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Kinetic nonlinear mixed-effects model fit by maximum likelihood
Structural model:
d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
* parent
d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
* exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
exp(-k2 * time))) * parent - k_m1 * m1
Data:
509 observations of 2 variable(s) grouped in 15 datasets
Log-likelihood: -1329
Fixed effects:
list(parent_0 ~ 1, log_k_m1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)
parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2
100.43 -5.34 -0.08 -2.90 -4.34
g_qlogis
-0.19
Random effects:
Formula: list(parent_0 ~ 1, log_k_m1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)
Level: ds
Structure: Diagonal
parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 g_qlogis Residual
StdDev: 1 0.1 0.3 0.6 0.5 0.3 3
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