check.result: Check Structure of the 'AdPaikModel' Output
In TimeDepFrail: Time Dependent Shared Frailty Cox Model

check.result

R Documentation

Check Structure of the 'AdPaikModel' Output

Description

The function controls that the structure of the input variable is coherent with the one returned by the 'AdPaikModel' execution.

Usage

check.result(result)

Arguments

result

S3 object of class 'AdPaik', composed of several elements. See details.

Details

The output of the model call 'AdPaikModel(...)' is a S3 object of class 'AdPaik', composed of:

formula: formula object provided in input by the user and specifying the relationship between the time-to-event, the covariates of the dataset (regressors) and the cluster variable.
Regressors: categorical vector of length R, with the name of the regressors. They could be different from the original covariates of the dataset in case of categorical covariates. Indeed, each categorical covariate with n levels needs to be transformed into (n-1) dummy variables and, therefore, (n-1) regressors.
NRegressors: number of regressors (R)
ClusterVariable: name of the variable with respect to which the individuals can be grouped.
NClusters: number of clusters/groups/centres
ClusterCodes
TimeDomain
NIntervals: number of intervals of the time-domain, also called with L. It corresponds to the length of the time-domain minus 1.
NParameters: number of parameters of the model. It can be computed as: n_p = 2L + R + 2.
ParametersCategories: Numerical vector of length 5, containing the numerosity of each parameter category.
ParametersRangeMin: Numerical vector of length n_p, giving the minimum range of each parameter.
ParametersRangeMax: Numerical vector of length n_p, giving the maximum range of each parameter.
Loglikelihood: value of the maximized log-likelihood function, at the optimal estimated parameters.
AIC: 'Akaike Information Criterion': it can be computed as AIC = 2n_p - 2ll_{optimal}. It gives an idea of the loss of information related to the model fitting and output. The smaller it is, the less loss of information and the better model accuracy.
Status: Logical value. Does the model reach convergence? If so, the variable is TRUE, otherwise FALSE.
NRun: Number of runs necessary to reach convergence. If the model does not reach convergence, such number is equal to the maximum number of imposed runs.
OptimalParameters: numerical vector of length n_p, containing the optimal estimated parameters or, in other words, the parameters that maximizes the log-likelihood function.
StandardErrorParameters: numerical vector of length n_p, corresponding to the standard error of each estimated parameters.
ParametersCI: S3 object of class 'ParametersCI', composed of two numerical vector of length equal to n_p: the left and right confidence interval of each estimated parameter.
FrailtyStandardDeviation: numerical vector of length equal to L (i.e. number of intervals of the time-domain), reporting the standard deviation of the frailty.
PosteriorFrailtyEstimates: S3 object of class 'PFE.AdPaik'. See details.
PosteriorFrailtyVariance: S3 object of class 'PFV.AdPaik'. See details.

The object of class 'PFE.AdPaik' contains the Posterior Frailty Estimates computed with the procedure indicated in the reference paper and it is composed of three elements:

'alpha': posterior frailty estimates for \alpha_j, \forall j. It is a vector of length equal to the number of groups/centres.
'eps': posterior frailty estimates for \epsilon_{jk}, \forall j,k. Matrix of dimension (N, L).
'Z': posterior frailty estimates for Z_{jk} = \alpha_j + \epsilon_{jk}, \forall j,k. Matrix of dimension (N, L).

The object of class 'PFV.AdPaik' contains the Posterior Frailty Variances computed as indicated in the reference papaer and it is composed of three elements:

'alphaVar': posterior frailty variance for \alpha_j, \forall j. It is a vector of length equal to the number of groups/centres.
'epsVar': posterior frailty variance for \epsilon_{jk}, \forall j,k. Matrix of dimension (N, L).
'ZVar': posterior frailty variance for Z_{jk} = \alpha_j + \epsilon_{jk}, \forall j,k. Matrix of dimension (N, L).