SaemixRes-class: Class "SaemixRes"
In saemix: Stochastic Approximation Expectation Maximization (SAEM) Algorithm
SaemixRes-class R Documentation
Class "SaemixRes"
Description
An object of the SaemixRes class, representing the results of a fit through the SAEM algorithm.
Slots
modeltypestring giving the type of model used for analysis
statusstring indicating whether a model has been run successfully; set to "empty" at initialisation, used to pass on error messages or fit status
name.fixeda vector containing the names of the fixed parameters in the model
name.randoma vector containing the names of the random parameters in the model
name.sigmaa vector containing the names of the parameters of the residual error model
npar.estthe number of parameters estimated (fixed, random and residual)
nbeta.randomthe number of estimated fixed effects for the random parameters in the model
nbeta.fixedthe number of estimated fixed effects for the non random parameters in the model
fixed.effectsa vector giving the estimated h(mu) and betas
fixed.psia vector giving the estimated h(mu)
betasa vector giving the estimated mu
betaCa vector with the estimates of the fixed effects for covariates
omegathe estimated variance-covariance matrix
resparthe estimated parameters of the residual error model
fimthe Fisher information matrix
se.fixeda vector giving the estimated standard errors of estimation for the fixed effect parameters
se.omegaa vector giving the estimated standard errors of estimation for Omega
se.cova 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.respara vector giving the estimated standard errors of estimation for the parameters of the residual variability
conf.inta 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
parpopa matrix tracking the estimates of the population parameters at each iteration
allpara matrix tracking the estimates of all the parameters (including covariate effects) at each iteration
indx.fixthe index of the fixed parameters (used in the estimation algorithm)
indx.covthe index of the covariance parameters (used in the estimation algorithm)
indx.omegathe index of the random effect parameters (used in the estimation algorithm)
indx.resthe index of the residual error model parameters (used in the estimation algorithm)
MCOVa matrix of covariates (used in the estimation algorithm)
cond.mean.phia matrix giving the conditional mean estimates of phi (estimated as the mean of the conditional distribution)
cond.mean.psia matrix giving the conditional mean estimates of psi (h(cond.mean.phi))
cond.var.phia matrix giving the variance on the conditional mean estimates of phi (estimated as the variance of the conditional distribution)
cond.mean.etaa matrix giving the conditional mean estimates of the random effect eta
cond.shrinkagea vector giving the shrinkage on the conditional mean estimates of eta
mean.phia matrix giving the population estimate (Ci*mu) including covariate effects, for each subject
map.psia dataframe giving the MAP estimates of individual parameters
map.phia dataframe giving the MAP estimates of individual phi
map.etaa matrix giving the individual estimates of the random effects corresponding to the MAP estimates
map.shrinkagea vector giving the shrinkage on the MAP estimates of eta
phiindividual 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.sampa three-dimensional array with samples of psi from the conditional distribution
phi.sampa three-dimensional array with samples of phi from the conditional distribution
phi.samp.vara three-dimensional array with the variance of phi
ll.linlog-likelihood computed by lineariation
aic.linAkaike Information Criterion computed by linearisation
bic.linBayesian Information Criterion computed by linearisation
bic.covariate.linSpecific Bayesian Information Criterion for covariate selection computed by linearisation
ll.islog-likelihood computed by Importance Sampling
aic.isAkaike Information Criterion computed by Importance Sampling
bic.isBayesian Information Criterion computed by Importance Sampling
bic.covariate.isSpecific Bayesian Information Criterion for covariate selection computed by Importance Sampling
LLa vector giving the conditional log-likelihood at each iteration of the algorithm
ll.gqlog-likelihood computed by Gaussian Quadrature
aic.gqAkaike Information Criterion computed by Gaussian Quadrature
bic.gqBayesian Information Criterion computed by Gaussian Quadrature
bic.covariate.gqSpecific Bayesian Information Criterion for covariate selection computed by Gaussian Quadrature
predictionsa data frame containing all the predictions and residuals in a table format
ppreda vector giving the population predictions obtained with the population estimates
ypreda vector giving the mean population predictions
ipreda vector giving the individual predictions obtained with the MAP estimates
icpreda vector giving the individual predictions obtained with the conditional estimates
iresa vector giving the individual residuals obtained with the MAP estimates
iwresa vector giving the individual weighted residuals obtained with the MAP estimates
icwresa vector giving the individual weighted residuals obtained with the conditional estimates
wresa vector giving the population weighted residuals
npdea vector giving the normalised prediction distribution errors
pda 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 Aug. 8, 2025, 7:47 p.m.
