Stochastic Approximation Expectation Maximization (SAEM) Algorithm
Backward procedure for joint selection of covariates and random effect...
Bootstrap datasets
Check initial fixed effects for an SaemixModel object applied to an Sa...
Extract coefficients from an saemix fit
Model comparison with information criteria (AIC, BIC).
Estimate conditional mean and variance of individual parameters using ...
Evolution of the weight of 560 cows, in SAEM format
Create saemix objects with only data filled in
Wrapper functions to produce certain sets of default plots
VPC for non Gaussian data models
VPC for time-to-event models
Epilepsy count data
Get/set methods for SaemixData object
Computes the Fisher Information Matrix by linearisation
Extract Model Predictions
Backward procedure for joint selection of covariates and random effect...
Methods for Function initialize
Knee pain data
Log-likelihood using Gaussian Quadrature
Log-likelihood using Importance Sampling
Extract likelihood from an SaemixObject resulting from a call to saemi...
NCCTG Lung Cancer Data, in SAEM format
Estimates of the individual parameters (conditional mode)
Matrix diagonal
Create an npdeObject from an saemixObject
Heights of Boys in Oxford
Data simulated according to an Emax response model, in SAEM format
Methods for Function plot
Plot of longitudinal data
Plot model predictions for a new dataset. If the dataset is large, onl...
Plot model predictions using an SaemixModel object
General plot function from SAEM
Plot non Gaussian data
Methods for Function predict
Predictions for a new dataset
Methods for Function print
Functions to extract the individual estimates of the parameters and ra...
Rutgers Alcohol Problem Index
Create a longitudinal data structure from a file or a dataframeHelper ...
Methods for Function read
Replace the data element in an SaemixObject object
Extract Model Residuals
Bootstrap for saemix fits
Internal saemix objects
Functions implementing each type of plot in SAEM
Plots of the results obtained by SAEM
Function setting the default options for the plots in SAEM
Compute model predictions after an saemix fit
Stochastic Approximation Expectation Maximization (SAEM) algorithm
List of options for running the algorithm SAEM
Class "SaemixData"
Function to create an SaemixData object
Class "SaemixModel"
Function to create an SaemixModel object
Class "SaemixObject"
Predictions for a new dataset
Class "SaemixRes"
Methods for Function show
Methods for Function showall
Perform simulations under the model for an saemixObject object
Perform simulations under the model for an saemixObject object defined...
Stepwise procedure for joint selection of covariates and random effect...
Stepwise procedure for joint selection of covariates and random effect...
Get/set methods for SaemixModel object
Get/set methods for SaemixObject object
Get/set methods for SaemixRes object
Data subsetting
Methods for Function summary
Tests for normalised prediction distribution errors
Pharmacokinetics of theophylline
Toenail data
Transform covariates
Transform covariates
Transform covariates
Validate the structure of the covariance model
Name validation (## )Helper function not intended to be called by the ...
Extracts the Variance-Covariance Matrix for a Fitted Model Object
Internal functions used to produce prediction intervals (from the npde...
Wheat yield in crops treated with fertiliser, in SAEM format
The 'saemix' package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. The SAEM algorithm (i) computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, (ii) provides standard errors for the maximum likelihood estimator (iii) estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm (see Comets et al. (2017) <doi:10.18637/jss.v080.i03>). Many applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. The full PDF documentation for the package including references about the algorithm and examples can be downloaded on the github of the IAME research institute for 'saemix': <https://github.com/iame-researchCenter/saemix/blob/7638e1b09ccb01cdff173068e01c266e906f76eb/docsaem.pdf>.