GLAM_2d_covariates {TwoTimeScales} | R Documentation |
Fit the 2d GLAM with covariates
Description
GLAM_2d_covariates()
fits a GLAM for the hazard with two time
scales, with covariates.
Usage
GLAM_2d_covariates(
R,
Y,
Bu,
Bs,
Z,
Wprior = NULL,
P,
ridge = 0,
control_algorithm = list(maxiter = 20, conv_crit = 1e-05, verbose = FALSE)
)
Arguments
R |
A 3d-array of dimensions nu by ns by n containing exposure times. |
Y |
A 3d-array of dimensions nu by ns by n containing event indicators. |
Bu |
A matrix of B-splines for the |
Bs |
A matrix of B-splines for the |
Z |
(optional) A regression matrix of covariates values of dimensions n by p. |
Wprior |
An optional matrix of a-priori weights. |
P |
The penalty matrix of dimension cucs by cucs. |
ridge |
A ridge penalty parameter: default is 0. |
control_algorithm |
A list with optional values for the parameters of the iterative processes:
|
Value
A list with the following elements:
-
Alpha
The matrix of estimated P-splines coefficients of dimension cu by cs. -
Cov_alpha
The variance-covariance matrix of theAlpha
coefficients, of dimension cucs by cucs. -
beta
The vector of length p of estimated covariates coefficients. -
Cov_beta
The variance-covariance matrix of thebeta
coefficients, of dimension p by p. -
SE_beta
The vector of length p of estimated Standard Errors for thebeta
coefficients. -
Eta0
The matrix of values of the baseline linear predictor (log-hazard) of dimension nu by ns. -
H
The hat-matrix. -
deviance
The deviance. -
ed
The effective dimension of the model. -
aic
The value of the AIC. -
bic
The value of the BIC. -
Bbases
a list with the B-spline basesBu
andBs
.