Time a part of a nlmixr operation and add to nlmixr object
Arguments
- name
Name of the timing to be integrated
- code
Code to be evaluated and timed
- envir
can be either the nlmixr2 fit data, the nlmixr2 fit environment or NULL, which implies it is going to be added to the nlmixr fit when it is finalized. If the function is being called after a fit is created, please supply this environmental variable
Examples
# \donttest{
one.cmt <- function() {
ini({
## You may label each parameter with a comment
tka <- 0.45 # Ka
tcl <- log(c(0, 2.7, 100)) # Log Cl
## This works with interactive models
## You may also label the preceding line with label("label text")
tv <- 3.45; label("log V")
## the label("Label name") works with all models
eta.ka ~ 0.6
eta.cl ~ 0.3
eta.v ~ 0.1
add.sd <- 0.7
})
model({
ka <- exp(tka + eta.ka)
cl <- exp(tcl + eta.cl)
v <- exp(tv + eta.v)
linCmt() ~ add(add.sd)
})
}
fit <- nlmixr(one.cmt, theo_sd, est="saem")
#>
#>
#>
#>
#> ℹ parameter labels from comments will be replaced by 'label()'
#>
#>
#> → loading into symengine environment...
#> → pruning branches (`if`/`else`) of saem model...
#> ✔ done
#> → finding duplicate expressions in saem model...
#> ✔ done
#> params: tka tcl tv V(eta.ka) V(eta.cl) V(eta.v) add.sd
#> Calculating covariance matrix
#> → loading into symengine environment...
#> → pruning branches (`if`/`else`) of saem model...
#> ✔ done
#> → finding duplicate expressions in saem predOnly model 0...
#> → finding duplicate expressions in saem predOnly model 1...
#> → finding duplicate expressions in saem predOnly model 2...
#> ✔ done
#>
#>
#> → Calculating residuals/tables
#> ✔ done
#> → compress origData in nlmixr2 object, save 5952
#> → compress phiM in nlmixr2 object, save 63664
#> → compress parHistData in nlmixr2 object, save 13816
#> → compress saem0 in nlmixr2 object, save 27336
nlmixrWithTiming("time1", {
Sys.sleep(1)
# note this can be nested, time1 will exclude the timing from time2
nlmixrWithTiming("time2", {
Sys.sleep(1)
}, envir=fit)
}, envir=fit)
print(fit)
#> ── nlmixr² SAEM OBJF by FOCEi approximation ──
#>
#> Gaussian/Laplacian Likelihoods: AIC() or $objf etc.
#> FOCEi CWRES & Likelihoods: addCwres()
#>
#> ── Time (sec $time): ──
#>
#> setup covariance saem table compress other time2 time1
#> elapsed 0.001049 0.008005 1.08 0.068 0.019 1.053946 1.002 1.002
#>
#> ── Population Parameters ($parFixed or $parFixedDf): ──
#>
#> Parameter Est. SE %RSE Back-transformed(95%CI) BSV(CV%) Shrink(SD)%
#> tka Ka 0.453 0.195 43.1 1.57 (1.07, 2.31) 71.4 -0.445%
#> tcl Log Cl 1.02 0.0843 8.29 2.76 (2.34, 3.26) 27.2 3.88%
#> tv log V 3.45 0.0467 1.35 31.5 (28.8, 34.5) 13.9 10.2%
#> add.sd 0.695 0.695
#>
#> Covariance Type ($covMethod): linFim
#> No correlations in between subject variability (BSV) matrix
#> Full BSV covariance ($omega) or correlation ($omegaR; diagonals=SDs)
#> Distribution stats (mean/skewness/kurtosis/p-value) available in $shrink
#> Censoring ($censInformation): No censoring
#>
#> ── Fit Data (object is a modified tibble): ──
#> # A tibble: 132 × 16
#> ID TIME DV PRED RES IPRED IRES IWRES eta.ka eta.cl eta.v ka
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 0.74 0 0.74 0 0.74 1.07 0.107 -0.485 -0.0809 1.75
#> 2 1 0.25 2.84 3.26 -0.424 3.87 -1.03 -1.49 0.107 -0.485 -0.0809 1.75
#> 3 1 0.57 6.57 5.84 0.726 6.82 -0.250 -0.360 0.107 -0.485 -0.0809 1.75
#> # ℹ 129 more rows
#> # ℹ 4 more variables: cl <dbl>, v <dbl>, tad <dbl>, dosenum <dbl>
# }