plot.mixed.mmkin {mkin} | R Documentation |
Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object
## S3 method for class 'mixed.mmkin' plot( x, i = 1:ncol(x$mmkin), obs_vars = names(x$mkinmod$map), standardized = TRUE, xlab = "Time", xlim = range(x$data$time), resplot = c("predicted", "time"), pred_over = NULL, test_log_parms = FALSE, conf.level = 0.6, ymax = "auto", maxabs = "auto", ncol.legend = ifelse(length(i) <= 3, length(i) + 1, ifelse(length(i) <= 8, 3, 4)), nrow.legend = ceiling((length(i) + 1)/ncol.legend), rel.height.legend = 0.02 + 0.07 * nrow.legend, rel.height.bottom = 1.1, pch_ds = 1:length(i), col_ds = pch_ds + 1, lty_ds = col_ds, frame = TRUE, ... )
x |
An object of class mixed.mmkin, saem.mmkin or nlme.mmkin |
i |
A numeric index to select datasets for which to plot the individual predictions, in case plots get too large |
obs_vars |
A character vector of names of the observed variables for which the data and the model should be plotted. Defauls to all observed variables in the model. |
standardized |
Should the residuals be standardized? Only takes effect if
|
xlab |
Label for the x axis. |
xlim |
Plot range in x direction. |
resplot |
Should the residuals plotted against time or against predicted values? |
pred_over |
Named list of alternative predictions as obtained from mkinpredict with a compatible mkinmod. |
test_log_parms |
Passed to mean_degparms in the case of an mixed.mmkin object |
conf.level |
Passed to mean_degparms in the case of an mixed.mmkin object |
ymax |
Vector of maximum y axis values |
maxabs |
Maximum absolute value of the residuals. This is used for the scaling of the y axis and defaults to "auto". |
ncol.legend |
Number of columns to use in the legend |
nrow.legend |
Number of rows to use in the legend |
rel.height.legend |
The relative height of the legend shown on top |
rel.height.bottom |
The relative height of the bottom plot row |
pch_ds |
Symbols to be used for plotting the data. |
col_ds |
Colors used for plotting the observed data and the corresponding model prediction lines for the different datasets. |
lty_ds |
Line types to be used for the model predictions. |
frame |
Should a frame be drawn around the plots? |
... |
Further arguments passed to |
The function is called for its side effect.
Johannes Ranke
ds <- lapply(experimental_data_for_UBA_2019[6:10], function(x) x$data[c("name", "time", "value")]) names(ds) <- paste0("ds ", 6:10) dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"), A1 = mkinsub("SFO"), quiet = TRUE) ## Not run: f <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE) plot(f[, 3:4], standardized = TRUE) # 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$bparms.optim[-1], c(parent = f_nlme$bparms.optim[[1]], A1 = 0), seq(0, 180, by = 0.2)) plot(f_saem, pred_over = list(nlme = pred_nlme)) ## End(Not run)