Version: 1.5
Date: 2026-03-13
Title: Sure Independence Screening
Depends: R (≥ 3.2.4)
Imports: glmnet, ncvreg, survival, nnet, doParallel, gcdnet, msaenet, foreach, methods
LinkingTo: Rcpp, RcppEigen
Description: Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Through this publicly available package, we provide a unified environment to carry out variable selection using iterative sure independence screening (SIS) (Fan and Lv (2008)<doi:10.1111/j.1467-9868.2008.00674.x>) and all of its variants in generalized linear models (Fan and Song (2009)<doi:10.1214/10-AOS798>) and the Cox proportional hazards model (Fan, Feng and Wu (2010)<doi:10.1214/10-IMSCOLL606>).
License: GPL-2
RoxygenNote: 7.2.3
Encoding: UTF-8
Suggests: rmarkdown, knitr, formatR, pROC
VignetteBuilder: knitr
LazyData: true
NeedsCompilation: yes
Packaged: 2026-03-14 04:00:26 UTC; yangfeng
Author: Yang Feng [aut, cre], Jianqing Fan [aut], Diego Franco Saldana [aut], Yichao Wu [aut], Richard Samworth [aut], Arce Domingo Relloso [aut]
Maintainer: Yang Feng <yangfengstat@gmail.com>
Repository: CRAN
Date/Publication: 2026-03-14 06:10:36 UTC

(Iterative) Sure Independence Screening ((I)SIS) and Fitting in Generalized Linear Models and Cox's Proportional Hazards Models

Description

This function first implements the Iterative Sure Independence Screening for different variants of (I)SIS, and then fits the final regression model using the R packages ncvreg, glmnet, and msaenet plus an internal Cox adaptive elastic-net implementation for the SCAD/MCP/LASSO/ENET/AENET regularized loglikelihood for the variables picked by (I)SIS.

Usage

SIS(
  x,
  y,
  family = c("gaussian", "binomial", "poisson", "cox", "multinom"),
  penalty = c("SCAD", "MCP", "lasso", "enet", "aenet", "msaenet"),
  concavity.parameter = switch(penalty, SCAD = 3.7, 3),
  tune = c("bic", "ebic", "aic", "cv"),
  nfolds = 10,
  type.measure = c("deviance", "class", "auc", "mse", "mae"),
  gamma.ebic = 1,
  nsis = NULL,
  iter = TRUE,
  iter.max = ifelse(greedy == FALSE, 10, floor(nrow(x)/log(nrow(x)))),
  varISIS = c("vanilla", "aggr", "cons"),
  perm = FALSE,
  q = 1,
  greedy = FALSE,
  greedy.size = 1,
  seed = NULL,
  standardize = TRUE,
  covars = NULL,
  boot_ci = FALSE,
  parallel = TRUE
)

Arguments

x

The design matrix, of dimensions n * p, without an intercept. Each row is an observation vector. SIS standardizes the data and includes an intercept by default.

y

The response vector of dimension n * 1. Quantitative for family='gaussian', non-negative counts for family='poisson', binary (0-1) for family='binomial', factor for family='multinom'. For family='cox', y should be an object of class Surv, as provided by the function Surv() in the package survival.

family

Response type (see above).

penalty

The penalty to be applied in the regularized likelihood subproblems. 'SCAD', 'MCP', or 'lasso' are provided. 'lasso' is the default for family = 'multinom' or 'cox', 'SCAD' is the default for other families.

concavity.parameter

The tuning parameter used to adjust the concavity of the SCAD/MCP penalty. Default is 3.7 for SCAD and 3 for MCP.

tune

Method for tuning the regularization parameter of the penalized likelihood subproblems and of the final model selected by (I)SIS. Options include tune='bic', tune='ebic', tune='aic', and tune='cv'.

nfolds

Number of folds used in cross-validation. The default is 10.

type.measure

Loss to use for cross-validation. Currently five options, not all available for all models. The default is type.measure='deviance', which uses squared-error for gaussian models (also equivalent to type.measure='mse' in this case), deviance for logistic and poisson regression, and partial-likelihood for the Cox model. Both type.measure='class' and type.measure='auc' apply only to logistic regression and give misclassification error and area under the ROC curve, respectively. type.measure='mse' or type.measure='mae' (mean absolute error) can be used by all models except the 'cox'; they measure the deviation from the fitted mean to the response. For penalty='SCAD' and penalty='MCP', only type.measure='deviance' is available.

