| Title: | R Speaks 'Monolix' |
|---|---|
| Description: | Provides methods for model building and model evaluation of mixed effects models using 'Monolix' <https://monolix.lixoft.com>. 'Monolix' is a software tool for nonlinear mixed effects modeling that must have been installed in order to use 'Rsmlx'. Among other tasks, 'Rsmlx' provides a powerful tool for automatic PK model building, performs statistical tests for model assessment, bootstrap simulation and likelihood profiling for computing confidence intervals. 'Rsmlx' also proposes several automatic covariate search methods for mixed effects models. |
| Authors: | Chloe Bracis [ctb, cre], Frano Mihaljevic [aut], Marc Lavielle [aut], Jonathan Chauvin [ctb], Clémence Pinaud [ctb] |
| Maintainer: | Chloe Bracis <[email protected]> |
| License: | BSD_2_clause + file LICENSE |
| Version: | 2024.1.0 |
| Built: | 2025-02-05 04:41:11 UTC |
| Source: | https://github.com/marclavielle/rsmlx |
Generate replicates of the original data using random sampling with replacement. Population parameters are then estimated from each replicate.
bootmlx( project, nboot = 100, dataFolder = NULL, parametric = FALSE, tasks = c(populationParameterEstimation = TRUE), settings = NULL )bootmlx( project, nboot = 100, dataFolder = NULL, parametric = FALSE, tasks = c(populationParameterEstimation = TRUE), settings = NULL )
project |
Monolix project |
nboot |
[optional] number of bootstrap replicates (default=100) |
dataFolder |
[optional] folder where already generated datasets are stored, e.g dataFolder="./dummy_project/boot/" (default: data set are generated by bootmlx) |
parametric |
[optional] boolean to define if parametric bootstrap is performed (new data is drawn from the model), (default: false) |
tasks |
[optional] vector of booleans defining the list of tasks to perform (default: estimation of the population parameters) available tasks: populationParameterEstimation, conditionalDistributionSampling, conditionalModeEstimation, standardErrorEstimation, logLikelihoodEstimation, plots |
settings |
[optional] a list of settings for the resampling and the results:
|
Bootstrap functionality is now available directly in the lixoftConnectors package using the function
runBootstrap. Please migrate, as this function will be deprecated in the future.
a data frame with the bootstrap estimates
getBootstrapSettings settings for bootstrap with lixoftConnectors runBootstrap run the bootstrap with lixoftConnectors getBootstrapResults results for bootstrap with lixoftConnectors
## Not run: # RsmlxDemo1.mlxtran is a Monolix project for modelling the PK of warfarin using a PK model # with parameters ka, V, Cl. # In this example, bootmlx will generate 100 random replicates of the original data and will # use Monolix to estimate the population parameters from each of these 100 replicates: r1 <- bootmlx(project="RsmlxDemo1.mlxtran") # 5 replicates will now be generated, with 50 individuals in each replicate: r2 <- bootmlx(project="RsmlxDemo1.mlxtran", nboot = 5, settings = list(N = 50)) # Proportions of males and females in the original dataset will be preserved # in each replicate: r3 <- bootmlx(project="RsmlxDemo1.mlxtran", settings = list(covStrat = "sex")) ## End(Not run) # See http://monolix.lixoft.com/rsmlx/bootmlx/ for detailed examples of use of bootmlx # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation## Not run: # RsmlxDemo1.mlxtran is a Monolix project for modelling the PK of warfarin using a PK model # with parameters ka, V, Cl. # In this example, bootmlx will generate 100 random replicates of the original data and will # use Monolix to estimate the population parameters from each of these 100 replicates: r1 <- bootmlx(project="RsmlxDemo1.mlxtran") # 5 replicates will now be generated, with 50 individuals in each replicate: r2 <- bootmlx(project="RsmlxDemo1.mlxtran", nboot = 5, settings = list(N = 50)) # Proportions of males and females in the original dataset will be preserved # in each replicate: r3 <- bootmlx(project="RsmlxDemo1.mlxtran", settings = list(covStrat = "sex")) ## End(Not run) # See http://monolix.lixoft.com/rsmlx/bootmlx/ for detailed examples of use of bootmlx # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation
buildAll builds the complete statistical model by iteratively calling functions buildmlx and buildVar
Penalization criterion can be either a custom penalization of the form gamma*(number of parameters), AIC (gamma=2) or BIC (gamma=log(N)).