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| SaemixRes-class | R Documentation |
Class "SaemixRes"
Description
An object of the SaemixRes class, representing the results of a fit through the SAEM algorithm.
Slots
modeltypestring giving the type of model used for analysis
statusstring indicating whether a model has been run successfully; set to "empty" at initialisation, used to pass on error messages or fit status
name.fixeda vector containing the names of the fixed parameters in the model
name.randoma vector containing the names of the random parameters in the model
name.sigmaa vector containing the names of the parameters of the residual error model
npar.estthe number of parameters estimated (fixed, random and residual)
nbeta.randomthe number of estimated fixed effects for the random parameters in the model
nbeta.fixedthe number of estimated fixed effects for the non random parameters in the model
fixed.effectsa vector giving the estimated h(mu) and betas
fixed.psia vector giving the estimated h(mu)
betasa vector giving the estimated mu
betaCa vector with the estimates of the fixed effects for covariates
omegathe estimated variance-covariance matrix
resparthe estimated parameters of the residual error model
fimthe Fisher information matrix
se.fixeda vector giving the estimated standard errors of estimation for the fixed effect parameters
se.omegaa vector giving the estimated standard errors of estimation for Omega
se.cova 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.respara vector giving the estimated standard errors of estimation for the parameters of the residual variability
conf.inta 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
parpopa matrix tracking the estimates of the population parameters at each iteration
allpara matrix tracking the estimates of all the parameters (including covariate effects) at each iteration
indx.fixthe index of the fixed parameters (used in the estimation algorithm)
indx.covthe index of the covariance parameters (used in the estimation algorithm)
indx.omegathe index of the random effect parameters (used in the estimation algorithm)
indx.resthe index of the residual error model parameters (used in the estimation algorithm)
MCOVa matrix of covariates (used in the estimation algorithm)
cond.mean.phia matrix giving the conditional mean estimates of phi (estimated as the mean of the conditional distribution)
cond.mean.psia matrix giving the conditional mean estimates of psi (h(cond.mean.phi))
cond.var.phia matrix giving the variance on the conditional mean estimates of phi (estimated as the variance of the conditional distribution)
cond.mean.etaa matrix giving the conditional mean estimates of the random effect eta
cond.shrinkagea vector giving the shrinkage on the conditional mean estimates of eta
mean.phia matrix giving the population estimate (Ci*mu) including covariate effects, for each subject
map.psia dataframe giving the MAP estimates of individual parameters
map.phia dataframe giving the MAP estimates of individual phi
map.etaa matrix giving the individual estimates of the random effects corresponding to the MAP estimates
map.shrinkagea vector giving the shrinkage on the MAP estimates of eta
phiindividual 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.sampa three-dimensional array with samples of psi from the conditional distribution
phi.sampa three-dimensional array with samples of phi from the conditional distribution
phi.samp.vara three-dimensional array with the variance of phi
ll.linlog-likelihood computed by lineariation
aic.linAkaike Information Criterion computed by linearisation
bic.linBayesian Information Criterion computed by linearisation
bic.covariate.linSpecific Bayesian Information Criterion for covariate selection computed by linearisation
ll.islog-likelihood computed by Importance Sampling
aic.isAkaike Information Criterion computed by Importance Sampling
bic.isBayesian Information Criterion computed by Importance Sampling
bic.covariate.isSpecific Bayesian Information Criterion for covariate selection computed by Importance Sampling
LLa vector giving the conditional log-likelihood at each iteration of the algorithm
ll.gqlog-likelihood computed by Gaussian Quadrature
aic.gqAkaike Information Criterion computed by Gaussian Quadrature
bic.gqBayesian Information Criterion computed by Gaussian Quadrature
bic.covariate.gqSpecific Bayesian Information Criterion for covariate selection computed by Gaussian Quadrature
predictionsa data frame containing all the predictions and residuals in a table format
ppreda vector giving the population predictions obtained with the population estimates
ypreda vector giving the mean population predictions
ipreda vector giving the individual predictions obtained with the MAP estimates
icpreda vector giving the individual predictions obtained with the conditional estimates
iresa vector giving the individual residuals obtained with the MAP estimates
iwresa vector giving the individual weighted residuals obtained with the MAP estimates
icwresa vector giving the individual weighted residuals obtained with the conditional estimates
wresa vector giving the population weighted residuals
npdea vector giving the normalised prediction distribution errors
pda 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 usedsignature(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")
R Package Documentation
Browse R Packages
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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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