Lung

Data from a randomised trial of two treatment regimens for lung cancer. This
is a standard survival analysis data set from the classic textbook by
Kalbfleisch and Prentice.

datasets

Efficient algorithms for fitting the regularization path of linear
regression, GLM, and Cox regression models with grouped penalties. This
includes group selection methods such as group lasso, group MCP, and
group SCAD as well as bi-level selection methods such as the group
exponential lasso, the composite MCP, and the group bridge. For more
information, see Breheny and Huang (2009) <doi:10.4310/sii.2009.v2.n3.a10>,
Huang, Breheny, and Ma (2012) <doi:10.1214/12-sts392>, Breheny and Huang
(2015) <doi:10.1007/s11222-013-9424-2>, and Breheny (2015)
<doi:10.1111/biom.12300>, or visit the package homepage
<https://pbreheny.github.io/grpreg/>.

Patrick Breheny

grpreg

Regularization Paths for Regression Models with Grouped
Covariates

Yaohui Zeng

Ryan Kurth

Lung function

A list of two objects: <code>y</code> and <code>X</code><dl>
<dt>y</dt>
<dd>A two column matrix (<code>Surv</code> object) containing the follow-up
time (in days) and an indicator variable for whether the patient died
while on the study or not.</dd><dt>X</dt>
<dd>A matrix with 137 observations (rows) and 9 predictor variables
(columns). The remainder of this list describes the columns of <code>X</code></dd><dt>trt</dt>
<dd>Treatment indicator (1=control group, 2=treatment group)</dd><dt>karno</dt>
<dd>Karnofsky performance score (0=bad, 100=good)</dd><dt>diagtime</dt>
<dd>Time from diagnosis to randomization (months)</dd><dt>age</dt>
<dd>Age (years, at baseline)</dd><dt>prior</dt>
<dd>Prior therapy (0=no, 1=yes)</dd><dt>squamous</dt>
<dd>Indicator for whether the cancer type is squamous cell
carcinoma (0=no, 1=yes)</dd><dt>small</dt>
<dd>Indicator for whether the cancer type is small cell lung
cancer (0=no, 1=yes)</dd><dt>adeno</dt>
<dd>Indicator for whether the cancer type is adenocarcinoma
(0=no, 1=yes)</dd><dt>large</dt>
<dd>Indicator for whether the cancer type is large cell carcinoma
(0=no, 1=yes)</dd>
</dl>

Format

VA lung cancer data set — Lung

A list of two objects: <code>y</code> and <code>X</code><dl>
<dt>y</dt>
<dd>A two column matrix (<code>Surv</code> object) containing the follow-up
time (in days) and an indicator variable for whether the patient died
while on the study or not.</dd>

<dt>X</dt>
<dd>A matrix with 137 observations (rows) and 9 predictor variables
(columns). The remainder of this list describes the columns of <code>X</code></dd>

<dt>trt</dt>
<dd>Treatment indicator (1=control group, 2=treatment group)</dd>

<dt>karno</dt>
<dd>Karnofsky performance score (0=bad, 100=good)</dd>

<dt>diagtime</dt>
<dd>Time from diagnosis to randomization (months)</dd>

<dt>age</dt>
<dd>Age (years, at baseline)</dd>

<dt>prior</dt>
<dd>Prior therapy (0=no, 1=yes)</dd>

<dt>squamous</dt>
<dd>Indicator for whether the cancer type is squamous cell
carcinoma (0=no, 1=yes)</dd>

<dt>small</dt>
<dd>Indicator for whether the cancer type is small cell lung
cancer (0=no, 1=yes)</dd>

<dt>adeno</dt>
<dd>Indicator for whether the cancer type is adenocarcinoma
(0=no, 1=yes)</dd>

<dt>large</dt>
<dd>Indicator for whether the cancer type is large cell carcinoma
(0=no, 1=yes)</dd>


</dl>

Lung: VA lung cancer data set

Description

Usage

Arguments

Format

References

See Also