spirometry

Data consists of 9471 longitudinal evaluations of forced 1-second
 expiratory volume (FEV1-percentage of predicted) after lung
 transplant from 509 patients who underwent lung transplant (LTx) at
 the Cleveland Clinic. Twenty three patient/procedure variables were
 collected at the time of the transplant. The major objectives are
 to evaluate the temporal trend of FEV1 after LTx, and to identify
 factors associated with post-LTx FEV1 and assessing the differences
 in the trends after Single LTx versus Double LTx.

datasets

Implements Friedman's gradient descent boosting algorithm for modeling longitudinal response using multivariate tree base learners. Longitudinal response could be continuous, binary, nominal or ordinal. A time-covariate interaction effect is modeled using penalized B-splines (P-splines) with estimated adaptive smoothing parameter. Although the package is design for longitudinal data, it can handle cross-sectional data as well. Implementation details are provided in Pande et al. (2017), Mach Learn <DOI:10.1007/s10994-016-5597-1>.

Udaya Kogalur

boostmtree

Boosted Multivariate Trees for Longitudinal Data

spirometry function

A list containing four elements:<ol>
<li>The 23 patient variables (features).</li>
<li>Time points (time).</li>
<li>Unique patient identifier (id).</li>
<li>FEV1-outcomes (y).</li>
</ol>

spirometry: Spirometry Data

Description

Arguments

Format

References

Examples