gamma.ebic

Specifies the parameter in the Extended BIC criterion penalizing the size of the corresponding model space. The default is gamma.ebic=1. See references at the end for details.

nsis

Number of pedictors recuited by (I)SIS.

iter

Specifies whether to perform iterative SIS. The default is iter=TRUE.

iter.max

Maximum number of iterations for (I)SIS and its variants.

varISIS

Specifies whether to perform any of the two ISIS variants based on randomly splitting the sample into two groups. The variant varISIS='aggr' is an aggressive variable screening procedure, while varISIS='cons' is a more conservative approach. The default is varISIS='vanilla', which performs the traditional vanilla version of ISIS. See references at the end for details.

perm

Specifies whether to impose a data-driven threshold in the size of the active sets calculated during the ISIS procedures. The threshold is calculated by first decoupling the predictors x_i and response y_i through a random permutation \pi of (1,...,n) to form a null model. For this newly permuted data, marginal regression coefficients for each predictor are recalculated. As the marginal regression coefficients of the original data should be larger than most recalculated coefficients in the null model, the data-driven threshold is given by the qth quantile of the null coefficients. This data-driven threshold only allows a 1-q proportion of inactive variables to enter the model when x_i and y_i are not related (in the null model). The default is here is perm=FALSE. See references at the end for details.

q

Quantile for calculating the data-driven threshold in the permutation-based ISIS. The default is q=1 (i.e., the maximum absolute value of the permuted estimates).

greedy

Specifies whether to run the greedy modification of the permutation-based ISIS. The default is greedy=FALSE.

greedy.size

Maximum size of the active sets in the greedy modification of the permutation-based ISIS. The default is greedy.size=1.

seed

Random seed used for sample splitting, random permutation, and cross-validation sampling of training and test sets.

standardize

Logical flag for x variable standardization, prior to performing (iterative) variable screening. The resulting coefficients are always returned on the original scale. Default is standardize=TRUE. If variables are in the same units already, you might not wish to standardize.

covars

Names of the factor variables.

boot_ci

Logical flag for computing bootstrap confidence intervals. Default = FALSE.

parallel

Specifies whether to conduct parallel computing

Value

A list with components:

sis.ix0

The vector of indices selected by only SIS.

ix

The vector of indices selected by (I)SIS with the regularization step.

coef.est

The vector of coefficients of the final model selected by (I)SIS.

fit

A fitted object of type ncvreg, cv.ncvreg, glmnet, or cv.glmnet for the final model selected by the (I)SIS procedure. If tune='cv', the returned fitted object is of type cv.ncvreg if penalty='SCAD' or penalty='MCP'; otherwise, the returned fitted object is of type cv.glmnet. For the remaining options of tune, the returned object is of type glmnet if penalty='lasso', and ncvreg otherwise.

path.index

The index along the solution path of fit for which the criterion specified in tune is minimized.

ix0

The vector of indices ordered by decreasing importance.

ix_list

The list of vectors of indices ordered by decreasing importance, for each screening step.

cis

A data frame with columns coef, CI_low, CI_up, Est, CI_low_perc, and CI_up_perc.

Author(s)

Jianqing Fan, Yang Feng, Diego Franco Saldana, Richard Samworth, Arce Domingo-Relloso and Yichao Wu

References

Diego Franco Saldana and Yang Feng (2018) SIS: An R package for Sure Independence Screening in Ultrahigh Dimensional Statistical Models, Journal of Statistical Software, 83, 2, 1-25.

Jianqing Fan and Jinchi Lv (2008) Sure Independence Screening for Ultrahigh Dimensional Feature Space (with discussion). Journal of Royal Statistical Society B, 70, 849-911.

Jianqing Fan and Rui Song (2010) Sure Independence Screening in Generalized Linear Models with NP-Dimensionality. The Annals of Statistics, 38, 3567-3604.