buildAll( project = NULL, final.project = NULL, model = "all", prior = NULL, weight = NULL, coef.w1 = 0.5, cv.min = 0.001, fError.min = 0.001, paramToUse = "all", covToTest = "all", covToTransform = "none", center.covariate = FALSE, criterion = "BICc", linearization = FALSE, ll = T, test = T, direction = NULL, steps = 1000, max.iter = 20, explor.iter = 2, seq.cov = FALSE, seq.corr = TRUE, seq.cov.iter = 0, p.max = 0.1, p.min = c(0.075, 0.05, 0.1), print = TRUE, nb.model = 1, fix.param1 = NULL, fix.param0 = NULL, remove = T, add = T, delta = c(30, 10, 5), omega.set = NULL, pop.set1 = NULL, pop.set2 = NULL )buildAll( project = NULL, final.project = NULL, model = "all", prior = NULL, weight = NULL, coef.w1 = 0.5, cv.min = 0.001, fError.min = 0.001, paramToUse = "all", covToTest = "all", covToTransform = "none", center.covariate = FALSE, criterion = "BICc", linearization = FALSE, ll = T, test = T, direction = NULL, steps = 1000, max.iter = 20, explor.iter = 2, seq.cov = FALSE, seq.corr = TRUE, seq.cov.iter = 0, p.max = 0.1, p.min = c(0.075, 0.05, 0.1), print = TRUE, nb.model = 1, fix.param1 = NULL, fix.param0 = NULL, remove = T, add = T, delta = c(30, 10, 5), omega.set = NULL, pop.set1 = NULL, pop.set2 = NULL )
project |
a string: the initial Monolix project |
final.project |
a string: the final Monolix project (default adds "_buildAll" to the original project) |
model |
components of the model to optimize c("residualError", "covariate", "correlation"), (default="all") |
prior |
list of prior probabilities for each component of the model (default=NULL) |
weight |
list of penalty weights for each component of the model (default=NULL) |
coef.w1 |
multiplicative weight coefficient used for the first iteration only (default=0.5) |
cv.min |
value of the coefficient of variation below which an individual parameter is considered fixed (default=0.001) |
fError.min |
minimum fraction of residual variance for combined error model (default = 1e-3) |
paramToUse |
list of parameters possibly function of covariates (default="all") |
covToTest |
components of the covariate model that can be modified (default="all") |
covToTransform |
list of (continuous) covariates to be log-transformed (default="none") |
center.covariate |
TRUE/FALSE center the covariates of the final model (default=FALSE) |
criterion |
penalization criterion to optimize c("AIC", "BIC", "BICc", gamma) |
linearization |
TRUE/FALSE whether the computation of the likelihood is based on a linearization of the model (default=FALSE, deprecated) |
ll |
TRUE/FALSE compute the observe likelihood and the criterion to optimize at each iteration |
test |
TRUE/FALSE perform additional statistical tests for building the model (default=TRUE) |
direction |
method for covariate search c("full", "both", "backward", "forward"), (default="full" or "both") |
steps |
maximum number of iteration for stepAIC (default=1000) |
max.iter |
maximum number of iterations (default=20) |
explor.iter |
number of iterations during the exploratory phase (default=2) |
seq.cov |
TRUE/FALSE whether the covariate model is built before the correlation model |
seq.corr |
TRUE/FALSE whether the correlation model is built iteratively (default=TRUE) |
seq.cov.iter |
number of iterations before building the correlation model (only when seq.cov=F, default=0) |
p.max |
maximum p-value used for removing non significant relationships between covariates and individual parameters (default=0.1) |
p.min |
minimum p-values used for testing the components of a new model (default=c(0.075, 0.05, 0.1)) |
print |
TRUE/FALSE display the results (default=TRUE) |
nb.model |
number of models to display at each iteration (default=1) |
fix.param1 |
parameters with variability that cannot be removed (default=NULL) |
fix.param0 |
parameters without variability that cannot be added (default=NULL) |
remove |
try to remove random effects (default=T) |
add |
try to add random effects (default=T) |
delta |
maximum difference in criteria for testing a new model (default=c(30,10,5)) |
omega.set |
settings to define how a variance varies during iterations of SAEM |
pop.set1 |
Monolix settings 1 |
pop.set2 |
Monolix settings 2 |
See https://monolix.lixoft.com/rsmlx/ for more details.
a new Monolix project with a new statistical model.
## Not run: # Build the complete statistical model using the default settings r1 <- buildAll(project="warfarinPK_project.mlxtran") # Force parameter Tlag to be fixed (no variability) and parameter Cl to vary r2 <- buildAll(project="warfarinPK_project.mlxtran", fix.param0="Tlag", fix.param1="Cl") # Estimate the log-likelihood by linearization of the model (faster) r3 <- buildAll(project="warfarinPK_project.mlxtran", linearization=T) ## End(Not run) # See http://monolix.lixoft.com/rsmlx/buildmlx/ for detailed examples of use of buildmlx # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation## Not run: # Build the complete statistical model using the default settings r1 <- buildAll(project="warfarinPK_project.mlxtran") # Force parameter Tlag to be fixed (no variability) and parameter Cl to vary r2 <- buildAll(project="warfarinPK_project.mlxtran", fix.param0="Tlag", fix.param1="Cl") # Estimate the log-likelihood by linearization of the model (faster) r3 <- buildAll(project="warfarinPK_project.mlxtran", linearization=T) ## End(Not run) # See http://monolix.lixoft.com/rsmlx/buildmlx/ for detailed examples of use of buildmlx # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation
Automatic statistical model building is available directly in the lixoftConnectors package using the function
runModelBuilding.
buildmlx( project = NULL, final.project = NULL, model = "all", prior = NULL, weight = NULL, coef.w1 = 0.5, paramToUse = "all", covToTest = "all", covToTransform = "none", center.covariate = FALSE, criterion = "BICc", linearization = FALSE, ll = T, test = T, direction = NULL, steps = 1000, n.full = 10, max.iter = 20, explor.iter = 2, fError.min = 0.001, seq.cov = FALSE, seq.cov.iter = 0, seq.corr = TRUE, p.max = 0.1, p.min = c(0.075, 0.05, 0.1), print = TRUE, nb.model = 1 )buildmlx( project = NULL, final.project = NULL, model = "all", prior = NULL, weight = NULL, coef.w1 = 0.5, paramToUse = "all", covToTest = "all", covToTransform = "none", center.covariate = FALSE, criterion = "BICc", linearization = FALSE, ll = T, test = T, direction = NULL, steps = 1000, n.full = 10, max.iter = 20, explor.iter = 2, fError.min = 0.001, seq.cov = FALSE, seq.cov.iter = 0, seq.corr = TRUE, p.max = 0.1, p.min = c(0.075, 0.05, 0.1), print = TRUE, nb.model = 1 )
project |
a string: the initial Monolix project |
final.project |
a string: the final Monolix project (default adds "_built" to the original project) |
model |
components of the model to optimize c("residualError", "covariate", "correlation"), (default="all") |
prior |
list of prior probabilities for each component of the model (default=NULL) |
weight |
list of penalty weights for each component of the model (default=NULL) |
coef.w1 |
multiplicative weight coefficient used for the first iteration only (default=0.5) |
paramToUse |
list of parameters possibly function of covariates (default="all") |
covToTest |
components of the covariate model that can be modified (default="all") |
covToTransform |
list of (continuous) covariates to be log-transformed (default="none") |
center.covariate |
TRUE/FALSE center the covariates of the final model (default=FALSE) |
criterion |
penalization criterion to optimize c("AIC", "BIC", "BICc", gamma) (default=BICc) |
linearization |
TRUE/FALSE whether the computation of the likelihood is based on a linearization of the model (default=FALSE) |
ll |
TRUE/FALSE compute the observe likelihood and the criterion to optimize at each iteration |
test |
TRUE/FALSE perform additional statistical tests for building the model (default=TRUE) |
direction |
method for covariate search c("full", "both", "backward", "forward"), (default="full" or "both") |
steps |
maximum number of iteration for stepAIC (default=1000) |
n.full |
maximum number of covariates for an exhaustive comparison of all possible covariate models (default=10) |
max.iter |
maximum number of iterations (default=20) |
explor.iter |
number of iterations during the exploratory phase (default=2) |
fError.min |
minimum fraction of residual variance for combined error model (default = 1e-3) |
seq.cov |
TRUE/FALSE whether the covariate model is built before the correlation model |
seq.cov.iter |
number of iterations before building the correlation model (only when seq.cov=F, default=0) |
seq.corr |
TRUE/FALSE whether the correlation model is built iteratively (default=TRUE) |
p.max |
maximum p-value used for removing non significant relationships between covariates and individual parameters (default=0.1) |
p.min |
vector of 3 minimum p-values used for testing the components of a new model (default=c(0.075, 0.05, 0.1)) |
print |
TRUE/FALSE display the results (default=TRUE) |
nb.model |
number of models to display at each iteration (default=1) |
buildmlx uses SAMBA (Stochastic Approximation for Model Building Algorithm), an iterative procedure to accelerate and optimize the process of model building by identifying at each step how best to improve some of the model components. This method allows to find the optimal statistical model which minimizes some information criterion in very few steps.
