function calls biglasso nfolds times, each time leaving out 1/nfolds of the data. The cross-validation error is based on the residual sum of squares when family="gaussian" and the binomial deviance when family="binomial".

The S3 class object cv.biglasso inherits class ncvreg::cv.ncvreg(). So S3 functions such as ⁠"summary", "plot"⁠ can be directly applied to the cv.biglasso object.

Value

An object with S3 class "cv.biglasso" which inherits from class "cv.ncvreg". The following variables are contained in the class (adopted from ncvreg::cv.ncvreg()).

cve

The error for each value of lambda, averaged across the cross-validation folds.
cvse

The estimated standard error associated with each value of for cve.
lambda

The sequence of regularization parameter values along which the cross-validation error was calculated.
fit

The fitted biglasso object for the whole data.
min

The index of lambda corresponding to lambda.min.
lambda.min

The value of lambda with the minimum cross-validation error.
lambda.1se

The largest value of lambda for which the cross-validation error is at most one standard error larger than the minimum cross-validation error.
null.dev

The deviance for the intercept-only model.
pe

If family="binomial", the cross-validation prediction error for each value of lambda.
cv.ind

Same as above.

Author(s)

Yaohui Zeng and Patrick Breheny

See Also

biglasso(), plot.cv.biglasso(), summary.cv.biglasso(), setupX()

Examples

## Not run: 
## cv.biglasso
data(colon)
X <- colon$X
y <- colon$y
X.bm <- as.big.matrix(X)

## logistic regression
cvfit <- cv.biglasso(X.bm, y, family = 'binomial', seed = 1234, ncores = 2)
par(mfrow = c(2, 2))
plot(cvfit, type = 'all')
summary(cvfit)

## End(Not run)

Plot coefficients from a "biglasso" object

Description

Produce a plot of the coefficient paths for a fitted biglasso() object.

Usage

## S3 method for class 'biglasso'
plot(x, alpha = 1, log.l = TRUE, ...)

Arguments

x

Fitted biglasso() model.
alpha

Controls alpha-blending, helpful when the number of covariates is large. Default is alpha=1.
log.l

Should horizontal axis be on the log scale? Default is TRUE.
...

Other graphical parameters to plot()

Author(s)

Yaohui Zeng and Patrick Breheny

See Also

biglasso(), cv.biglasso()

Examples

## See examples in "biglasso"

Plots the cross-validation curve from a "cv.biglasso" object

Description

Plot the cross-validation curve from a cv.biglasso() object, along with standard error bars.

Usage

## S3 method for class 'cv.biglasso'
plot(
  x,
  log.l = TRUE,
  type = c("cve", "rsq", "scale", "snr", "pred", "all"),
  selected = TRUE,
  vertical.line = TRUE,
  col = "red",
  ...
)

Arguments

x

A "cv.biglasso" object.
log.l

Should horizontal axis be on the log scale? Default is TRUE.
type

What to plot on the vertical axis. cve plots the cross-validation error (deviance); rsq plots an estimate of the fraction of the deviance explained by the model (R-squared); snr plots an estimate of the signal-to-noise ratio; scale plots, for family="gaussian", an estimate of the scale parameter (standard deviation); pred plots, for family="binomial", the estimated prediction error; all produces all of the above.
selected

If TRUE (the default), places an axis on top of the plot denoting the number of variables in the model (i.e., that have a nonzero regression coefficient) at that value of lambda.
vertical.line

If TRUE (the default), draws a vertical line at the value where cross-validaton error is minimized.
col

Controls the color of the dots (CV estimates).
...

Other graphical parameters to plot

Details

Error bars representing approximate 68\ along with the estimates at value of lambda. For rsq and snr, these confidence intervals are quite crude, especially near.

Author(s)

Yaohui Zeng and Patrick Breheny

See Also

biglasso(), cv.biglasso()

Examples

## See examples in "cv.biglasso"

Plot coefficients from a "mbiglasso" object

Description

Produce a plot of the coefficient paths for a fitted multiple responses mbiglasso object.

Usage

## S3 method for class 'mbiglasso'
plot(x, alpha = 1, log.l = TRUE, norm.beta = TRUE, ...)

Arguments

x

Fitted mbiglasso model.
alpha

Controls alpha-blending, helpful when the number of covariates is large. Default is alpha=1.
log.l

Should horizontal axis be on the log scale? Default is TRUE.
norm.beta

Should the vertical axis be the l2 norm of coefficients for each variable? Default is TRUE. If False, the vertical axis is the coefficients.
...

