Concentration predictions for 1-compartmental PK model after single or multiple bolus doses

Usage

pk_1cmt_inf(
  t = c(0:24),
  dose = 100,
  tau = 12,
  t_inf = 2,
  CL = 3,
  V = 30,
  ruv = NULL
)

Arguments

t: vector of time
dose: dose
tau: dosing interval
t_inf: infusion time
CL: clearance
V: volume of distribution
ruv: residual error (list)

Examples

pk_1cmt_inf(dose = 500, tau = 12, t_inf = 2, CL = 5, V = 50)
#>     t        dv
#> 1   0  0.000000
#> 2   1  4.758129
#> 3   2  9.063462
#> 4   3  8.200960
#> 5   4  7.420535
#> 6   5  6.714378
#> 7   6  6.075420
#> 8   7  5.497268
#> 9   8  4.974134
#> 10  9  4.500782
#> 11 10  4.072476
#> 12 11  3.684929
#> 13 12  3.334261
#> 14 13  7.775094
#> 15 14 11.793325
#> 16 15 10.671042
#> 17 16  9.655558
#> 18 17  8.736710
#> 19 18  7.905302
#> 20 19  7.153013
#> 21 20  6.472314
#> 22 21  5.856392
#> 23 22  5.299082
#> 24 23  4.794808
#> 25 24  4.338522
pk_1cmt_inf(
  dose = 500, tau = 12, t_inf = 2, CL = 5, V = 50,
  ruv = list(prop = 0.1, add = 0.1))
#>     t         dv
#> 1   0  0.2211769
#> 2   1  4.9764129
#> 3   2  8.9837039
#> 4   3  7.7411604
#> 5   4  8.1540415
#> 6   5  7.3099090
#> 7   6  5.1203647
#> 8   7  5.9962814
#> 9   8  5.0768481
#> 10  9  4.7253827
#> 11 10  3.4575314
#> 12 11  3.5068449
#> 13 12  2.9167823
#> 14 13  7.9755108
#> 15 14 11.3982794
#> 16 15 11.3998273
#> 17 16  9.5694928
#> 18 17  7.9233434
#> 19 18  9.0236776
#> 20 19  7.9644472
#> 21 20  7.0310635
#> 22 21  6.4201354
#> 23 22  5.1840119
#> 24 23  4.1762237
#> 25 24  4.1787122