SaemixRes-class: Class "SaemixRes"

SaemixRes-classR Documentation

Class "SaemixRes"

Description

An object of the SaemixRes class, representing the results of a fit through the SAEM algorithm.

Slots

modeltype

string giving the type of model used for analysis

status

string indicating whether a model has been run successfully; set to "empty" at initialisation, used to pass on error messages or fit status

name.fixed

a vector containing the names of the fixed parameters in the model

name.random

a vector containing the names of the random parameters in the model

name.sigma

a vector containing the names of the parameters of the residual error model

npar.est

the number of parameters estimated (fixed, random and residual)

nbeta.random

the number of estimated fixed effects for the random parameters in the model

nbeta.fixed

the number of estimated fixed effects for the non random parameters in the model

fixed.effects

a vector giving the estimated h(mu) and betas

fixed.psi

a vector giving the estimated h(mu)

betas

a vector giving the estimated mu

betaC

a vector with the estimates of the fixed effects for covariates

omega

the estimated variance-covariance matrix

respar

the estimated parameters of the residual error model

fim

the Fisher information matrix

se.fixed

a vector giving the estimated standard errors of estimation for the fixed effect parameters

se.omega

a vector giving the estimated standard errors of estimation for Omega

se.cov

a matrix giving the estimated SE for each term of the covariance matrix (diagonal elements represent the SE on the variances of the random effects and off-diagonal elements represent the SE on the covariance terms)

se.respar

a vector giving the estimated standard errors of estimation for the parameters of the residual variability

conf.int

a dataframe containing the estimated parameters, their estimation error (SE), coefficient of variation (CV), and the associated confidence intervals; the variabilities for the random effects are presented first as estimated (variances) then converted to standard deviations (SD), and the correlations are computed. For SD and correlations, the SE are estimated via the delta-method

parpop

a matrix tracking the estimates of the population parameters at each iteration

allpar

a matrix tracking the estimates of all the parameters (including covariate effects) at each iteration

indx.fix

the index of the fixed parameters (used in the estimation algorithm)

indx.cov

the index of the covariance parameters (used in the estimation algorithm)

indx.omega

the index of the random effect parameters (used in the estimation algorithm)

indx.res

the index of the residual error model parameters (used in the estimation algorithm)

MCOV

a matrix of covariates (used in the estimation algorithm)

cond.mean.phi

a matrix giving the conditional mean estimates of phi (estimated as the mean of the conditional distribution)

cond.mean.psi

a matrix giving the conditional mean estimates of psi (h(cond.mean.phi))

cond.var.phi

a matrix giving the variance on the conditional mean estimates of phi (estimated as the variance of the conditional distribution)

cond.mean.eta

a matrix giving the conditional mean estimates of the random effect eta

cond.shrinkage

a vector giving the shrinkage on the conditional mean estimates of eta

mean.phi

a matrix giving the population estimate (Ci*mu) including covariate effects, for each subject

map.psi

a dataframe giving the MAP estimates of individual parameters

map.phi

a dataframe giving the MAP estimates of individual phi

map.eta

a matrix giving the individual estimates of the random effects corresponding to the MAP estimates

map.shrinkage

a vector giving the shrinkage on the MAP estimates of eta

phi

individual parameters, estimated at the end of the estimation process as the average over the chains of the individual parameters sampled during the successive E-steps

psi.samp

a three-dimensional array with samples of psi from the conditional distribution

phi.samp

a three-dimensional array with samples of phi from the conditional distribution

phi.samp.var

a three-dimensional array with the variance of phi

ll.lin

log-likelihood computed by lineariation

aic.lin

Akaike Information Criterion computed by linearisation

bic.lin

Bayesian Information Criterion computed by linearisation

bic.covariate.lin

Specific Bayesian Information Criterion for covariate selection computed by linearisation

ll.is

log-likelihood computed by Importance Sampling

aic.is

Akaike Information Criterion computed by Importance Sampling

bic.is

Bayesian Information Criterion computed by Importance Sampling

bic.covariate.is

Specific Bayesian Information Criterion for covariate selection computed by Importance Sampling

LL

a vector giving the conditional log-likelihood at each iteration of the algorithm

ll.gq

log-likelihood computed by Gaussian Quadrature

aic.gq

Akaike Information Criterion computed by Gaussian Quadrature

bic.gq

Bayesian Information Criterion computed by Gaussian Quadrature

bic.covariate.gq

Specific Bayesian Information Criterion for covariate selection computed by Gaussian Quadrature

predictions

a data frame containing all the predictions and residuals in a table format

ppred

a vector giving the population predictions obtained with the population estimates

ypred

a vector giving the mean population predictions

ipred

a vector giving the individual predictions obtained with the MAP estimates

icpred

a vector giving the individual predictions obtained with the conditional estimates

ires

a vector giving the individual residuals obtained with the MAP estimates

iwres

a vector giving the individual weighted residuals obtained with the MAP estimates

icwres

a vector giving the individual weighted residuals obtained with the conditional estimates

wres

a vector giving the population weighted residuals

npde

a vector giving the normalised prediction distribution errors

pd

a vector giving the prediction discrepancies

Objects from the Class

An object of the SaemixData class can be created by using the function saemixData and contain the following slots:

Methods

[<-

signature(x = "SaemixRes"): replace elements of object

[

signature(x = "SaemixRes"): access elements of object

initialize

signature(.Object = "SaemixRes"): internal function to initialise object, not to be used

print

signature(x = "SaemixRes"): prints details about the object (more extensive than show)

read

signature(object = "SaemixRes"): internal function, not to be used

showall

signature(object = "SaemixRes"): shows all the elements in the object

show

signature(object = "SaemixRes"): prints details about the object

summary

signature(object = "SaemixRes"): summary of the results. Returns a list with a number of elements extracted from the results ().

Author(s)

Emmanuelle Comets emmanuelle.comets@inserm.fr

Audrey Lavenu

Marc Lavielle.

References

E Comets, A Lavenu, M Lavielle M (2017). Parameter estimation in nonlinear mixed effect models using saemix, an R implementation of the SAEM algorithm. Journal of Statistical Software, 80(3):1-41.

E Kuhn, M Lavielle (2005). Maximum likelihood estimation in nonlinear mixed effects models. Computational Statistics and Data Analysis, 49(4):1020-1038.

E Comets, A Lavenu, M Lavielle (2011). SAEMIX, an R version of the SAEM algorithm. 20th meeting of the Population Approach Group in Europe, Athens, Greece, Abstr 2173.

See Also

saemixData SaemixModel saemixControl saemix

Examples

methods(class="SaemixRes")

showClass("SaemixRes")


saemix documentation built on May 29, 2024, 10:43 a.m.