Other graphical parameters to plot()

Author(s)

Chuyi Wang

See Also

biglasso()

Examples

## See examples in "biglasso"

Model predictions based on a fitted biglasso object

Description

Extract predictions (fitted reponse, coefficients, etc.) from a fitted biglasso() object.

Usage

## S3 method for class 'biglasso'
predict(
  object,
  X,
  row.idx = 1:nrow(X),
  type = c("link", "response", "class", "coefficients", "vars", "nvars"),
  lambda,
  which = 1:length(object$lambda),
  ...
)

## S3 method for class 'mbiglasso'
predict(
  object,
  X,
  row.idx = 1:nrow(X),
  type = c("link", "response", "coefficients", "vars", "nvars"),
  lambda,
  which = 1:length(object$lambda),
  k = 1,
  ...
)

## S3 method for class 'biglasso'
coef(object, lambda, which = 1:length(object$lambda), drop = TRUE, ...)

## S3 method for class 'mbiglasso'
coef(object, lambda, which = 1:length(object$lambda), intercept = TRUE, ...)

Arguments

object

A fitted "biglasso" model object.
X

Matrix of values at which predictions are to be made. It must be a bigmemory::big.matrix() object. Not used for type="coefficients".
row.idx

Similar to that in biglasso(), it's a vector of the row indices of X that used for the prediction. 1:nrow(X) by default.
type

Type of prediction:

  • "link" returns the linear predictors

  • "response" gives the fitted values

  • "class" returns the binomial outcome with the highest probability

  • "coefficients" returns the coefficients

  • "vars" returns a list containing the indices and names of the nonzero variables at each value of lambda

  • "nvars" returns the number of nonzero coefficients at each value of lambda

lambda

Values of the regularization parameter lambda at which predictions are requested. Linear interpolation is used for values of lambda not in the sequence of lambda values in the fitted models.
which

Indices of the penalty parameter lambda at which predictions are required. By default, all indices are returned. If lambda is specified, this will override which.
...

Not used.
k

Index of the response to predict in multiple responses regression ( family="mgaussian").
drop

If coefficients for a single value of lambda are to be returned, reduce dimensions to a vector? Setting drop=FALSE returns a 1-column matrix.
intercept

Whether the intercept should be included in the returned coefficients. For family="mgaussian" only.

Value

The object returned depends on type.

Author(s)

Yaohui Zeng and Patrick Breheny

See Also

biglasso(), cv.biglasso()

Examples

## Logistic regression
data(colon)
X <- colon$X
y <- colon$y
X.bm <- as.big.matrix(X, backingfile = "")
fit <- biglasso(X.bm, y, penalty = 'lasso', family = "binomial")
coef <- coef(fit, lambda=0.05, drop = TRUE)
coef[which(coef != 0)]
predict(fit, X.bm, type="link", lambda=0.05)[1:10]
predict(fit, X.bm, type="response", lambda=0.05)[1:10]
predict(fit, X.bm, type="class", lambda=0.1)[1:10]
predict(fit, type="vars", lambda=c(0.05, 0.1))
predict(fit, type="nvars", lambda=c(0.05, 0.1))

Model predictions based on a fitted cv.biglasso() object

Description

Extract predictions from a fitted cv.biglasso() object.

Usage

## S3 method for class 'cv.biglasso'
predict(
  object,
  X,
  row.idx = 1:nrow(X),
  type = c("link", "response", "class", "coefficients", "vars", "nvars"),
  lambda = object$lambda.min,
  which = object$min,
  ...
)

## S3 method for class 'cv.biglasso'
coef(object, lambda = object$lambda.min, which = object$min, ...)

Arguments

object

A fitted "cv.biglasso" model object.
X

Matrix of values at which predictions are to be made. It must be a bigmemory::big.matrix() object. Not used for type="coefficients".
row.idx

Similar to that in biglasso(), it's a vector of the row indices of X that used for the prediction. 1:nrow(X) by default.
type

Type of prediction:

  • "link" returns the linear predictors

  • "response" gives the fitted values

  • "class" returns the binomial outcome with the highest probability

  • "coefficients" returns the coefficients

  • "vars" returns a list containing the indices and names of the nonzero variables at each value of lambda

  • "nvars" returns the number of nonzero coefficients at each value of lambda

lambda

Values of the regularization parameter lambda at which predictions are requested. The default value is the one corresponding to the minimum cross-validation error. Accepted values are also the strings "lambda.min" (lambda of minimum cross-validation error) and "lambda.1se" (Largest value of lambda for which the cross-validation error was at most one standard error larger than the minimum.).
which

Indices of the penalty parameter lambda at which predictions are requested. The default value is the index of lambda corresponding to lambda.min. Note: this is overridden if lambda is specified.
...