Penalization criterion can be either a custom penalization of the form gamma*(number of parameters), AIC (gamma=2) or BIC (gamma=log(N)).
Several strategies can be used for building the covariate model at each iteration of the algorithm:
direction="full" means that all the possible models are compared (default when the number of covariates
is less than 10). Othrwise, direction is the mode of stepwise search of stepAIC {MASS},
can be one of "both", "backward", or "forward", with a default of "both" when there are at least 10 covariates.
See https://monolix.lixoft.com/rsmlx/ for more details.
a new Monolix project with a new statistical model.
getModelBuildingSettings settings for model building with lixoftConnectors runModelBuilding run model building with lixoftConnectors getModelBuildingResults results for model building with lixoftConnectors
## Not run: # RsmlxDemo1.mlxtran is a Monolix project for modelling the pharmacokinetics (PK) of warfarin # using a PK model with parameters ka, V, Cl. # By default, buildmlx will compute the best statistical model in term of BIC, i.e , # the best covariate model, the best correlation model for the three random effects and the best # residual error model in terms of BIC. # In this example, three covariates (wt, age, sex) are available with the data and will be used # for building the covariate model for the three PK parameters: r1 <- buildmlx(project="RsmlxDemo1.mlxtran") # Here, the covariate model will be built for V and Cl only and log-transformation of all # continuous covariates will also be considered: r2 <- buildmlx(project="RsmlxDemo1.mlxtran", paramToUse=c("V", "Cl"), covToTransform="all") # Only the covariate model will be built, using AIC instead of BIC: r3 <- buildmlx(project="RsmlxDemo1.mlxtran", model="covariate", criterion="AIC") ## End(Not run) # See http://monolix.lixoft.com/rsmlx/buildmlx/ for detailed examples of use of buildmlx # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation## Not run: # RsmlxDemo1.mlxtran is a Monolix project for modelling the pharmacokinetics (PK) of warfarin # using a PK model with parameters ka, V, Cl. # By default, buildmlx will compute the best statistical model in term of BIC, i.e , # the best covariate model, the best correlation model for the three random effects and the best # residual error model in terms of BIC. # In this example, three covariates (wt, age, sex) are available with the data and will be used # for building the covariate model for the three PK parameters: r1 <- buildmlx(project="RsmlxDemo1.mlxtran") # Here, the covariate model will be built for V and Cl only and log-transformation of all # continuous covariates will also be considered: r2 <- buildmlx(project="RsmlxDemo1.mlxtran", paramToUse=c("V", "Cl"), covToTransform="all") # Only the covariate model will be built, using AIC instead of BIC: r3 <- buildmlx(project="RsmlxDemo1.mlxtran", model="covariate", criterion="AIC") ## End(Not run) # See http://monolix.lixoft.com/rsmlx/buildmlx/ for detailed examples of use of buildmlx # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation
buildVar is designed to build the best variance model for the random effects by selecting which individual parameters vary and which ones are fixed.
buildVar( project = NULL, final.project = NULL, prior = NULL, weight = NULL, cv.min = 0.001, fix.param1 = NULL, fix.param0 = NULL, criterion = "BICc", linearization = F, remove = T, add = T, delta = c(30, 10, 5), omega.set = NULL, pop.set1 = NULL, pop.set2 = NULL, print = TRUE )buildVar( project = NULL, final.project = NULL, prior = NULL, weight = NULL, cv.min = 0.001, fix.param1 = NULL, fix.param0 = NULL, criterion = "BICc", linearization = F, remove = T, add = T, delta = c(30, 10, 5), omega.set = NULL, pop.set1 = NULL, pop.set2 = NULL, print = TRUE )
project |
a string: the initial Monolix project |
final.project |
a string: the final Monolix project (default adds "_var" to the original project) |
prior |
named vector of prior probabilities (default=NULL) |
weight |
named vector of weights (default=NULL) |
cv.min |
value of the coefficient of variation below which an individual parameter is considered fixed (default=0.001) |
fix.param1 |
parameters with variability that cannot be removed (default=NULL) |
fix.param0 |
parameters without variability that cannot be added (default=NULL) |
criterion |
penalization criterion to optimize c("AIC", "BIC", "BICc", gamma) (default=BICc) |
linearization |
TRUE/FALSE whether the computation of the likelihood is based on a linearization of the model (default=FALSE) |
remove |
TRUE/FALSE try to remove random effects (default=TRUE) |
add |
TRUE/FALSE try to add random effects (default=TRUE) |
delta |
maximum difference in criteria for testing a new model (default=c(30,10,5)) |
omega.set |
settings to define how a variance varies during iterations of SAEM |
pop.set1 |
Monolix settings 1 |
pop.set2 |
Monolix settings 2 |
print |
TRUE/FALSE display the results (default=TRUE) |
Penalization criterion can be either a custom penalization of the form gamma*(number of parameters), AIC (gamma=2) or BIC (gamma=log(N)).
