library(dplyr)
library(magrittr)
library(mrgsolve)
code <- '
$CMT GUT
$PKMODEL ncmt=1,depot=FALSE
$PARAM CL=1, V = 20, KA=1
$OMEGA
name="FOO", cor=TRUE
0.2 0.67 0.3
$SIGMA
name=s(BAR)
0.0025
'
mod <-mcode("model", code)
## Compiling model.cpp.cpp ...
## done.
## Loading: model69426a9016e9.so
named
revar(mod)
## $omega
## $FOO
## [,1] [,2]
## 1: 0.2000000 0.1641158
## 2: 0.1641158 0.3000000
##
##
## $sigma
## $BAR
## [,1]
## 1: 0.0025
The correlation is turned into covariance
as.matrix(omat(mod))
## [,1] [,2]
## 1: 0.2000000 0.1641158
## 2: 0.1641158 0.3000000
cov2cor(as.matrix(omat(mod)))
## [,1] [,2]
## 1: 1.00 0.67
## 2: 0.67 1.00
devtools::session_info()
## Session info --------------------------------------------------------------
## setting value
## version R version 3.2.3 (2015-12-10)
## system x86_64, darwin13.4.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## tz America/New_York
## date 2016-04-26
## Packages ------------------------------------------------------------------
## package * version date source
## assertthat 0.1 2013-11-08 local
## DBI 0.3.1 2014-09-24 CRAN (R 3.1.1)
## devtools 1.10.0 2016-01-23 CRAN (R 3.2.1)
## digest 0.6.9 2016-01-08 CRAN (R 3.2.1)
## dplyr * 0.4.3 2015-09-01 CRAN (R 3.2.1)
## evaluate 0.8.3 2016-03-05 CRAN (R 3.2.3)
## formatR 1.3 2016-03-05 CRAN (R 3.2.3)
## htmltools 0.3.5 2016-03-21 CRAN (R 3.2.3)
## knitr 1.12.3 2016-01-22 CRAN (R 3.2.1)
## lazyeval 0.1.10 2015-01-02 CRAN (R 3.1.2)
## magrittr * 1.5 2014-11-22 CRAN (R 3.1.2)
## memoise 1.0.0 2016-01-29 CRAN (R 3.2.1)
## mrgsolve * 0.5.12.9000 2016-04-27 local
## R6 2.1.2 2016-01-26 CRAN (R 3.2.3)
## Rcpp 0.12.4.5 2016-04-19 local
## rmarkdown 0.9.5 2016-02-22 CRAN (R 3.2.3)
## stringi 1.0-1 2015-10-22 CRAN (R 3.2.1)
## stringr 1.0.0 2015-04-30 CRAN (R 3.1.3)
## yaml 2.1.13 2014-06-12 CRAN (R 3.0.2)