Jianqing Fan, Richard Samworth, and Yichao Wu (2009) Ultrahigh Dimensional Feature Selection: Beyond the Linear Model. Journal of Machine Learning Research, 10, 2013-2038.

Jianqing Fan, Yang Feng, and Yichao Wu (2010) High-dimensional Variable Selection for Cox Proportional Hazards Model. IMS Collections, 6, 70-86.

Jianqing Fan, Yang Feng, and Rui Song (2011) Nonparametric Independence Screening in Sparse Ultrahigh Dimensional Additive Models. Journal of the American Statistical Association, 106, 544-557.

Jiahua Chen and Zehua Chen (2008) Extended Bayesian Information Criteria for Model Selection with Large Model Spaces. Biometrika, 95, 759-771.

Domingo-Relloso, Arce, Yang Feng, Zulema Rodriguez-Hernandez, Karin Haack, Shelley A. Cole, Ana Navas-Acien, Maria Tellez-Plaza, and Jose D. Bermudez (2024) Omics feature selection with the extended SIS R package: identification of a body mass index epigenetic multimarker in the Strong Heart Study. American Journal of Epidemiology, 193, no. 7: 1010-1018.

See Also

predict.SIS

Examples



set.seed(0)
n <- 400
p <- 50
rho <- 0.5
corrmat <- diag(rep(1 - rho, p)) + matrix(rho, p, p)
corrmat[, 4] <- sqrt(rho)
corrmat[4, ] <- sqrt(rho)
corrmat[4, 4] <- 1
corrmat[, 5] <- 0
corrmat[5, ] <- 0
corrmat[5, 5] <- 1
cholmat <- chol(corrmat)
x <- matrix(rnorm(n * p, mean = 0, sd = 1), n, p)
x <- x %*% cholmat

# gaussian response
set.seed(1)
b <- c(4, 4, 4, -6 * sqrt(2), 4 / 3)
y <- x[, 1:5] %*% b + rnorm(n)


# SIS without regularization
model10 <- SIS(x, y, family = "gaussian", iter = FALSE)
model10$sis.ix0
# The top 10 selected variables
model10$ix0[1:10]
# The top 10 selected variables for each step
lapply(model10$ix_list, f <- function(x) {
  x[1:10]
})
# ISIS with regularization
model11 <- SIS(x, y, family = "gaussian", tune = "bic")
model12 <- SIS(x, y, family = "gaussian", tune = "bic", varISIS = "aggr", seed = 11)
model11$ix
model12$ix
## Not run: 
# binary response
set.seed(2)
feta <- x[, 1:5] %*% b
fprob <- exp(feta) / (1 + exp(feta))
y <- rbinom(n, 1, fprob)
model21 <- SIS(x, y, family = "binomial", tune = "bic")
model22 <- SIS(x, y, family = "binomial", tune = "bic", varISIS = "aggr", seed = 21)
model21$ix
model22$ix

# poisson response
set.seed(3)
b <- c(0.6, 0.6, 0.6, -0.9 * sqrt(2))
myrates <- exp(x[, 1:4] %*% b)
y <- rpois(n, myrates)
model31 <- SIS(x, y,
  family = "poisson", penalty = "lasso", tune = "bic", perm = TRUE, q = 0.9,
  greedy = TRUE, seed = 31
)
model32 <- SIS(x, y,
  family = "poisson", penalty = "lasso", tune = "bic", varISIS = "aggr",
  perm = TRUE, q = 0.9, seed = 32
)
model31$ix
model32$ix


# Cox model
set.seed(4)
b <- c(4, 4, 4, -6 * sqrt(2), 4 / 3)
myrates <- exp(x[, 1:5] %*% b)
Sur <- rexp(n, myrates)
CT <- rexp(n, 0.1)
Z <- pmin(Sur, CT)
ind <- as.numeric(Sur <= CT)
y <- survival::Surv(Z, ind)
model41 <- SIS(x, y,
  family = "cox", penalty = "lasso", tune = "bic",
  varISIS = "aggr", seed = 41
)
model42 <- SIS(x, y,
  family = "cox", penalty = "lasso", tune = "bic",
  varISIS = "cons", seed = 41
)
model41$ix
model42$ix

# SIS with bootstrap confidence intervals
sis <- SIS(x, y, family = "cox", penalty='aenet', tune='cv', varISIS='cons',
seed = 41, boot_ci=FALSE)
sis$cis

## End(Not run)

Internal package globals

Description

Internal package globals

Calculates confidence intervals by using the quantile bootstrap approach.

