| densLPS.object {DALSM} | R Documentation |
Object generated by function densityLPS
Description
An object returned by function densityLPS: this is a list
with various components related to the estimation of a density with given mean and variance from potentially right- or interval-censored data using Laplace P-splines.
Value
An object returned by densityLPS has the following elements:
Essential part:
converged: logical convergence indicator.ddist: fitted density function.Hdist: fitted cumulative hazard function.hdist: fitted hazard function.pdist: fitted cumulative distribution function.ymin, ymax: assumed values for the support of the distribution.phi: estimated B-spline coefficients for the log-hazard of the error distribution.U.phi: score of the Lagrangian G(\phi|\omega).tau,ltau: selected penalty parameter and its logarithm.est: vector containing the estimated/selected (\phi,\log\tau) parameters.fixed.phi: logical indicating whether the spline parameters were given fixed values or estimated from the data.phi.ref: reference values for the spline parameters with respect to which\phiis compared during penalization.BWB: Hessian for\phiwithout the penalty contribution.Prec: Hessian or posterior precision matrix for\phi.Fisher: Fisher information for\phi.bins, ugrid, du: bins (of width 'du') and with midpoints 'ugrid' partitioning the support of the density.h.grid, H.grid, dens.grid: hazard, cumulative hazard and density values at the grid midpoints 'ugrid'.h.bins, H.bins, dens.bins: hazard, cumulative hazard and density values at the bin limits 'bins'.expected: expected number of observations within each bin.Finfty: integrated density value over the considered support.Mean0, Var0: when specified, constrained mean and variance values during estimation.mean.dist, var.dist: mean and variance of the fitted density.method: method used for penaly selection: "evidence" (by maximizing the marginal posterior for\tau) or "Schall" (Schall's method).ed: effective number of (spline) parameters.iterations: total number of iterations necessary for convergence.elapsed.time: time required for convergence.
Additional elements: the content of the Dens1d.object used when densityLPS was called.
Author(s)
Philippe Lambert p.lambert@uliege.be
References
Lambert, P. (2021). Fast Bayesian inference using Laplace approximations in nonparametric double additive location-scale models with right- and interval-censored data. Computational Statistics and Data Analysis, 161: 107250. <doi:10.1016/j.csda.2021.107250>