et.Rd
Event Table Function
et(x, ..., envir = parent.frame()) # S3 method for RxODE et(x, ..., envir = parent.frame()) # S3 method for rxSolve et(x, ..., envir = parent.frame()) # S3 method for rxParams et(x, ..., envir = parent.frame()) # S3 method for default et( x, ..., time, amt, evid, cmt, ii, addl, ss, rate, dur, until, id, amountUnits, timeUnits, addSampling, envir = parent.frame(), by = NULL, length.out = NULL )
x  This is the first argument supplied to the event table.
This is named to allow 

...  Times or event tables. They can also be one of the named arguments below. 
envir  the 
time  Time is the time of the dose or the sampling times. This can also be unspecified and is determined by the object type (list or numeric/integer). 
amt  Amount of the dose. If specified, this assumes a dosing record, instead of a sampling record. 
evid  Event ID; This can be:

cmt  Compartment name or number. If a number, this is an integer starting at 1. Negative compartments turn off a compartment. If the compartment is a name, the compartment name is changed to the correct state/compartment number before running the simulation. For a compartment named "cmt" the compartment is turned off. Can also specify 
ii  When specifying a dose, this is the interdose interval
for 
addl  The number of additional doses at a interdose interval after one dose. 
ss  Steady state flag; It can be one of:
All other values of 
rate  When positive, this is the rate of infusion. Otherwise:
When a modeled bioavailability is applied to positive rates
( If instead you want the modeled bioavailability to increase the
rate of infusion instead of the duration of infusion, specify the

dur  Duration of infusion. When 
until  This is the time until the dosing should end. It can be an easier way to figure out how many additional doses are needed over your sampling period. 
id  A integer vector of IDs to add or remove from the event table. If the event table is identical for each ID, then you may expand it to include all the IDs in this vector. All the negative IDs in this vector will be removed. 
amountUnits  The units for the dosing records ( 
timeUnits  The units for the time records ( 
addSampling  This is a boolean indicating if a sampling time
should be added at the same time as a dosing time. By default
this is 
by  When there are no observations in the event table, this
is the amount to increment for the observations between 
length.out  The number of observations to create if there isn't any observations in the event table. By default this is 200. 
A new event table
Wang W, Hallow K, James D (2015). "A Tutorial on RxODE: Simulating Differential Equation Pharmacometric Models in R." CPT: Pharmacometrics \& Systems Pharmacology, 5(1), 310. ISSN 21638306, <URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4728294/>.
eventTable
, add.sampling
,
add.dosing
, et
,
etRep
, etRbind
,
RxODE
## Model from RxODE tutorial mod1 <RxODE({ KA=2.94E01; CL=1.86E+01; V2=4.02E+01; Q=1.05E+01; V3=2.97E+02; Kin=1; Kout=1; EC50=200; C2 = centr/V2; C3 = peri/V3; d/dt(depot) =KA*depot; d/dt(centr) = KA*depot  CL*C2  Q*C2 + Q*C3; d/dt(peri) = Q*C2  Q*C3; d/dt(eff) = Kin  Kout*(1C2/(EC50+C2))*eff; }); ## These are making the more complex regimens of the RxODE tutorial ## bid for 5 days bid < et(timeUnits="hr") %>% et(amt=10000,ii=12,until=set_units(5, "days")) ## qd for 5 days qd < et(timeUnits="hr") %>% et(amt=20000,ii=24,until=set_units(5, "days")) ## bid for 5 days followed by qd for 5 days et < seq(bid,qd) %>% et(seq(0,11*24,length.out=100)); bidQd < rxSolve(mod1, et) plot(bidQd, C2)## Now Infusion for 5 days followed by oral for 5 days ## note you can dose to a named compartment instead of using the compartment number infusion < et(timeUnits = "hr") %>% et(amt=10000, rate=5000, ii=24, until=set_units(5, "days"), cmt="centr") qd < et(timeUnits = "hr") %>% et(amt=10000, ii=24, until=set_units(5, "days"), cmt="depot") et < seq(infusion,qd) infusionQd < rxSolve(mod1, et) plot(infusionQd, C2)## 2wkon, 1wkoff qd < et(timeUnits = "hr") %>% et(amt=10000, ii=24, until=set_units(2, "weeks"), cmt="depot") et < seq(qd, set_units(1,"weeks"), qd) %>% add.sampling(set_units(seq(0, 5.5,by=0.005),weeks)) wkOnOff < rxSolve(mod1, et) plot(wkOnOff, C2)## You can also repeat the cycle easily with the rep function qd <et(timeUnits = "hr") %>% et(amt=10000, ii=24, until=set_units(2, "weeks"), cmt="depot") et < etRep(qd, times=4, wait=set_units(1,"weeks")) %>% add.sampling(set_units(seq(0, 12.5,by=0.005),weeks)) repCycle4 < rxSolve(mod1, et) plot(repCycle4, C2)