See https://monolix.lixoft.com/rsmlx/ for more details.
a new Monolix project with a new inter individual variability model.
## Not run: # Build the variability model using the default settings r1 <- buildVar(project="warfarinPK_project.mlxtran") # Force parameter Tlag to be fixed (no variability) and parameter Cl to vary r2 <- buildVar(project="warfarinPK_project.mlxtran", fix.param0="Tlag", fix.param1="Cl") # Estimate the log-likelihood by linearization of the model (faster) r3 <- buildVar(project="warfarinPK_project.mlxtran", linearization=T) ## End(Not run) # See http://monolix.lixoft.com/rsmlx/buildvar/ for detailed examples of use of buildvar # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation## Not run: # Build the variability model using the default settings r1 <- buildVar(project="warfarinPK_project.mlxtran") # Force parameter Tlag to be fixed (no variability) and parameter Cl to vary r2 <- buildVar(project="warfarinPK_project.mlxtran", fix.param0="Tlag", fix.param1="Cl") # Estimate the log-likelihood by linearization of the model (faster) r3 <- buildVar(project="warfarinPK_project.mlxtran", linearization=T) ## End(Not run) # See http://monolix.lixoft.com/rsmlx/buildvar/ for detailed examples of use of buildvar # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation
Compute confidence intervals for the population parameters estimated by Monolix.
confintmlx( project, parameters = "all", method = "fim", level = 0.9, linearization = TRUE, nboot = 100, parametric = FALSE, settings = NULL )confintmlx( project, parameters = "all", method = "fim", level = 0.9, linearization = TRUE, nboot = 100, parametric = FALSE, settings = NULL )
project |
a Monolix project |
parameters |
list of parameters for which confidence intervals are computed (default="all") |
method |
method |
level |
confidence level, a real number between 0 and 1 (default=0.90) |
linearization |
TRUE/FALSE whether the calculation of the standard errors (default=TRUE) or the profile likelihood is based on a linearization of the model (default=TRUE) |
nboot |
number of bootstrat replicates (default=100, used when method="bootstrap") |
parametric |
boolean to define if parametric bootstrap is performed (new data is drawn from the model), (default: FALSE) |
settings |
a list of settings for the profile likelihood method:
|
Most functionality to compute confidence intervals (other than profile likelihood) is now available directly in the lixoftConnectors package. Please migrate the following uses of this function:
| confintmlx method | lixoftConnectors function | |
"fim" |
linearizarion = TRUE |
getEstimatedConfidenceIntervals (method = "linearization") |
"fim" |
linearizarion = FALSE |
getEstimatedConfidenceIntervals (method = "stochasticApproximation") |
"bootstrap" |
parametric = TRUE |
runBootstrap (method = "parametric") |
"bootstrap" |
parametric = FALSE |
runBootstrap (method = "nonparametric") |
For method="proflike", continue using this function.
The method used for computing the confidence intervals can be either based on the
standard errors derived from an estimation of the Fisher Information Matrix ("fim"),
on the profile likelihood ("proflike") or on nonparametric bootstrap estimate ("bootstrap").
method="fim" is used by default.
When method="fim", the FIM can be either estimated using a linearization of the model
or a stochastic approximation. When method="proflike", the observed likelihood can be
either estimated using a linearization of the model or an importance sampling Monte Carlo
procedure. When method="bootstrap", the bootstrap estimates are obtained using the bootmlx
function
a list with the computed confidence intervals, the method used and the level.
getEstimatedConfidenceIntervals replaces this function for method = "fim" in lixoftConnectors runBootstrap replaces this function for method = "bootstrap" in lixoftConnectors
## Not run: # RsmlxDemo2.mlxtran is a Monolix project for modelling the PK of warfarin using a PK model # with parameters ka, V, Cl. # confintmlx will compute a 90% confidence interval for all the population parameters # using the population estimates obtained by Monolix and the Fisher Information Matrix # estimated by linearization r1 <- confintmlx(project="RsmlxDemo2.mlxtran") # 95% confidence intervals are now computed, using the FIM estimated by Monolix using a # stochastic approximation algorithm: r2 <- confintmlx(project="RsmlxDemo2.mlxtran", linearization=FALSE, level=0.95) # Confidence intervals are computed for ka_pop and omega_ka only, # using the profile likelihood method: r <- confintmlx(project = "RsmlxDemo2.mlxtran", method = "proflike", parameters = c("ka_pop","omega_ka")) # Confidence intervals are computed using 200 bootstrap samples: r3 <- confintmlx(project="RsmlxDemo2.mlxtran", method="bootstrap", nboot=200) ## End(Not run) # See http://monolix.lixoft.com/rsmlx/confintmlx/ for detailed examples of use of confintmlx # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation## Not run: # RsmlxDemo2.mlxtran is a Monolix project for modelling the PK of warfarin using a PK model # with parameters ka, V, Cl. # confintmlx will compute a 90% confidence interval for all the population parameters # using the population estimates obtained by Monolix and the Fisher Information Matrix # estimated by linearization r1 <- confintmlx(project="RsmlxDemo2.mlxtran") # 95% confidence intervals are now computed, using the FIM estimated by Monolix using a # stochastic approximation algorithm: r2 <- confintmlx(project="RsmlxDemo2.mlxtran", linearization=FALSE, level=0.95) # Confidence intervals are computed for ka_pop and omega_ka only, # using the profile likelihood method: r <- confintmlx(project = "RsmlxDemo2.mlxtran", method = "proflike", parameters = c("ka_pop","omega_ka")) # Confidence intervals are computed using 200 bootstrap samples: r3 <- confintmlx(project="RsmlxDemo2.mlxtran", method="bootstrap", nboot=200) ## End(Not run) # See http://monolix.lixoft.com/rsmlx/confintmlx/ for detailed examples of use of confintmlx # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation
Automatic search of the best covariate model. Automatic covariate model building is available directly in the lixoftConnectors package using the function
runModelBuilding. Please migrate, as this function will be deprecated in the future.
