ordregr.object: Object resulting from the fit of a proportional odds model...
In ordgam: Additive Model for Ordinal Data using Laplace P-Splines
ordregr.object R Documentation
Object resulting from the fit of a proportional odds model using 'ordregr'
Description
An object returned by the ordregr function: this is a list
with various components related to the fit of such a model.
Value
A ordregr object is a list with following elements:
val : Value of the log-posterior at convergence.
val.start : Value of the log-posterior at the start of the Newton-Raphson (N-R) algorithm.
theta : (Penalized) MLE or MAP of the regression coefficients.
grad : Gradient of the log-posterior at theta.
Hessian : Hessian of the log-posterior at theta.
iter : Number of iterations of the N-R algorithm.
Hessian0 : Hessian of the (non-penalized) log-likelihood at theta.
Sigma.theta : Variance-covariance of 'theta'.
ED.full : Effective degrees of freedom associated to each regression parameter, penalized parameters included.
se.theta : Standard errors of the regression coefficents.
theta.mat : Matrix containing the point estimate, standard error, credible interval, Z-score and P-value for theta.
nc : Number of categories for the ordinal response.
nalpha : Number of intercepts in the proportional odds model (=nc-1) .
nbeta : Number of regression parameters (intercepts excluded).
nfixed : Number of non-penalized regression parameters.
ci.level : Nominal coverage of the credible intervals (Default: .95).
n : Sample size.
call : Function call.
descending : Logical indicating if the odds of the response taking a value in the upper scale should be preferred over values in the lower scale.
use.prior : Logical indicating if a prior (such as a penalty) is assumed for the regression parameters.
lpost : Value of the log-posterior at convergence.
levidence : Log of the marginal likelihood (also named 'evidence').
Author(s)
Philippe Lambert p.lambert@uliege.be
References
Lambert, P. and Gressani, 0. (2023) Penalty parameter selection and asymmetry corrections
to Laplace approximations in Bayesian P-splines models. Statistical Modelling. <doi:10.1177/1471082X231181173>. Preprint: <arXiv:2210.01668>.
See Also
ordregr, print.ordregr
ordgam documentation built on Sept. 14, 2023, 5:07 p.m.
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| ordregr.object | R Documentation |
Object resulting from the fit of a proportional odds model using 'ordregr'
Description
An object returned by the ordregr function: this is a list
with various components related to the fit of such a model.
Value
A ordregr object is a list with following elements:
val: Value of the log-posterior at convergence.val.start: Value of the log-posterior at the start of the Newton-Raphson (N-R) algorithm.theta: (Penalized) MLE or MAP of the regression coefficients.grad: Gradient of the log-posterior attheta.Hessian: Hessian of the log-posterior attheta.iter: Number of iterations of the N-R algorithm.Hessian0: Hessian of the (non-penalized) log-likelihood attheta.Sigma.theta: Variance-covariance of 'theta'.ED.full: Effective degrees of freedom associated to each regression parameter, penalized parameters included.se.theta: Standard errors of the regression coefficents.theta.mat: Matrix containing the point estimate, standard error, credible interval, Z-score and P-value fortheta.nc: Number of categories for the ordinal response.nalpha: Number of intercepts in the proportional odds model (=nc-1) .nbeta: Number of regression parameters (intercepts excluded).nfixed: Number of non-penalized regression parameters.ci.level: Nominal coverage of the credible intervals (Default: .95).n: Sample size.call: Function call.descending: Logical indicating if the odds of the response taking a value in the upper scale should be preferred over values in the lower scale.use.prior: Logical indicating if a prior (such as a penalty) is assumed for the regression parameters.lpost: Value of the log-posterior at convergence.levidence: Log of the marginal likelihood (also named 'evidence').
Author(s)
Philippe Lambert p.lambert@uliege.be
References
Lambert, P. and Gressani, 0. (2023) Penalty parameter selection and asymmetry corrections to Laplace approximations in Bayesian P-splines models. Statistical Modelling. <doi:10.1177/1471082X231181173>. Preprint: <arXiv:2210.01668>.
See Also
ordregr, print.ordregr
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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