evaluate.results

Get statistics for a single set of predictions.

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

evaluate.results function

<dl><dt>y</dt>
<dd>data frame of the response variable.</dd>
<dt>predict.data</dt>
<dd>output of prediction function from kfold.cv.</dd>
<dt>censor.vec</dt>
<dd>data frame indicating censoring for survival data. For binary or continuous data, set censor.vec &lt;- NULL.</dd>
<dt>trt.vec</dt>
<dd>data frame indicating whether or not the patient was treated. For the pronostic case, set trt.vec &lt;- NULL.</dd>
<dt>trtref</dt>
<dd>treatment reference.</dd>
<dt>type</dt>
<dd>data type - "c" - continuous , "b" - binary, "s" - time to event - default = "c".</dd></dl>

Arguments

evaluate.results — evaluate.results

<dl>

<dt>y</dt>
<dd>data frame of the response variable.</dd>


<dt>predict.data</dt>
<dd>output of prediction function from kfold.cv.</dd>


<dt>censor.vec</dt>
<dd>data frame indicating censoring for survival data. For binary or continuous data, set censor.vec &lt;- NULL.</dd>


<dt>trt.vec</dt>
<dd>data frame indicating whether or not the patient was treated. For the pronostic case, set trt.vec &lt;- NULL.</dd>


<dt>trtref</dt>
<dd>treatment reference.</dd>


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

</dl>

evaluate.results: evaluate.results

Description

Usage

Value

Arguments

Details