Two methods for covariate model building are proposed
SCM: stepwise covariate modeling method In the forward selection, at each step, each of the remaining (i.e not yet included) parameter-covariate relationships are added to the model in an univariate model (one model per relationship), and run. Among all models, the model that improves some criteria (LRT, BIC or AIC) most is selected and taken forward to the next step. During backward elimination, parameter-covariate relationships are removed in an univariate manner.
COSSAC: COnditional Sampling for Stepwise Approach based on Correlation tests method COSSAC makes use of the information contained in the base model run to choose which covariate to try first (instead of trying all covariates "blindly" as in SCM). Indeed, the correlation between the individual parameters (or random effects) and the covariates hints at possibly relevant parameter-covariate relationships. If the EBEs (empirical Bayes estimates) are used, shrinkage may bias the result. COSSAC instead uses samples from the a posteriori conditional distribution (available as "conditional distribution" task in MonolixSuite2018) to calculate the correlation between the random effects and covariates. A p-value can be derived using the Pearson's correlation test for continuous covariate and ANOVA for categorical covariate. The p-values are used to sort all the random effect-covariate relationships. Relationships with the lowest p-value are added first, run and confirmed using a likelihood ratio test, AIC or BIC criteria.
covariateSearch( project, final.project = NULL, method = NULL, covToTest = NULL, covToTransform = NULL, paramToUse = NULL, testRelations = NULL, settings = NULL )covariateSearch( project, final.project = NULL, method = NULL, covToTest = NULL, covToTransform = NULL, paramToUse = NULL, testRelations = NULL, settings = NULL )
project |
a Monolix project |
final.project |
[optional] string corresponding to the final Monolix project (default: 'runFinal.mlxtran' in covariate search output folder) |
method |
[optional] string correspondig to the method. It can be 'COSSAC' or 'SCM'. By default, COSSAC' is used. |
covToTest |
[optional] vector of covariates to test. Cannot be used if testRelations is defined. By default, all covariates are tested. |
covToTransform |
[optional] vector of covariates to transform. The transformation consists in a log transform of the covariate with centering by the mean value (ex: WT is transformed into log(WT/mean) with mean the mean WT value over the individuals of the data set). Both the transformed and untransformed covariate are tested by the algorithm. By default, no covariate is transformed. Note: adding a non-transformed covariate on a lognormally distributed parameter results in an exponential relationship: log(V) = log(Vpop) + beta*WT + eta <=> V = Vpop * exp(beta*WT) * exp(eta) adding a log-transformed covariate on a lognormally distributed parameter results in a power law relationship: log(V) = log(Vpop) + beta*log(WT/70) + eta <=> V = Vpop * (WT/70)^beta * exp(eta) |
paramToUse |
[optional] vector of parameters which may be function of covariates. Cannot be used if testRelations is defined. By default, all parameters are tested. |
testRelations |
[optional] list of parameter-covariate relationships to test, ex: list(V=c("WT","SEX"),Cl=c("CRCL")). Cannot be used if covToTest or paramToUse is defined. By default, all parameter-covariate relationships are tested. |
settings |
[optional] list of settings for the covariate search:
|
getModelBuildingSettings settings for model building with lixoftConnectors runModelBuilding run model building with lixoftConnectors getModelBuildingResults results for model building with lixoftConnectors
## Not run: # RsmlxDemo1.mlxtran is a Monolix project for modelling the pharmacokinetics (PK) of warfarin # using a PK model with parameters ka, V, Cl. # In this example, three covariates (wt, age, sex) are available with the data # covariatesearch will compute the best covariate model, in term of BIC, # for the three PK parameters using the three covariates. r1 <- covariateSearch(project="RsmlxDemo1.mlxtran") # Instead of using the COSSAC method, we can use the SCM method: r2 <- covariateSearch(project="RsmlxDemo1.mlxtran", method = 'SCM') # Here, the covariate model is built using age and wt only, for V and Cl only: r3 <- covariateSearch(project = "RsmlxDemo1.mlxtran", paramToUse = c("V","Cl"), covToTest = c("age","wt")) ## End(Not run) # See http://monolix.lixoft.com/rsmlx/covariatesearch/ for detailed examples of covariatesearch # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation## Not run: # RsmlxDemo1.mlxtran is a Monolix project for modelling the pharmacokinetics (PK) of warfarin # using a PK model with parameters ka, V, Cl. # In this example, three covariates (wt, age, sex) are available with the data # covariatesearch will compute the best covariate model, in term of BIC, # for the three PK parameters using the three covariates. r1 <- covariateSearch(project="RsmlxDemo1.mlxtran") # Instead of using the COSSAC method, we can use the SCM method: r2 <- covariateSearch(project="RsmlxDemo1.mlxtran", method = 'SCM') # Here, the covariate model is built using age and wt only, for V and Cl only: r3 <- covariateSearch(project = "RsmlxDemo1.mlxtran", paramToUse = c("V","Cl"), covToTest = c("age","wt")) ## End(Not run) # See http://monolix.lixoft.com/rsmlx/covariatesearch/ for detailed examples of covariatesearch # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation
Get estimated covariance and correlation matrices
getEstimatedCovarianceMatrix()getEstimatedCovarianceMatrix()
a list of two matrices.
