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| author | Johannes Ranke <jranke@uni-bremen.de> | 2021-06-09 16:53:31 +0200 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2021-06-09 17:00:41 +0200 |
| commit | c6eb6b2bb598002523c3d34d71b0e4a99671ccd6 (patch) | |
| tree | 7c13470ea01fca6c1cec3749b66a84a17154ec82 /R/plot.mixed.mmkin.R | |
| parent | 9907f17aa98bddfe60e82a71c70a2fea914a02f7 (diff) | |
Rudimentary support for setting up nlmixr models
- All degradation models are specified as ODE models. This appears to be
fast enough
- Error models are being translated to nlmixr as close to the mkin error
model as possible. When using the 'saem' backend, it appears not to be
possible to use the same error model for more than one observed variable
- No support yet for models with parallel formation of metabolites, where
the ilr transformation is used in mkin per default
- There is a bug in nlmixr which appears to be triggered if the data are
not balanced, see nlmixrdevelopment/nlmixr#530
- There is a print and a plot method, the summary method is not finished
Diffstat (limited to 'R/plot.mixed.mmkin.R')
| -rw-r--r-- | R/plot.mixed.mmkin.R | 17 |
1 files changed, 17 insertions, 0 deletions
diff --git a/R/plot.mixed.mmkin.R b/R/plot.mixed.mmkin.R index f0682c10..1ac62b07 100644 --- a/R/plot.mixed.mmkin.R +++ b/R/plot.mixed.mmkin.R @@ -40,12 +40,17 @@ utils::globalVariables("ds") #' #' # For this fit we need to increase pnlsMaxiter, and we increase the #' # tolerance in order to speed up the fit for this example evaluation +#' # It still takes 20 seconds to run #' f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3)) #' plot(f_nlme) #' #' f_saem <- saem(f, transformations = "saemix") #' plot(f_saem) #' +#' f_obs <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE, error_model = "obs") +#' f_nlmix <- nlmix(f_obs) +#' plot(f_nlmix) +#' #' # We can overlay the two variants if we generate predictions #' pred_nlme <- mkinpredict(dfop_sfo, #' f_nlme$bparms.optim[-1], @@ -109,6 +114,18 @@ plot.mixed.mmkin <- function(x, names(degparms_pop) <- degparms_i_names } + if (inherits(x, "nlmixr.mmkin")) { + eta_i <- random.effects(x$nm)[-1] + names(eta_i) <- gsub("^eta.", "", names(eta_i)) + degparms_i <- eta_i + degparms_pop <- x$nm$theta + for (parm_name in names(degparms_i)) { + degparms_i[parm_name] <- eta_i[parm_name] + degparms_pop[parm_name] + } + residual_type = ifelse(standardized, "standardized", "residual") + residuals <- x$data[[residual_type]] + } + degparms_fixed <- fit_1$fixed$value names(degparms_fixed) <- rownames(fit_1$fixed) degparms_all <- cbind(as.matrix(degparms_i), |