cv.pval

p-value calculation for each iteration of cross validation.

Implementation of Sequential BATTing (bootstrapping and aggregating of thresholds from trees) for developing threshold-based multivariate (prognostic/predictive) biomarker signatures. Variable selection is automatically built-in. Final signatures are returned with interaction plots for predictive signatures. Cross-validation performance evaluation and testing dataset results are also output. Detail algorithms are described in Huang et al (2017) <doi:10.1002/sim.7236>.

Xin Huang

SubgrpID

Patient Subgroup Identification for Clinical Drug Development

Yan Sun

Saptarshi Chatterjee

Paul Trow

cv.pval function

<dl><dt>yvar</dt>
<dd>response variable name.</dd>
<dt>censorvar</dt>
<dd>censor-variable name.</dd>
<dt>trtvar</dt>
<dd>treatment variable name. For prognostic case trtvar=NULL.</dd>
<dt>data</dt>
<dd>dataset containing response and predicted output.</dd>
<dt>type</dt>
<dd>data type - "c" - continuous , "b" - binary, "s" - time to event - default = "c".</dd></dl>

Arguments

cv.pval — cv.pval

<dl>

<dt>yvar</dt>
<dd>response variable name.</dd>


<dt>censorvar</dt>
<dd>censor-variable name.</dd>


<dt>trtvar</dt>
<dd>treatment variable name. For prognostic case trtvar=NULL.</dd>


<dt>data</dt>
<dd>dataset containing response and predicted output.</dd>


<dt>type</dt>
<dd>data type - "c" - continuous , "b" - binary, "s" - time to event - default = "c".</dd>

</dl>

cv.pval: cv.pval

Description

Usage

Value

Arguments

Details