## Not run: # Assume that the Monolix project "warfarinPKPD.mlxtran" has been loaded r = getEstimatedCovarianceMatrix() # r is a list with elements "cor.matrix" and "cov.matrix" # See http://monolix.lixoft.com/rsmlx/newconnectors/ for more detailed examples # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation ## End(Not run)## Not run: # Assume that the Monolix project "warfarinPKPD.mlxtran" has been loaded r = getEstimatedCovarianceMatrix() # r is a list with elements "cor.matrix" and "cov.matrix" # See http://monolix.lixoft.com/rsmlx/newconnectors/ for more detailed examples # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation ## End(Not run)
Get the individual individual parameters, the population parameters with the population covariates and the population parameters with the individual covariates.
getEstimatedIndividualParameters2()getEstimatedIndividualParameters2()
a list of data frames.
## Not run: # Assume that the Monolix project "warfarinPKPD.mlxtran" has been loaded r = getEstimatedIndividualParameters2() # r is a list with elements "saem", "conditionalMean", "conditionalSD", "conditionalMode", # "popPopCov" and "popIndCov" # See http://monolix.lixoft.com/rsmlx/newconnectors/ for more detailed examples # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation ## End(Not run)## Not run: # Assume that the Monolix project "warfarinPKPD.mlxtran" has been loaded r = getEstimatedIndividualParameters2() # r is a list with elements "saem", "conditionalMean", "conditionalSD", "conditionalMode", # "popPopCov" and "popIndCov" # See http://monolix.lixoft.com/rsmlx/newconnectors/ for more detailed examples # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation ## End(Not run)
Get the individual predictions obtained with the estimated individual parameters :
getEstimatedPredictions()getEstimatedPredictions()
a list of data frames (one data frame per output).
## Not run: # Assume that the Monolix project "warfarinPKPD.mlxtran" has been loaded r = getEstimatedPredictions() # r is a list with elements "y1" and "y2" # See http://monolix.lixoft.com/rsmlx/newconnectors/ for more detailed examples # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation ## End(Not run)## Not run: # Assume that the Monolix project "warfarinPKPD.mlxtran" has been loaded r = getEstimatedPredictions() # r is a list with elements "y1" and "y2" # See http://monolix.lixoft.com/rsmlx/newconnectors/ for more detailed examples # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation ## End(Not run)
Get the residuals computed from the individual predictions obtained with the estimated individual parameters:
getEstimatedResiduals()getEstimatedResiduals()
a list of data frames (one data frame per output).
## Not run: # Assume that the Monolix project "warfarinPKPD.mlxtran" has been loaded r = getEstimatedResiduals() # r is a list with elements "y1" and "y2" # See http://monolix.lixoft.com/rsmlx/newconnectors/ for more detailed examples # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation ## End(Not run)## Not run: # Assume that the Monolix project "warfarinPKPD.mlxtran" has been loaded r = getEstimatedResiduals() # r is a list with elements "y1" and "y2" # See http://monolix.lixoft.com/rsmlx/newconnectors/ for more detailed examples # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation ## End(Not run)
Get the individual predictions obtained with the simulated individual parameters :
getSimulatedPredictions()getSimulatedPredictions()
a list of data frames (one data frame per output).
## Not run: # Assume that the Monolix project "warfarinPKPD.mlxtran" has been loaded r = getSimulatedPredictions() # r is a list with elements "Cc" and "E" # See http://monolix.lixoft.com/rsmlx/newconnectors/ for more detailed examples # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation ## End(Not run)## Not run: # Assume that the Monolix project "warfarinPKPD.mlxtran" has been loaded r = getSimulatedPredictions() # r is a list with elements "Cc" and "E" # See http://monolix.lixoft.com/rsmlx/newconnectors/ for more detailed examples # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation ## End(Not run)
Get the residuals computed from the individual predictions obtained with the simulated individual parameters:
getSimulatedResiduals()getSimulatedResiduals()
a list of data frames (one data frame per output).
## Not run: # Assume that the Monolix project "warfarinPKPD.mlxtran" has been loaded r = getSimulatedResiduals() # r is a list with elements "y1" and "y2" # See http://monolix.lixoft.com/rsmlx/newconnectors/ for more detailed examples # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation ## End(Not run)## Not run: # Assume that the Monolix project "warfarinPKPD.mlxtran" has been loaded r = getSimulatedResiduals() # r is a list with elements "y1" and "y2" # See http://monolix.lixoft.com/rsmlx/newconnectors/ for more detailed examples # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation ## End(Not run)
Initialize Rsmlx library and lixoftConnectors. Prints information about the versions of Monolix and lixoftConnectors used.
initRsmlx(path = NULL)initRsmlx(path = NULL)
path |
Monolix path |
A list:
software: the software that is used (should be monolix with Rsmlx)
path: the path to MonolixSuite
version: the version of MonolixSuite that is used
status: boolean equaling TRUE if the initialization has been successful.
## Not run: initRsmlx() initRsmlx(path="C:/ProgramData/Lixoft/MonolixSuite2024R1") # specifiy a specific path ## End(Not run)## Not run: initRsmlx() initRsmlx(path="C:/ProgramData/Lixoft/MonolixSuite2024R1") # specifiy a specific path ## End(Not run)
Fit several structural PK models and select the best one based on a Bayesian Information Criterion. Models to compare can be defined by rate constants and/or clearances and can include or not nonlinear elimination models. See https://monolix.lixoft.com/rsmlx/pkbuild/ for more details.
