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-rw-r--r--docs/dev/reference/summary.nlmixr.mmkin.html10
1 files changed, 5 insertions, 5 deletions
diff --git a/docs/dev/reference/summary.nlmixr.mmkin.html b/docs/dev/reference/summary.nlmixr.mmkin.html
index 70a71683..4831bbdf 100644
--- a/docs/dev/reference/summary.nlmixr.mmkin.html
+++ b/docs/dev/reference/summary.nlmixr.mmkin.html
@@ -76,7 +76,7 @@ endpoints such as formation fractions and DT50 values. Optionally
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">mkin</a>
- <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span>
+ <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span>
</span>
</div>
@@ -258,12 +258,12 @@ nlmixr authors for the parts inherited from nlmixr.</p>
quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span>, cores <span class='op'>=</span> <span class='fl'>5</span><span class='op'>)</span>
<span class='va'>f_saemix_dfop_sfo</span> <span class='op'>&lt;-</span> <span class='fu'>mkin</span><span class='fu'>::</span><span class='fu'><a href='saem.html'>saem</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span><span class='op'>)</span>
</div><div class='output co'>#&gt; Running main SAEM algorithm
-#&gt; [1] "Wed Aug 4 16:22:46 2021"
+#&gt; [1] "Thu Sep 16 14:35:21 2021"
#&gt; ....
#&gt; Minimisation finished
-#&gt; [1] "Wed Aug 4 16:22:59 2021"</div><div class='input'><span class='va'>f_nlme_dfop_sfo</span> <span class='op'>&lt;-</span> <span class='fu'>mkin</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span><span class='op'>)</span>
+#&gt; [1] "Thu Sep 16 14:35:33 2021"</div><div class='input'><span class='va'>f_nlme_dfop_sfo</span> <span class='op'>&lt;-</span> <span class='fu'>mkin</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 6, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'>→ generate SAEM model</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_m1</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 1.383 0.12 1.503</span></div><div class='input'><span class='co'># The following takes a very long time but gives</span>
+</div><div class='output co'>#&gt; <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'>→ generate SAEM model</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_m1</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 1.412 0.101 1.525</span></div><div class='input'><span class='co'># The following takes a very long time but gives</span>
<span class='va'>f_nlmixr_dfop_sfo_focei</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ calculate jacobian</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling inner model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>Model:</span></div><div class='output co'>#&gt; <span class='message'>cmt(parent);</span>
#&gt; <span class='message'>cmt(m1);</span>
@@ -323,7 +323,7 @@ nlmixr authors for the parts inherited from nlmixr.</p>
#&gt; <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span>
#&gt; <span class='message'>g=1/(rx_expr_21);</span>
#&gt; <span class='message'>tad=tad();</span>
-#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_m1" "CMT" </span></div><div class='output co'>#&gt; <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper &lt;- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 18.43 0.422 18.87</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f_nlmixr_dfop_sfo_saem</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_dfop_sfo_focei</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span>
+#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_m1" "CMT" </span></div><div class='output co'>#&gt; <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper &lt;- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 18.28 0.455 18.73</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f_nlmixr_dfop_sfo_saem</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_dfop_sfo_focei</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='error'>Error in AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm): object 'f_nlmixr_dfop_sfo_saem' not found</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/summary-methods.html'>summary</a></span><span class='op'>(</span><span class='va'>f_nlmixr_dfop_sfo_sfo</span>, data <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='error'>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'f_nlmixr_dfop_sfo_sfo' not found</span></div><div class='input'><span class='co'># }</span>

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