This returns a new fit object with CWRES attached
Usage
addCwres(fit, focei = TRUE, updateObject = TRUE, envir = parent.frame(1))Arguments
- fit
nlmixr2 fit without WRES/CWRES
- focei
Boolean indicating if the focei objective function is added. If not the foce objective function is added.
- updateObject
Boolean indicating if the original fit object should be updated. By default this is true.
- envir
Environment that should be checked for object to update. By default this is the global environment.
Examples
# \donttest{
one.cmt <- function() {
ini({
## You may label each parameter with a comment
tka <- 0.45 # Log 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)
})
}
f <- try(nlmixr2(one.cmt, theo_sd, "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
#> using C compiler: ‘gcc (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0’
#> params: tka tcl tv V(eta.ka) V(eta.cl) V(eta.v) add.sd
#> rxode2 2.0.13.9000 using 1 threads (see ?getRxThreads)
#> no cache: create with `rxCreateCache()`
#> 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
#>
#>
#> using C compiler: ‘gcc (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0’
#> → Calculating residuals/tables
#> ✔ done
#> → compress origData in nlmixr2 object, save 5952
#> → compress phiM in nlmixr2 object, save 63664
#> → compress parHist in nlmixr2 object, save 9544
#> → compress saem0 in nlmixr2 object, save 29088
print(f)
#> ── nlmixr² SAEM OBJF by FOCEi approximation ──
#>
#> Gaussian/Laplacian Likelihoods: AIC() or $objf etc.
#> FOCEi CWRES & Likelihoods: addCwres()
#>
#> ── Time (sec $time): ──
#>
#> setup covariance saem table compress other
#> elapsed 0.001579 0.010007 1.577 0.052 0.027 2.973414
#>
#> ── Population Parameters ($parFixed or $parFixedDf): ──
#>
#> Parameter Est. SE %RSE Back-transformed(95%CI) BSV(CV%) Shrink(SD)%
#> tka Log Ka 0.453 0.195 43.1 1.57 (1.07, 2.31) 71.4 -0.445%
#> tcl Log Cl 1.02 0.0843 8.29 2.76 (2.34, 3.26) 27.2 3.88%
#> tv log V 3.45 0.0467 1.35 31.5 (28.8, 34.5) 13.9 10.2%
#> add.sd 0.695 0.695
#>
#> Covariance Type ($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>
# even though you may have forgotten to add the cwres, you can add it to the data.frame:
if (!inherits(f, "try-error")) {
f <- try(addCwres(f))
print(f)
}
#>
#>
#> → loading into symengine environment...
#> → pruning branches (`if`/`else`) of full model...
#> ✔ done
#> → calculate jacobian
#> → calculate ∂(f)/∂(η)
#> → calculate ∂(R²)/∂(η)
#> → finding duplicate expressions in inner model...
#> → optimizing duplicate expressions in inner model...
#> → finding duplicate expressions in EBE model...
#> → optimizing duplicate expressions in EBE model...
#> → compiling inner model...
#>
#>
#> using C compiler: ‘gcc (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0’
#> ✔ done
#> → finding duplicate expressions in FD model...
#> → compiling EBE model...
#>
#>
#> using C compiler: ‘gcc (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0’
#> ✔ done
#> → compiling events FD model...
#>
#>
#> using C compiler: ‘gcc (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0’
#> ✔ done
#> → Calculating residuals/tables
#> ✔ done
#> ── nlmixr² SAEM OBJF by FOCEi approximation ──
#>
#> OBJF AIC BIC Log-likelihood Condition#(Cov) Condition#(Cor)
#> FOCEi 116.9949 373.5947 393.7743 -179.7973 18.15903 1.404028
#>
#> ── Time (sec $time): ──
#>
#> setup covariance saem table compress other
#> elapsed 0.001579 0.010007 1.577 0.052 0.027 2.973414
#>
#> ── Population Parameters ($parFixed or $parFixedDf): ──
#>
#> Parameter Est. SE %RSE Back-transformed(95%CI) BSV(CV%) Shrink(SD)%
#> tka Log Ka 0.453 0.195 43.1 1.57 (1.07, 2.31) 71.4 -0.445%
#> tcl Log Cl 1.02 0.0843 8.29 2.76 (2.34, 3.26) 27.2 3.88%
#> tv log V 3.45 0.0467 1.35 31.5 (28.8, 34.5) 13.9 10.2%
#> add.sd 0.695 0.695
#>
#> Covariance Type ($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 × 20
#> 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 8 more variables: cl <dbl>, v <dbl>, tad <dbl>, dosenum <dbl>, WRES <dbl>,
#> # CPRED <dbl>, CRES <dbl>, CWRES <dbl>
# Note this also adds the FOCEi objective function
# }