pkbuild( data = NULL, project = NULL, stat = FALSE, param = "clearance", new.dir = ".", MM = FALSE, linearization = F, criterion = "BICc", level = NULL, settings.stat = NULL )pkbuild( data = NULL, project = NULL, stat = FALSE, param = "clearance", new.dir = ".", MM = FALSE, linearization = F, criterion = "BICc", level = NULL, settings.stat = NULL )
data |
a list with fields
|
project |
a Monolix project |
stat |
(FALSE, TRUE): the statistical model is also built (using buildmlx) (default=FALSE) |
param |
("clearance", "rate", "both): parametrization (default="clearance") |
new.dir |
name of the directory where the created files are stored (default is the current working directory) ) |
MM |
(FALSE, TRUE): tested models include or not Michaelis Menten elimination models (default=FALSE) |
linearization |
TRUE/FALSE whether the computation of the likelihood is based on a linearization of the model (default=FALSE) |
criterion |
penalization criterion to optimize c("AIC", "BIC", "BICc", gamma) (default="BICc") |
level |
an integer between 1 and 9 (used by setSettings) |
settings.stat |
list of settings used by buildmlx (only if stat=TRUE) |
A list of results
## Not run: # Build a PK model for the warfarin PK data. # By default, only models using clearance (and inter compartmental clearances) are used warf.pk1 <- pkbuild(data=warfarin) # Models using elimination and transfer rate constants are used, # as well as nonlinear elimination models warf.pk2 <- pkbuild(data=warfarin, new.dir="warfarin", param="rate", MM=TRUE) # Both models using clearances and rates are used. # Level is set to 7 in order to get accurate results. warf.pk3 <- pkbuild(data=warfarin, new.dir="warfarin", param="both", level=7) ## End(Not run)## Not run: # Build a PK model for the warfarin PK data. # By default, only models using clearance (and inter compartmental clearances) are used warf.pk1 <- pkbuild(data=warfarin) # Models using elimination and transfer rate constants are used, # as well as nonlinear elimination models warf.pk2 <- pkbuild(data=warfarin, new.dir="warfarin", param="rate", MM=TRUE) # Both models using clearances and rates are used. # Level is set to 7 in order to get accurate results. warf.pk3 <- pkbuild(data=warfarin, new.dir="warfarin", param="both", level=7) ## End(Not run)
Use the pooled PK data to derive population PK parameters for a "standard" PK model (i.e. a model of the Monolix PK library). The structural model is automatically defined using the names of the PK parameters. Allowed names are: 'Tlag', 'Mtt', 'Ktr', 'ka', 'Tk0', 'V', 'V1', 'V2', 'V3', 'Q', 'Q2', 'Q3', 'Cl', 'k', 'k12', 'k21', 'k13', 'k31', 'Vm', 'Km'.
pkpopini( data = NULL, project = NULL, parameter = NULL, new.project = NULL, new.dir = NULL, par.ini = NULL )pkpopini( data = NULL, project = NULL, parameter = NULL, new.project = NULL, new.dir = NULL, par.ini = NULL )
data |
a list with fields
|
project |
a Monolix project |
parameter |
a vector of strings (names of the PK parameters) |
new.project |
name of the new Monolix project (a default name is created if not provided) |
new.dir |
name of the directory where the created files are stored (default is the current working directory) ) |
par.ini |
a vector of PK parameter values |
A Monolix project is then automatically created using these values as initial population parameters.
See https://monolix.lixoft.com/rsmlx/pkpopini/ for more details.
A list of results
## Not run: # Create in the working directory a Monolix project for a 1 cpt model with # lag time, 0 order absorption and linear elimination warf.ini1 <- pkpopini(data=warfarin, param=c("Tlag", "Tk0", "V", "Cl")) # Create in directory 'warfarin' a Monolix project called 'warfPK2.mlxtran' # for a 2 cpt model with 1st order absorption and nonlinear elimination warf.ini3 <- pkpopini(data=warfarin, param=c("ka", "V", "k12", "k21", "Vm", "Km"), new.dir="warfarin", new.project="warfPK2.mlxtran") ## End(Not run)## Not run: # Create in the working directory a Monolix project for a 1 cpt model with # lag time, 0 order absorption and linear elimination warf.ini1 <- pkpopini(data=warfarin, param=c("Tlag", "Tk0", "V", "Cl")) # Create in directory 'warfarin' a Monolix project called 'warfPK2.mlxtran' # for a 2 cpt model with 1st order absorption and nonlinear elimination warf.ini3 <- pkpopini(data=warfarin, param=c("ka", "V", "k12", "k21", "Vm", "Km"), new.dir="warfarin", new.project="warfPK2.mlxtran") ## End(Not run)
Read data in a Monolix/NONMEM format
readDatamlx( data = NULL, out.data = FALSE, nbSSDoses = 10, obs.rows = FALSE, datafile = NULL, header = NULL )readDatamlx( data = NULL, out.data = FALSE, nbSSDoses = 10, obs.rows = FALSE, datafile = NULL, header = NULL )
data |
a list with fields
|
out.data |
TRUE/FALSE (default=FALSE) returns the original data as a table and some information about the Monolix project |
nbSSDoses |
number of additional doses to use for steady-state (default=10) |
obs.rows |
a list of observation indexes |
datafile |
(deprecated) a formatted data file |
header |
(deprecated) a vector of strings |
A list of data frames
## Not run: # using a data file: warfarinPK <- list(dataFile = "data/warfarinPK.csv", headerTypes = c("id", "time", "observation", "amount", "contcov", "contcov", "catcov"), administration = "oral") d <- readDatamlx(data=warfarinPK) ## End(Not run)## Not run: # using a data file: warfarinPK <- list(dataFile = "data/warfarinPK.csv", headerTypes = c("id", "time", "observation", "amount", "contcov", "contcov", "catcov"), administration = "oral") d <- readDatamlx(data=warfarinPK) ## End(Not run)
Monolix results used by the Rsmlx examples
resMonolixresMonolix
A R list
Monolix demos
Rsmlx website: http://rsmlx.webpopix.org
RsmlxDemo2.mlxtran is a Monolix project for modelling the pharmacokinetics (PK) of warfarin using a PK model with parameters ka, V, Cl. There is no covariate in the model.
RsmlxDemo1.projectRsmlxDemo1.project
A text file
Monolix project
Rsmlx doumentation
RsmlxDemo2.mlxtran is a Monolix project for modelling the pharmacokinetics (PK) of warfarin using a PK model with parameters ka, V, Cl. Here, V and Cl are function of weight.