Description

The algorithm first conducts 10 repetitions of a 200 iterations bootstrap. Then, it checks whether the standard error of the mean of those 10 estimations for both upper and lower confidence intervals is lower than the 5 the bootstrap sample is adequate and we use the constructed sample to calculate upper and lower confidence intervals. If the condition is not met for more than 5 then the variability is too high and we need to increase the number of bootstrap repetitions. Thus, the process of testing 200 bootstrap samples 10 times is repeated and added to the previous bootstrap estimates. This process is repeated until the condition is met for more than 95

Usage

boot_sis(
  x,
  y,
  family,
  penalty,
  sig = 0.05,
  covars,
  probs = c(0.1, 0.9),
  parallel = TRUE
)

Arguments

x

The design matrix, of dimensions n * p, without an intercept. Each row is an observation vector.

y

The response vector of dimension n * 1. Quantitative for family='gaussian', non-negative counts for family='poisson', binary (0-1) for family='binomial'. For family='cox', y should be an object of class Surv, as provided by the function Surv() in the package survival.

family

Response type (see above).

penalty

The penalty to be applied in the regularized likelihood subproblems. 'SCAD' (the default), 'MCP', 'lasso', 'enet' (elastic-net), 'msaenet' (multi-step adaptive elastic-net) and 'aenet' (adaptive elastic-net) are provided.

sig

significance threshold for the confidence interval

covars

factor covariate names

probs

Quantiles to compare for the effect estimation. By default quantiles are 10th and 90th

parallel

Specifies whether to conduct parallel computing. If TRUE, it uses the parameter parallel from the glmnet package to parallelize the computation

Value

A data frame with columns coef, CI_low, CI_up, Est, CI_low_perc, and CI_up_perc.

Author(s)

Jianqing Fan, Yang Feng, Diego Franco Saldana, Richard Samworth, Arce Domingo-Relloso and Yichao Wu

References

Jerome Friedman and Trevor Hastie and Rob Tibshirani (2010) Regularization Paths for Generalized Linear Models Via Coordinate Descent. Journal of Statistical Software, 33(1), 1-22.

Noah Simon and Jerome Friedman and Trevor Hastie and Rob Tibshirani (2011) Regularization Paths for Cox's Proportional Hazards Model Via Coordinate Descent. Journal of Statistical Software, 39(5), 1-13.

Patrick Breheny and Jian Huang (2011) Coordiante Descent Algorithms for Nonconvex Penalized Regression, with Applications to Biological Feature Selection. The Annals of Applied Statistics, 5, 232-253.

Hirotogu Akaike (1973) Information Theory and an Extension of the Maximum Likelihood Principle. In Proceedings of the 2nd International Symposium on Information Theory, BN Petrov and F Csaki (eds.), 267-281.

Gideon Schwarz (1978) Estimating the Dimension of a Model. The Annals of Statistics, 6, 461-464.

Jiahua Chen and Zehua Chen (2008) Extended Bayesian Information Criteria for Model Selection with Large Model Spaces. Biometrika, 95, 759-771.

Gene expression Leukemia test data set from Golub et al. (1999)

Description

Gene expression test data of 7129 genes from 34 patients with acute leukemias (20 in class Acute Lymphoblastic Leukemia and 14 in class Acute Myeloid Leukemia) from the microarray study of Golub et al. (1999).

Usage

leukemia.test

Format

## 'leukemia.test' A data frame with 34 rows and 7130 columns:

...

V7130

class label

Source

<http://wwwprod.broadinstitute.org/cgi-bin/cancer/datasets.cgi>

Gene expression Leukemia training data set from Golub et al. (1999)

Description

Gene expression training data of 7129 genes from 38 patients with acute leukemias (27 in class Acute Lymphoblastic Leukemia and 11 in class Acute Myeloid Leukemia) from the microarray study of Golub et al. (1999).

Usage

leukemia.train

Format

## 'leukemia.train' A data frame with 38 rows and 7130 columns:

...

