mse_cv

Calculate MSE for Bandwidth Selection using Cross-Validation

internal

Nonparametric methods for landmark prediction of long-term survival outcomes, incorporating covariate and short-term event information. The package supports the construction of flexible varying-coefficient models that use discrete covariates, as well as multiple continuous covariates.
The goal is to improve prediction accuracy when censored short-term events are available as predictors, using robust nonparametric procedures that do not require correct model specification and avoid restrictive parametric assumptions found in alternative methods.
More information on these methods can be found in Parast et al. 2012 <doi:10.1080/01621459.2012.721281>,
Parast et al. 2011 <doi:10.1002/bimj.201000150>, and Parast and Cai 2013 <doi:10.1002/sim.5776>.
A tutorial for this package is available here: <https://www.laylaparast.com/landpred>.

Layla Parast

landpred

Landmark Prediction of a Survival Outcome

Dylan Huynh

mse_cv function

<dl><dt>bw</dt>
<dd>The bandwidth to test.</dd>
<dt>landpred_obj</dt>
<dd>The landpred object.</dd>
<dt>t0</dt>
<dd>The landmark time.</dd>
<dt>tau</dt>
<dd>The prediction window.</dd>
<dt>transform</dt>
<dd>Transformation function for short-term event.</dd>
<dt>reps</dt>
<dd>Number of repetitions.</dd>
<dt>train_prop</dt>
<dd>Proportion of data to use for training.</dd></dl>

Arguments

Calculate MSE for Bandwidth Selection using Cross-Validation — mse_cv

<dl>

<dt>bw</dt>
<dd>The bandwidth to test.</dd>


<dt>landpred_obj</dt>
<dd>The landpred object.</dd>


<dt>t0</dt>
<dd>The landmark time.</dd>


<dt>tau</dt>
<dd>The prediction window.</dd>


<dt>transform</dt>
<dd>Transformation function for short-term event.</dd>


<dt>reps</dt>
<dd>Number of repetitions.</dd>


<dt>train_prop</dt>
<dd>Proportion of data to use for training.</dd>

</dl>

mse_cv: Calculate MSE for Bandwidth Selection using Cross-Validation

Description

Usage

Value

Arguments