biexp {PK} | R Documentation |
Estimation of inital and terminal half-life by fitting a biexponential model.
biexp(conc, time, prev=0, tol=1E-9)
time |
time points of concentration assessments. |
conc |
levels of concentrations. |
prev |
pre-dosing value. |
tol |
relative error tolerance. |
Estimation of inital and terminal half-life using the biexponential y=a1*exp(-b1*x)+a2*exp(-b2*x)
model with a parametrization to ensure b1 > b2 > 0 fitted by the least squares criteria with function optim
of package base
with method
"Nelder-Mead". Curve peeling (Foss, 1969) is used get start values for nonlinear model fitting. When no adequate start values are determined by curve peeling, a single exponential model is fitted with start values obtained from an OLS regression on log transformed values with a parametrization to ensure b > 0.
If the pre-dosing value indicating the intrinsinc level is greater than 0, the pre-dosing value is subtracted from all concentration levels before calculation of inital and terminal half-life.
parms |
half-life and model estimates. |
time |
time points of concentration assessments. |
conc |
levels of concentrations. |
method |
"biexp". |
Records including missing values and values below or equal to zero are omitted.
Martin Wolfsegger
Foss S. D. (1969). A Method for Obtaining Initial Estimates of the Parameters in Exponential Curve Fitting. Biometrics. 25:580-584
Pinheiro J. C. and Bates D. M. (200). Mixed-Effects Models in S and S-PLUS. Springer, New York.
## examples from Pinheiro J.C. and Bates D.M. (2000, page 279) time <- c(0.25, 0.5, 0.75, 1, 1.25, 2, 3, 4, 5, 6, 8, 0.25, 0.5, 0.75, 1, 1.25, 2, 3, 4, 5, 6, 8, 0.25, 0.5, 0.75, 1, 1.25, 2, 3, 4, 5, 6, 8, 0.25, 0.5, 0.75, 1, 1.25, 2, 3, 4, 5, 6, 8, 0.25, 0.5, 0.75, 1, 1.25, 2, 3, 4, 5, 6, 8, 0.25, 0.5, 0.75, 1, 1.25, 2, 3, 4, 5, 6, 8) conc <- c(1.5, 0.94, 0.78, 0.48, 0.37, 0.19, 0.12, 0.11, 0.08, 0.07, 0.05, 2.03, 1.63, 0.71, 0.7, 0.64, 0.36, 0.32, 0.2, 0.25, 0.12, 0.08, 2.72, 1.49, 1.16, 0.8, 0.8, 0.39, 0.22, 0.12, 0.11, 0.08, 0.08, 1.85, 1.39, 1.02, 0.89, 0.59, 0.4, 0.16, 0.11, 0.1, 0.07, 0.07, 2.05, 1.04, 0.81, 0.39, 0.3, 0.23, 0.13, 0.11, 0.08, 0.1, 0.06, 2.31, 1.44, 1.03, 0.84, 0.64, 0.42, 0.24, 0.17, 0.13, 0.1, 0.09) result <- biexp(conc=conc, time=time) print(result) plot(result)