V7130

class label

Source

<http://wwwprod.broadinstitute.org/cgi-bin/cancer/datasets.cgi>

Model prediction based on a fitted SIS object.

Description

Similar to the usual predict methods, this function returns predictions from a fitted 'SIS' object.

Usage

## S3 method for class 'SIS'
predict(
  object,
  newx,
  lambda = object$lambda,
  which = NULL,
  type = c("response", "link", "class"),
  ...
)

Arguments

object

Fitted 'SIS' model object.

newx

Matrix of new values for x at which predictions are to be made, without the intercept term.

lambda

Penalty parameter lambda of the final fitted model by (I)SIS at which predictions are required. By default, only the lambda minimizing the criterion tune is returned.

which

Indices of the penalty parameter lambda of the final fitted model by (I)SIS at which predictions are required. If supplied, will overwrite the default lambda value.

type

Type of prediction required. Type 'response' gives the fitted values for 'gaussian', fitted probabilities for 'binomial', fitted mean for 'poisson', and the fitted relative risk for 'cox'. Type 'link' returns the linear predictors for 'binomial', 'poisson' and 'cox' models; for 'gaussian' models it is equivalent to type 'response'. Type 'class' applies only to 'binomial' models, and produces the class label corresponding to the maximum probability (0-1 labels).

...

Not used. Other arguments to predict.

Value

The object returned depends on type.

Author(s)

Jianqing Fan, Yang Feng, Diego Franco Saldana, Richard Samworth, and Yichao Wu

References

Diego Franco Saldana and Yang Feng (2018) SIS: An R package for Sure Independence Screening in Ultrahigh Dimensional Statistical Models, Journal of Statistical Software, 83, 2, 1-25.

Jianqing Fan and Jinchi Lv (2008) Sure Independence Screening for Ultrahigh Dimensional Feature Space (with discussion). Journal of Royal Statistical Society B, 70, 849-911.

Jianqing Fan and Rui Song (2010) Sure Independence Screening in Generalized Linear Models with NP-Dimensionality. The Annals of Statistics, 38, 3567-3604.

Jianqing Fan, Richard Samworth, and Yichao Wu (2009) Ultrahigh Dimensional Feature Selection: Beyond the Linear Model. Journal of Machine Learning Research, 10, 2013-2038.

Jianqing Fan, Yang Feng, and Yichao Wu (2010) High-dimensional Variable Selection for Cox Proportional Hazards Model. IMS Collections, 6, 70-86.

Jianqing Fan, Yang Feng, and Rui Song (2011) Nonparametric Independence Screening in Sparse Ultrahigh Dimensional Additive Models. Journal of the American Statistical Association, 106, 544-557.

Diego Franco Saldana and Yang Feng (2014) SIS: An R package for Sure Independence Screening in Ultrahigh Dimensional Statistical Models, Journal of Statistical Software.

See Also

SIS

Examples



set.seed(0)
n <- 400
p <- 50
rho <- 0.5
corrmat <- diag(rep(1 - rho, p)) + matrix(rho, p, p)
corrmat[, 4] <- sqrt(rho)
corrmat[4, ] <- sqrt(rho)
corrmat[4, 4] <- 1
corrmat[, 5] <- 0
corrmat[5, ] <- 0
corrmat[5, 5] <- 1
cholmat <- chol(corrmat)
x <- matrix(rnorm(n * p, mean = 0, sd = 1), n, p)
x <- x %*% cholmat
colnames(x) <- unlist(lapply(seq(1:dim(x)[2]), function(y) paste0('V',y)))
testX <- matrix(rnorm(10 * p, mean = 0, sd = 1), nrow = 10, ncol = p)

# gaussian response
set.seed(1)
b <- c(4, 4, 4, -6 * sqrt(2), 4 / 3)
y <- x[, 1:5] %*% b + rnorm(n)
model1 <- SIS(x, y, family = "gaussian", tune = "bic", varISIS = "aggr", seed = 11)

predict(model1, testX, type = "response")
predict(model1, testX, which = 1:10, type = "response")
## Not run: 
# binary response
set.seed(2)
feta <- x[, 1:5] %*% b
fprob <- exp(feta) / (1 + exp(feta))
y <- rbinom(n, 1, fprob)
model2 <- SIS(x, y, family = "binomial", tune = "bic", varISIS = "aggr", seed = 21)

predict(model2, testX, type = "response")
predict(model2, testX, type = "link")
predict(model2, testX, type = "class")

predict(model2, testX, which = 1:10, type = "response")
predict(model2, testX, which = 1:10, type = "link")
predict(model2, testX, which = 1:10, type = "class")

