A simulated demo dataset <code>sim</code>

datasets

Efficient procedures for fitting and cross-validating the overlapping group Lasso (implemented in C++) for Cox models with time-dependent covariates. The penalty term is a weighted sum of infinity norms of (overlapping) groups of coefficients, which can select variables structurally with a specific grouping structure.

Yi Lian

netcox

Structural Learning in Cox Models with Time-Dependent Covariates

Guanbo Wang

Archer Y. Yang

Julien Mairal

sim function

A simulated data frame that is used to illustrate the use of the netcox package. The max follow-up time for each subject is set to be 5. The total number of subject is 50.<dl>
 <dt>Id</dt>
<dd>The ID of each subject.</dd> <dt>Event</dt>
<dd>During the time from <code>Start</code> to <code>Stop</code>, if the subject experience the event. We use the function <code>permalgorithm</code> in the <code>R</code> package <code>PermAlgo</code> to generate the Event.</dd> <dt>Start</dt>
<dd>Start time.</dd> <dt>Stop</dt>
<dd>Stop time.</dd> <dt>Fup</dt>
<dd>The total follow-up time for the subject.</dd> <dt>Covariates</dt>
<dd>A1, A2, C1, C2, B, A1B, A2B, C1B, C2B. The dataset contains 5 variables (9 columns after one-hot encoding). Variable A is a e 3-level categorical variable, which results in 2 binary variables (A1 and A2), the same with the variable C. B is a continuous variable. The interaction term AB and CB are also two 3-level categorical variables. The code for generating the covariates is given below.</dd>
</dl>

Format

A simulated demo dataset sim — sim

A simulated data frame that is used to illustrate the use of the netcox package. The max follow-up time for each subject is set to be 5. The total number of subject is 50.<dl>
 <dt>Id</dt>
<dd>The ID of each subject.</dd>

 <dt>Event</dt>
<dd>During the time from <code>Start</code> to <code>Stop</code>, if the subject experience the event. We use the function <code>permalgorithm</code> in the <code>R</code> package <code>PermAlgo</code> to generate the Event.</dd>

 <dt>Start</dt>
<dd>Start time.</dd>

 <dt>Stop</dt>
<dd>Stop time.</dd>

 <dt>Fup</dt>
<dd>The total follow-up time for the subject.</dd>

 <dt>Covariates</dt>
<dd>A1, A2, C1, C2, B, A1B, A2B, C1B, C2B. The dataset contains 5 variables (9 columns after one-hot encoding). Variable A is a e 3-level categorical variable, which results in 2 binary variables (A1 and A2), the same with the variable C. B is a continuous variable. The interaction term AB and CB are also two 3-level categorical variables. The code for generating the covariates is given below.</dd>


</dl>

sim: A simulated demo dataset `sim`

Description

Usage

Arguments

Format

See Also

Examples