densLPS.object: Object generated by function 'densityLPS'
In DALSM: Nonparametric Double Additive Location-Scale Model (DALSM)
densLPS.object 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 \phi is compared during penalization.
BWB : Hessian for \phi without 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>
See Also
densityLPS, DALSM
DALSM documentation built on Oct. 2, 2023, 5:09 p.m.
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| densLPS.object | 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>
See Also
densityLPS, DALSM
R Package Documentation
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