# poisson response
set.seed(3)
b <- c(0.6, 0.6, 0.6, -0.9 * sqrt(2))
myrates <- exp(x[, 1:4] %*% b)
y <- rpois(n, myrates)
model3 <- SIS(x, y, 
family = "poisson", penalty = "lasso", tune = "bic", 
varISIS = "aggr", seed = 31)

predict(model3, testX, type = "response")
predict(model3, testX, type = "link")

## End(Not run)

Gene expression Prostate Cancer test data set from Singh et al. (2002)

Description

Gene expression training data of 12600 genes from 25 patients with prostate tumors and 9 normal specimens from the microarray study of Singh et al. (2002).

Usage

prostate.test

Format

## 'prostate.test' A data frame with 34 rows and 12601 columns:

...

V12601

class label

Source

<http://wwwprod.broadinstitute.org/cgi-bin/cancer/datasets.cgi>

Gene expression Prostate Cancer training data set from Singh et al. (2002)

Description

Gene expression training data of 12600 genes from 52 patients with prostate tumors and 50 normal specimens from the microarray study of Singh et al. (2002).

Usage

prostate.train

Format

## 'prostate.train' A data frame with 102 rows and 12601 columns:

...

V12601

class label

Source

<http://wwwprod.broadinstitute.org/cgi-bin/cancer/datasets.cgi>

Standardization of High-Dimensional Design Matrices

Description

Standardizes the columns of a high-dimensional design matrix to mean zero and unit Euclidean norm.

Usage

standardize(X)

Arguments

X

A design matrix to be standardized.

Details

Performs a location and scale transform to the columns of the original design matrix, so that the resulting design matrix with p-dimensional observations \{x_i : i=1,...,n\} of the form x_i=(x_{i1},x_{i2},...,x_{ip}) satisfies \sum_{i=1}^{n} x_{ij} = 0 and \sum_{i=1}^{n} x_{ij}^{2} = 1 for j=1,...,p.

Value

A design matrix with standardized predictors or columns.

Author(s)

Jianqing Fan, Yang Feng, Diego Franco Saldana, Richard Samworth, and Yichao Wu

References

Diego Franco Saldana and Yang Feng (2018) SIS: An R package for Sure Independence Screening in Ultrahigh Dimensional Statistical Models, Journal of Statistical Software, 83, 2, 1-25.

Examples

## Not run: 
set.seed(0)
n <- 400
p <- 50
rho <- 0.5
corrmat <- diag(rep(1 - rho, p)) + matrix(rho, p, p)
corrmat[, 4] <- sqrt(rho)
corrmat[4, ] <- sqrt(rho)
corrmat[4, 4] <- 1
corrmat[, 5] <- 0
corrmat[5, ] <- 0
corrmat[5, 5] <- 1
cholmat <- chol(corrmat)
x <- matrix(rnorm(n * p, mean = 15, sd = 9), n, p)
x <- x %*% cholmat

x.standard <- standardize(x)

## End(Not run)

Using the glmnet, ncvreg, msaenet, and gcdnet packages plus an internal Cox adaptive elastic-net implementation, fits a Generalized Linear Model or Cox Proportional Hazards Model using various methods for choosing the regularization parameter \lambda

Description

This function fits a generalized linear model or a Cox proportional hazards model via penalized maximum likelihood, with available penalties as indicated in the glmnet, ncvreg, msaenet, and gcdnet packages. Instead of providing the whole regularization solution path, the function returns the solution at a unique value of \lambda, the one optimizing the criterion specified in tune.

