# Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object ```r ## S3 method for class 'mixed.mmkin' plot( x, i = 1:ncol(x$mmkin), obs_vars = names(x$mkinmod$map), standardized = TRUE, covariates = NULL, covariate_quantiles = c(0.5, 0.05, 0.95), xlab = "Time", xlim = range(x$data$time), resplot = c("predicted", "time"), pop_curves = "auto", pred_over = NULL, test_log_parms = FALSE, conf.level = 0.6, default_log_parms = NA, 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, ... ) ``` ## Arguments - `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 `resplot = "time"`. - `covariates`: Data frame with covariate values for all variables in any covariate models in the object. If given, it overrides 'covariate_quantiles'. Each line in the data frame will result in a line drawn for the population. Rownames are used in the legend to label the lines. - `covariate_quantiles`: This argument only has an effect if the fitted object has covariate models. If so, the default is to show three population curves, for the 5th percentile, the 50th percentile and the 95th percentile of the covariate values used for fitting the model. - `xlab`: Label for the x axis. - `xlim`: Plot range in x direction. - `resplot`: Should the residuals plotted against time or against predicted values? - `pop_curves`: Per default, one population curve is drawn in case population parameters are fitted by the model, e.g. for saem objects. In case there is a covariate model, the behaviour depends on the value of 'covariates' - `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 - `default_log_parms`: 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 `plot`. ## Returns The function is called for its side effect. ## Note Covariate models are currently only supported for saem.mmkin objects. ## Examples ```r 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) ``` ## Author(s) Johannes Ranke

plot.mixed.mmkin function