plot the sensitivity matrix

Produce a cumulative shaded area plot for the sensitivity matrix. This function is intended for use with many observables, e.g. the state of the model at several given times. The x-axis of the plot is meant to be continuous. This will not produce a bar-chart, but a graph that shows how sensitivities change between farily similar observables.

Usage

sensitivity.graph(
  u,
  S,
  color = hcl.colors(dim(S)[2]),
  line.color = hcl.colors(dim(S)[2] + 1),
  do.sort = TRUE,
  decreasing = FALSE,
  ...
)

Arguments

u

the values of the x-axis for the plot, if named, the names are put at the tick-marks

S

the sensitivity matrix as returned by globalSensitivity(), S\[i,j\] is with respect to model output i and parameter j

color

the list of colors to use for the shaded areas, e.g.: rainbow(24)

line.color

the color of the lines drawn between the shaded areas

do.sort

the parameter sensitivities are sorted according to the mean over all outputs, the parameter with the most sensitivity is plotted first, at the bottom

decreasing

direction of sort, the first item in the sorted list (the parameter) will be plotted first, and thus at the bottom of the plot

...

passed on to plot

Value

nothing

Examples

  rprior <- rNormalPrior(c(-1,0,1),c(1,2,3))
  X <- rprior(10000)
  colnames(X) <- LETTERS[seq(3)]
  Z <- exp(
    cbind(
      rowSums(X),
      rowMeans(X),
      exp(X[,1])
    )
  )
  colnames(Z) <- c("sum","mean","exp1")
  GSA <- gsa_binning(X,Z)
  print(GSA)
#>                 A           B            C
#> sum  0.0007854606 0.002571978 0.9420698722
#> mean 0.0309862707 0.139056175 0.5386581569
#> exp1 0.9998999990 0.001062760 0.0002820863
  sensitivity.graph(c(sum=1,mean=2,exp1=3),GSA)

#> [1] 2