Not used.

Value

The object returned depends on type.

Author(s)

Yaohui Zeng and Patrick Breheny

See Also

biglasso(), cv.biglasso()

Examples

## Not run: 
## predict.cv.biglasso
data(colon)
X <- colon$X
y <- colon$y
X.bm <- as.big.matrix(X, backingfile = "")
fit <- biglasso(X.bm, y, penalty = 'lasso', family = "binomial")
cvfit <- cv.biglasso(X.bm, y, penalty = 'lasso', family = "binomial", seed = 1234, ncores = 2)
coef <- coef(cvfit)
coef[which(coef != 0)]
predict(cvfit, X.bm, type = "response")
predict(cvfit, X.bm, type = "link")
predict(cvfit, X.bm, type = "class")
predict(cvfit, X.bm, lambda = "lambda.1se")

## End(Not run)

Set up design matrix X by reading data from big data file

Description

Set up the design matrix X as a big.matrix object based on external massive data file stored on disk that cannot be fullly loaded into memory. The data file must be a well-formated ASCII-file, and contains only one single type. Current version only supports double type. Other restrictions about the data file are described in biglasso-package. This function reads the massive data, and creates a big.matrix object. By default, the resulting big.matrix is file-backed, and can be shared across processors or nodes of a cluster.

Usage

setupX(
  filename,
  dir = getwd(),
  sep = ",",
  backingfile = paste0(unlist(strsplit(filename, split = "\\."))[1], ".bin"),
  descriptorfile = paste0(unlist(strsplit(filename, split = "\\."))[1], ".desc"),
  type = "double",
  ...
)

Arguments

filename

The name of the data file. For example, "dat.txt".
dir

The directory used to store the binary and descriptor files associated with the big.matrix. The default is current working directory.
sep

The field separator character. For example, "," for comma-delimited files (the default); "\t" for tab-delimited files.
backingfile

The binary file associated with the file-backed big.matrix. By default, its name is the same as filename with the extension replaced by ".bin".
descriptorfile

The descriptor file used for the description of the file-backed big.matrix. By default, its name is the same as filename with the extension replaced by ".desc".
type

The data type. Only "double" is supported for now.
...

Additional arguments that can be passed into function bigmemory::read.big.matrix().

Details

For a data set, this function needs to be called only one time to set up the big.matrix object with two backing files (.bin, .desc) created in current working directory. Once set up, the data can be "loaded" into any (new) R session by calling attach.big.matrix(discriptorfile).

This function is a simple wrapper of bigmemory::read.big.matrix(). See bigmemory for more details.

Value

A big.matrix object corresponding to a file-backed bigmemory::big.matrix(). It's ready to be used as the design matrix X in biglasso() and cv.biglasso().

Author(s)

Yaohui Zeng and Patrick Breheny

See Also

biglasso(), cv.ncvreg(), biglasso-package

Examples

## see the example in "biglasso-package"

Summarizing inferences based on cross-validation

Description

Summary method for cv.biglasso objects.

Usage

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

## S3 method for class 'summary.cv.biglasso'
print(x, digits, ...)

Arguments

object

A cv.biglasso object.
...

Further arguments passed to or from other methods.
x

A "summary.cv.biglasso" object.
digits

Number of digits past the decimal point to print out. Can be a vector specifying different display digits for each of the five non-integer printed values.

Value

summary.cv.biglasso produces an object with S3 class "summary.cv.biglasso". The class has its own print method and contains the following list elements:

penalty

The penalty used by biglasso.
model

Either "linear" or "logistic", depending on the family option in biglasso.
n

Number of observations
p

Number of regression coefficients (not including the intercept).
min

The index of lambda with the smallest cross-validation error.
lambda

The sequence of lambda values used by cv.biglasso.
cve

Cross-validation error (deviance).
r.squared

Proportion of variance explained by the model, as estimated by cross-validation.
snr

Signal to noise ratio, as estimated by cross-validation.
sigma

For linear regression models, the scale parameter estimate.
pe

For logistic regression models, the prediction error (misclassification error).

Author(s)

Yaohui Zeng and Patrick Breheny

See Also

biglasso(), cv.biglasso(), plot.cv.biglasso(), biglasso-package

Examples

## See examples in "cv.biglasso" and "biglasso-package"