RsmlxDemo2.projectRsmlxDemo2.project
A text file
Monolix project
Rsmlx doumentation
Use a single accuracy level, between 1 and 9, to automatically tune all the settings of a Monolix project. When the accuray level is equal to 1, the algorithms are very fast but the results may be not precise. When the accuray level is equal to 9, the algorithms are slow but the results are accurate. Default Monolix settings are obtained with level=5.
setSettings(project = NULL, new.project = NULL, level = 5)setSettings(project = NULL, new.project = NULL, level = 5)
project |
a string: a Monolix project (the loaded project if NULL) |
new.project |
a string: the new created Monolix project (default is the original project) |
level |
an integer between 1 and 9 (default=5) |
## Not run: # RsmlxDemo1.mlxtran is a Monolix project for modelling the PK of warfarin. # All settings of the project are set so that algorithms used by Monolix converge as # quickly as possible possible: setSettings(project="RsmlxDemo1.mlxtran", level=1) # A new project will be created with settings set in order to obtain the most # precise results possible: new.project= file.path(tempdir(),"RsmlxDemoNew.mlxtran") setSettings(project="RsmlxDemo1.mlxtran", new.project=new.project, level=9) # See http://monolix.lixoft.com/rsmlx/setSettings/ for detailed examples of use of setSettings # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation ## End(Not run)## Not run: # RsmlxDemo1.mlxtran is a Monolix project for modelling the PK of warfarin. # All settings of the project are set so that algorithms used by Monolix converge as # quickly as possible possible: setSettings(project="RsmlxDemo1.mlxtran", level=1) # A new project will be created with settings set in order to obtain the most # precise results possible: new.project= file.path(tempdir(),"RsmlxDemoNew.mlxtran") setSettings(project="RsmlxDemo1.mlxtran", new.project=new.project, level=9) # See http://monolix.lixoft.com/rsmlx/setSettings/ for detailed examples of use of setSettings # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation ## End(Not run)
Perform several statistical tests using the results of a Monolix run to assess the statistical components of the model in use.
testmlx( project = NULL, tests = c("covariate", "randomEffect", "correlation", "residual"), plot = FALSE, adjust = "edf", n.sample = NULL )testmlx( project = NULL, tests = c("covariate", "randomEffect", "correlation", "residual"), plot = FALSE, adjust = "edf", n.sample = NULL )
project |
a Monolix project |
tests |
a vector of strings: the list of tests to perform among c("covariate","randomEffect","correlation","residual") |
plot |
FALSE/TRUE display some diagnostic plots associated to the tests (default=FALSE) |
adjust |
method to take into account the dependency of MCMC sample c("edf","BH") (default="edf") |
n.sample |
number of samples from the conditional distribution to be used (default = number of available samples in the project) |
The tests used are: 1) F-tests (or, equivalently, correlation tests) to evaluate the effect of each covariate on each parameter ("covariate"), 2) correlation tests to assess the correlation structure of the random effects ("correlation"), 3) Shapiro-Wilk and Miao-Gel-Gastwirth tests to assess, respectively the normality and the symmetry of the distribution of the random effects (""randomEffect"), 4) Shapiro-Wilk and Miao-Gel-Gastwirth tests to assess, respectively the normality and the symmetry of the distribution of residual errors ("residual").
By default, the four tests are performed.
When several samples of the conditional distributions are used, two methods are proposed in order to take into the dependance of the samples for the Shapiro-Wilk and Miao-Gel-Gastwirth tests: "edf" computes an effective degrees of freedom, "BH" performs one test per replicates and adjust the smallest p-value using the Benjamini-Hochberg correction.
a list of data frames and ggplot objects if plot=TRUE
## Not run: # RsmlxDemo2.mlxtran is a Monolix project for modelling the PK of warfarin using a PK model # with parameters ka, V, Cl. #testmlx will perform statistical tests for the different component of the statistical model: r1 <- testmlx(project="RsmlxDemo2.mlxtran") #testmlx will perform statistical tests for the covariate model and the correlation model only. r2 <- testmlx(project="RsmlxDemo2.mlxtran", tests=c("covariate","correlation")) ## End(Not run) # See http://monolix.lixoft.com/rsmlx/testmlx/ for detailed examples of use of testmlx # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation## Not run: # RsmlxDemo2.mlxtran is a Monolix project for modelling the PK of warfarin using a PK model # with parameters ka, V, Cl. #testmlx will perform statistical tests for the different component of the statistical model: r1 <- testmlx(project="RsmlxDemo2.mlxtran") #testmlx will perform statistical tests for the covariate model and the correlation model only. r2 <- testmlx(project="RsmlxDemo2.mlxtran", tests=c("covariate","correlation")) ## End(Not run) # See http://monolix.lixoft.com/rsmlx/testmlx/ for detailed examples of use of testmlx # Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation
The warfarin PK and PD data for 32 patients
warfarin.datawarfarin.data
A csv file
Monolix demos
O’Reilly (1968). Studies on coumarin anticoagulant drugs. Initiation of warfarin therapy without a loading dose. Circulation 1968, 38:169-177.
Return the path of the Monolix PK model defined by a list of parameter names See https://monolix.lixoft.com/rsmlx/whichPKmodel/ for more details.
whichPKmodel(parameter, mlxPath = NULL, pkPath = NULL, lib = FALSE)whichPKmodel(parameter, mlxPath = NULL, pkPath = NULL, lib = FALSE)
parameter |
a vector of PK parameter names |
mlxPath |
path to Monolix install |
pkPath |
path to the Monolix PK library |
lib |
boolean to define if the absolute path is returned |
## Not run: whichPKmodel(parameter=c("Tlag", "Tk0", "V", "Cl")) ## End(Not run)## Not run: whichPKmodel(parameter=c("Tlag", "Tk0", "V", "Cl")) ## End(Not run)