DALSM.object {DALSM} | R Documentation |
An object returned by the DALSM
function: this is a list
with various components related to the fit of a double additive location-scale model using Laplace P-splines.
A DALSM
object has the following elements:
Essential part:
converged
:
logical convergence indicator.
derr
:
estimated standardized error distribution returned as a densLPS.object.
psi1
:
estimated regression parameters for location (fixed effects, B-spline coefs for the J1 additive terms).
psi2
:
estimated regression parameters for dispersion (fixed effects, B-spline coefs for the J2 additive terms).
fixed.loc
:
matrix with the estimated fixed effects (est,se,ci.low,ci.up) in the location sub-model.
fixed.disp
:
matrix with the estimated fixed effects (est,se,ci.low,ci.up) in the dispersion sub-model.
mu
:
n-vector with the fitted conditional mean.
sd
:
n-vector with the fitted conditional standard deviation.
Additional elements:
data
:
the original data frame used when calling the DALSM
function.
phi
:
estimated B-spline coefs for the log-hazard of the error distribution.
K.error
:
number of B-splines used to approximate the log of the error hazard.
rmin, rmax
:
minimum and maximum values for the support of the standardized error distribution.
knots.error
:
equidistant knots on (rmin,rmax) used to specify the B-spline basis for the log of the error hazard.
bread.psi1, Sand.psi1, Cov.psi1
:
estimated Variance-Covariance matrix for \psi_1
.
U.psi1
:
gradient for \psi_1
.
bread.psi2, Sand.psi2, Cov.psi2
:
estimated Variance-Covariance matrix for \psi_2
.
U.psi2
:
gradient for \psi_2
.
U.psi
:
gradient for \psi=(\psi_1,\psi_2)
.
Cov.psi
:
variance-covariance for \psi=(\psi_1,\psi_2)
.
regr1
:
object generated by DesignFormula
for the specified submodel for location.
regr2
:
object generated by DesignFormula
for the specified submodel for dispersion.
res
:
n-vector or nx2 matrix (if IC data) with the standardized residuals for the fitted model.
expctd.res
:
n-vector with observed standardized residual for a non RC unit, or their expected value if right-censored.
REML
:
logical indicating whether REML estimation was performed.
n
:
the sample size.
n.uncensored
:
number of non-censored response data.
event
:
n-vector of event indicators (1: non right-censored ; 0: right censoring).
is.IC
:
n-vector with interval censoring indicators.
n.IC
:
number of interval-censored response data.
n.RC
:
number of right-censored response data.
perc.obs
:
percentage of exactly observed response data.
perc.IC
:
percentage of interval-censored response data.
perc.RC
:
percentage of right-censored response data.
cred.int
:
nominal level for the reported credible intervals.
alpha
:
user-specified \alpha
with Bayesian (1-\alpha)
credible intervals reported.
sandwich
:
logical indicating if variance-covariance and standard errors computed using sandwich estimator in the NP case.
diag.only
:
logical indicating if the correction to the Hessian under REML only concerns diagonal elements.
iter
:
number of iterations.
elapsed.time
:
time required by the model fitting procedure.
If there are additive terms in the location submodel:
K1
:
number of B-splines used to describe an additive term in the location submodel.
xi1
:
matrix with the selected log penalty parameters for the J1 additive terms in the location submodel (point estimate, se, ci.low, ci.up.
U.xi1
:
gradient for the log of the penalty parameters for the J1 additive terms in the location submodel.
U.lambda1
:
gradient for the penalty parameters for the J1 additive terms in the location submodel.
Cov.xi1
:
estimated Variance-Covariance matrix for the parameters involved in the J1 additive terms in the location submodel.
lambda1.min
:
minimal value for the penalty parameters in the additive submodel for location.
lambda1
:
matrix with the selected penalty parameters for the J1 additive terms in the location submodel (point estimate, se, ci.low, ci.up).
ED1
:
matrix with the effective dimensions for each of the J1 additive terms in the location submodel (point estimate,ci.low,ci.up).
If there are additive terms in the dispersion submodel:
K2
:
number of B-splines used to describe an additive term in the dispersion submodel.
xi2
:
matrix with the selected log penalty parameters for the J2 additive terms in the dispersion submodel (point estimate, se, ci.low, ci.up).
U.xi2
:
gradient for the log of the penalty parameters for the J2 additive terms in the dispersion submodel.
U.lambda2
:
gradient for the penalty parameters for the J2 additive terms in the dispersion submodel.
Cov.xi2
:
estimated Variance-Covariance matrix for the parameters involved in the J2 additive terms in the dispersion submodel.
lambda2.min
:
minimal value for the penalty parameters in the additive submodel for dispersion.
lambda2
:
matrix with the selected penalty parameters for the J2 additive terms in the dispersion submodel (point estimate, se, ci.low, ci.up).
ED2
:
matrix with the effective dimensions for each of the J2 additive terms in the dispersion submodel (point estimate,ci.low,ci.up).
Philippe Lambert p.lambert@uliege.be
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>
DALSM
, print.DALSM
, plot.DALSM
, densityLPS
, densLPS.object