confintmlx {Rsmlx} | R Documentation |
Confidence intervals for population parameters
Description
Compute confidence intervals for the population parameters estimated by Monolix.
Usage
confintmlx(
project,
parameters = "all",
method = "fim",
level = 0.9,
linearization = TRUE,
nboot = 100,
parametric = FALSE,
settings = NULL
)
Arguments
project |
a Monolix project |
parameters |
list of parameters for which confidence intervals are computed (default="all") |
method |
method c("fim", "proflike", "bootstrap") (default="fim") |
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:
|
Details
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
Value
a list with the computed confidence intervals, the method used and the level.
Examples
# 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)
# 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