mrgsolve facilitates simulation in R from hierarchical, ordinary differential equation (ODE) based models typically employed in drug development. See the example below.
Please see mrgsolve.github.io for additional resources.
We recommend staying up to date with the development version
devtools::install_github("metrumresearchgroup/mrgsolve")Otherwise, install the latest release on CRAN
install.packages("mrgsolve", type="source")Please be sure to see important install-related information here.
- Issue tracker (preferred) (requires GitHub account; ok for questions or issue reports)
- Google Group (email list or web-based discussion)
library(mrgsolve)
library(dplyr)
library(ggplot2)code <- '
$GLOBAL
#define CP (CENT/VC)
#define INH (CP/(IC50+CP))
$SET delta=0.1
$PARAM TVCL=1, TVVC=20, KA = 1.3, KIN=100, KOUT=2, IC50=10
$CMT GUT CENT RESP
$MAIN
double CL = exp(log(TVCL) + ETA(1));
double VC = exp(log(TVVC) + ETA(2));
RESP_0 = KIN/KOUT;
$OMEGA 0 0
$ODE
dxdt_GUT = -KA*GUT;
dxdt_CENT = KA*GUT - (CL/VC)*CENT;
dxdt_RESP = KIN*(1-INH) - KOUT*RESP;
$CAPTURE CP
'- Information about the model is saved as an
Robject - Important model attributes can be updated in
Rwithout recompiling
mod <- mcode("demo", code)- Simulated data are returned as
Robjects - Input and output data are kept in memory in the
Rprocess; writing or reading to disk is never necessary (unless results are to be saved for later use).
out <-
mod %>%
ev(amt=100, ii=24, addl=2) %>%
mrgsim(end=120)
out. Model: demo.cpp
. Dim: 1202 x 6
. Time: 0 to 120
. ID: 1
. ID time GUT CENT RESP CP
. [1,] 1 0.0 0.00 0.00 50.00 0.000
. [2,] 1 0.0 100.00 0.00 50.00 0.000
. [3,] 1 0.1 87.81 12.16 49.72 0.608
. [4,] 1 0.2 77.11 22.78 49.03 1.139
. [5,] 1 0.3 67.71 32.04 48.11 1.602
. [6,] 1 0.4 59.45 40.11 47.06 2.006
. [7,] 1 0.5 52.20 47.14 45.96 2.357
. [8,] 1 0.6 45.84 53.25 44.87 2.663
plot(out, CP+RESP~.)
out <-
mod %>% update(end=48) %>%
ev(amt=100) %>%
Req(CP,RESP) %>%
knobs(TVVC=c(10,20,40), TVCL=c(0.5,1.5))plot(out)
mod <- mod %>% omat(cmat(0.1, 0.67, 0.4))- Data set format that is likely familiar to modeling and simulation scientists
- No need to include observation records;
mrgsolvewill automatically insert
.data <-
expand.ev(ID=1:10, amt=c(100,300,1000)) %>%
mutate(dose=amt)
head(.data). ID amt evid cmt time dose
. 1 1 100 1 1 0 100
. 2 2 100 1 1 0 100
. 3 3 100 1 1 0 100
. 4 4 100 1 1 0 100
. 5 5 100 1 1 0 100
. 6 6 100 1 1 0 100
- Pass many different data sets or implement different designs in the same model code without recompiling
- Control simulation output from
Rto better manage memory
out <-
mod %>%
data_set(.data) %>%
Req(RESP,CP) %>% obsonly %>%
carry_out(dose) %>%
mrgsim(end=48, seed=1010)plot(out, RESP~time|factor(dose), scales="same")
Summarise with dplyr
out %>%
as.tbl %>%
group_by(dose) %>%
summarise(rmin = min(RESP), tmim=time[which.min(RESP)]). # A tibble: 3 × 3
. dose rmin tmim
. <dbl> <dbl> <dbl>
. 1 100 18.958869 2.9
. 2 300 16.117261 3.5
. 3 1000 6.198648 3.5
Plot with ggplot2
out %>%
as.tbl %>%
ggplot(data=.) +
geom_line(aes(x=time, y=RESP, group=ID, col=factor(dose)))