# tdmore_deSolve: Create a TDM-capable model from a deSolve model In tdmore-dev/tdmore: Bayesian Therapeutic Drug Monitoring Framework

## Description

Create a TDM-capable model from a deSolve model

## Usage

 `1` ```tdmore_deSolve(parameters, add = 0, prop = 0, exp = 0, ...) ```

## Arguments

 `parameters` list of parameter names to be passed to the original function `add` additive residual error, as stdev `prop` proportional residual error, as stdev `exp` exponential residual error, as stdev. The exponential error cannot be used in conjunction with the additive or proportional error `...` Arguments to deSolve

## Value

An object of class tdmore, which can be used to estimate posthoc Bayesian parameters

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36``` ```# # Example for using deSolve with tdmore # rm(list=ls(all=TRUE)) # func <- function(t, y, parms) { # ETA1 <- as.numeric(parms['ETA1']) # ETA2 <- as.numeric(parms['ETA2']) # CL = 23.6 * exp(ETA1*0.42) # Vc = 1070 * exp(ETA2*1.11); # ka=4.48; # abs = as.numeric(y[1]) # centr = as.numeric(y[2]) # CONC = centr / Vc * 1000 # dabs = -ka*abs # dcentr = ka*abs - CL/Vc*centr # return(list(c(dabs,dcentr), c(CONC=CONC))) # } # model <- tdmore_deSolve(parameters=c("ETA1", "ETA2"), # add=3.7, # func=func, # y=c(abs=0, centr=0), # ynames=FALSE) # regimen <- data.frame( # TIME=c(0, 24), # AMT=c(15, 15), # II=c(0, 24), # ADDL=c(0, 10) # ) # pred <- predict(model, newdata=0:24, regimen=regimen) # ggplot(pred, aes(x=TIME, y=CONC)) + geom_line() # # observed <- data.frame(TIME=c(2.4, 23), CONC=c(10, 5)) # ipredfit <- model %>% # estimate(observed, regimen) # z <- plot(ipredfit) # print(z) # # D <- findDose(ipredfit, regimen=regimen, target=data.frame(TIME=48, CONC=13.5)) ```

tdmore-dev/tdmore documentation built on Nov. 15, 2018, 9:45 p.m.