Usage

tune.fit(
  x,
  y,
  family = c("gaussian", "binomial", "poisson", "cox", "multinom"),
  penalty = c("SCAD", "MCP", "lasso", "aenet", "msaenet", "enet"),
  concavity.parameter = switch(penalty, SCAD = 3.7, 3),
  tune = c("cv", "aic", "bic", "ebic"),
  nfolds = 10,
  type.measure = c("deviance", "class", "auc", "mse", "mae"),
  gamma.ebic = 1,
  parallel = TRUE,
  seed = NULL
)

Arguments

x

The design matrix, of dimensions n * p, without an intercept. Each row is an observation vector.

y

The response vector of dimension n * 1. Quantitative for family='gaussian', non-negative counts for family='poisson', binary (0-1) for family='binomial'. For family='cox', y should be an object of class Surv, as provided by the function Surv() in the package survival.

family

Response type (see above).

penalty

The penalty to be applied in the regularized likelihood subproblems. 'SCAD' (the default), 'MCP', or 'lasso' are provided.

concavity.parameter

The tuning parameter used to adjust the concavity of the SCAD/MCP penalty. Default is 3.7 for SCAD and 3 for MCP.

tune

Method for selecting the regularization parameter along the solution path of the penalized likelihood problem. Options to provide a final model include tune='cv', tune='aic', tune='bic', and tune='ebic'. See references at the end for details.

nfolds

Number of folds used in cross-validation. The default is 10.

type.measure

Loss to use for cross-validation. Currently five options, not all available for all models. The default is type.measure='deviance', which uses squared-error for gaussian models (also equivalent to type.measure='mse' in this case), deviance for logistic and poisson regression, and partial-likelihood for the Cox model. Both type.measure='class' and type.measure='auc' apply only to logistic regression and give misclassification error and area under the ROC curve, respectively. type.measure='mse' or type.measure='mae' (mean absolute error) can be used by all models except the 'cox'; they measure the deviation from the fitted mean to the response. For penalty='SCAD', penalty='MCP', penalty='aenet' and penalty='msaenet', only type.measure='deviance' is available.

gamma.ebic

Specifies the parameter in the Extended BIC criterion penalizing the size of the corresponding model space. The default is gamma.ebic=1. See references at the end for details.

parallel

Specifies whether to conduct parallel computing

seed

An optimal argument for setting the seed to ensure reproducibility

Value

Returns an object with

ix

The vector of indices of the nonzero coefficients selected by the maximum penalized likelihood procedure with tune as the method for choosing the regularization parameter.

a0

The intercept of the final model selected by tune.

beta

The vector of coefficients of the final model selected by tune.

fit

The fitted penalized regression object.

lambda

The corresponding lambda in the final model.

Author(s)

Jianqing Fan, Yang Feng, Diego Franco Saldana, Richard Samworth, Arce Domingo-Relloso and Yichao Wu

References

Jerome Friedman and Trevor Hastie and Rob Tibshirani (2010) Regularization Paths for Generalized Linear Models Via Coordinate Descent. Journal of Statistical Software, 33(1), 1-22.

Noah Simon and Jerome Friedman and Trevor Hastie and Rob Tibshirani (2011) Regularization Paths for Cox's Proportional Hazards Model Via Coordinate Descent. Journal of Statistical Software, 39(5), 1-13.

Patrick Breheny and Jian Huang (2011) Coordiante Descent Algorithms for Nonconvex Penalized Regression, with Applications to Biological Feature Selection. The Annals of Applied Statistics, 5, 232-253.

Hirotogu Akaike (1973) Information Theory and an Extension of the Maximum Likelihood Principle. In Proceedings of the 2nd International Symposium on Information Theory, BN Petrov and F Csaki (eds.), 267-281.

Gideon Schwarz (1978) Estimating the Dimension of a Model. The Annals of Statistics, 6, 461-464.

Jiahua Chen and Zehua Chen (2008) Extended Bayesian Information Criteria for Model Selection with Large Model Spaces. Biometrika, 95, 759-771.

Examples



set.seed(0)
data("leukemia.train", package = "SIS")
y.train <- leukemia.train[, dim(leukemia.train)[2]]
x.train <- as.matrix(leukemia.train[, -dim(leukemia.train)[2]])
x.train <- standardize(x.train)
model <- tune.fit(x.train[, 1:3500], y.train, family = "binomial", tune = "bic")
model$ix
model$a0
model$beta