Boundary issue triggered when lower and upper bounds are on both sides of zero

[billdenney]

The below estimation inaccurately indicates that tka is at the boundary. The issue is because the bounds are on both sides of zero.

library(nlmixr2)
#> Loading required package: nlmixr2data

one.compartment <- function() {
  ini({
    tka <- c(-6, -4, 2)
    tcl <- 1
    tv <- 3.45
    eta.ka ~ 0.6
    eta.cl ~ 0.3
    eta.v ~ 0.1
    add.sd <- 0.7
  })
  model({
    ka <- exp(tka + eta.ka)*100
    cl <- exp(tcl + eta.cl)
    v <- exp(tv + eta.v)
    d/dt(depot) = -ka * depot
    d/dt(center) = ka * depot - cl / v * center
    cp = center / v
    cp ~ add(add.sd)
  })
}

## The fit is performed by the function nlmixr/nlmix2 specifying the model, data and estimate
fit <- nlmixr2(one.compartment, theo_sd,  est="focei", control = list(print=0))
#> rxode2 2.0.11.9000 using 8 threads (see ?getRxThreads)
#>   no cache: create with `rxCreateCache()`
#> done
#> → Calculating residuals/tables
#> ✔ done
#> → compress origData in nlmixr2 object, save 5952
#> → compress parHist in nlmixr2 object, save 2720
print(fit)
#> ── nlmixr² FOCEi (outer: nlminb) ──
#> 
#>           OBJF      AIC      BIC Log-likelihood
#> FOCEi 116.8089 373.4087 393.5883      -179.7043
#> 
#> ── Time (sec $time): ──
#> 
#>         setup table compress other
#> elapsed 0.017  0.03     0.01 3.113
#> 
#> ── Population Parameters ($parFixed or $parFixedDf): ──
#> 
#>         Est. Back-transformed BSV(CV%) Shrink(SD)%
#> tka    -4.14            0.016     69.7      1.26% 
#> tcl     1.01             2.75     26.7      3.61% 
#> tv      3.46             31.8     13.8      9.95% 
#> add.sd 0.697            0.697                     
#>  
#>   Covariance Type ($covMethod): Boundary issue; Get SEs with `getVarCov()`: "tka" "tka" 
#>   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 $shrink 
#>   Information about run found ($runInfo):
#>    • gradient problems with initial estimate; see $scaleInfo 
#>    • parameter estimate near boundary; covariance not calculated: "tka" "tka" use 'getVarCov' to calculate anyway 
#>    • last objective function was not at minimum, possible problems in optimization 
#>    • ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.)) 
#>    • initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=)) 
#>   Censoring ($censInformation): No censoring
#>   Minimization message ($message):  
#>     false convergence (8) 
#>   In an ODE system, false convergence may mean "useless" evaluations were performed.
#>   See https://tinyurl.com/yyrrwkce
#>   It could also mean the convergence is poor, check results before accepting fit
#>   You may also try a good derivative free optimization:
#>     nlmixr2(...,control=list(outerOpt="bobyqa"))
#> 
#> ── Fit Data (object is a modified tibble): ──
#> # A tibble: 132 × 22
#>   ID     TIME    DV  PRED    RES   WRES IPRED   IRES  IWRES CPRED   CRES  CWRES
#>   <fct> <dbl> <dbl> <dbl>  <dbl>  <dbl> <dbl>  <dbl>  <dbl> <dbl>  <dbl>  <dbl>
#> 1 1      0     0.74  0     0.74   1.06   0     0.74   1.06   0     0.74   1.06 
#> 2 1      0.25  2.84  3.27 -0.434 -0.233  3.85 -1.01  -1.44   3.23 -0.392 -0.185
#> 3 1      0.57  6.57  5.85  0.725  0.293  6.79 -0.215 -0.309  5.79  0.778  0.284
#> # … with 129 more rows, and 10 more variables: eta.ka <dbl>, eta.cl <dbl>,
#> #   eta.v <dbl>, depot <dbl>, center <dbl>, ka <dbl>, cl <dbl>, v <dbl>,
#> #   tad <dbl>, dosenum <dbl>

^{Created on 2023-03-04 with reprex v2.0.2}

[billdenney]

I'm almost done with a PR for this.