plotCombined: Plot a list of model predictions and a list of data points in...

In dMod: Dynamic Modeling and Parameter Estimation in ODE Models

Description

Plot a list of model predictions and a list of data points in a combined plot

Usage

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

Details

The data.frame being plotted has columns time, value, sigma, name and condition.

Value

A plot object of class ggplot.

Examples

    
    ## 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

dMod documentation built on Jan. 27, 2021, 1:07 a.m.