# Plot a list of model predictions and a list of data points in a combined plot ```r plotCombined(prediction, ...) ## S3 method for class 'prdlist' plot(x, data = NULL, ..., scales = "free", facet = "wrap", transform = NULL) ## S3 method for class 'prdlist' plotCombined( prediction, data = NULL, ..., scales = "free", facet = "wrap", transform = NULL, aesthetics = NULL ) ## S3 method for class 'prdframe' plot(x, data = NULL, ..., scales = "free", facet = "wrap", transform = NULL) ``` ## Arguments - `prediction`: Named list of matrices or data.frames, usually the output of a prediction function as generated by Xs . - `...`: Further arguments going to `dplyr::filter`. - `x`: prediction - `data`: Named list of data.frames as being used in res , i.e. with columns `name`, `time`, `value` and `sigma`. - `scales`: The scales argument of `facet_wrap` or `facet_grid`, i.e. `"free"`, `"fixed"`, `"free_x"` or `"free_y"` - `facet`: `"wrap"` or `"grid"`. Try `"wrap_plain"` for high amounts of conditions and low amounts of observables. - `transform`: list of transformation for the states, see coordTransform . - `aesthetics`: Named list of aesthetic mappings, specified as character, e.g. `list(linetype = "name")`. Can refer to variables in the condition.grid ## Returns A plot object of class `ggplot`. ## Details The data.frame being plotted has columns `time`, `value`, `sigma`, `name` and `condition`. ## Examples ```r ## Observation function fn <- eqnvec( sine = "1 + sin(6.28*omega*time)", cosine = "cos(6.28*omega*time)" ) g <- Y(fn, parameters = "omega") ## Prediction function for time x <- Xt() ## Parameter transformations to split conditions p <- NULL for (i in 1:3) { p <- p + P(trafo = c(omega = paste0("omega_", i)), condition = paste0("frequency_", i)) } ## Evaluate prediction times <- seq(0, 1, .01) pars <- structure(seq(1, 2, length.out = 3), names = attr(p, "parameters")) prediction <- (g*x*p)(times, pars) ## Plotting prediction # plot(prediction) plotPrediction(prediction) plotPrediction(prediction, scales = "fixed") plotPrediction(prediction, facet = "grid") plotPrediction(prediction, scales = "fixed", transform = list(sine = "x^2", cosine = "x - 1")) ## Simulate data dataset <- wide2long(prediction) dataset <- dataset[seq(1, nrow(dataset), 5),] set.seed(1) dataset$value <- dataset$value + rnorm(nrow(dataset), 0, .1) dataset$sigma <- 0.1 data <- as.datalist(dataset, split.by = "condition") ## Plotting data # plot(data) plot1 <- plotData(data) plot1 ## Plotting data and prediction with subsetting # plot(prediction, data) plot2 <- plotCombined(prediction, data) plot2 plot3 <- plotCombined(prediction, data, time <= 0.5 & condition == "frequency_1") plot3 plot4 <- plotCombined(prediction, data, time <= 0.5 & condition != "frequency_1", facet = "grid") plot4 plot5 <- plotCombined(prediction, data, aesthetics = list(linetype = "condition")) plot5 ```

plotCombined function