plot.ODEmorris: Plot of the Results of Morris Screening for Objects of Class...
In surmann/ODEsensitivity: Sensitivity Analysis of Ordinary Differential Equations

Description Usage Arguments Details Value Note Author(s) See Also Examples

plot.ODEmorris plots the results of Morris screening for objects of class ODEmorris.

## S3 method for class 'ODEmorris'
plot(x, pars_plot = NULL, state_plot = names(x)[1],
  kind = "sep", colors_pars = NULL, main_title = NULL,
  legendPos = "outside", type = "l", ...)

`x`	[`ODEmorris`] output of `ODEmorris` (of class `ODEmorris`).
`pars_plot`	[`character(k)`] names of the `k` parameters to be plotted. If `NULL` (the default), all parameters are plotted.
`state_plot`	[`character(1)`] name of the state variable to be plotted. Defaults to the name of the first state variable.
`kind`	[`character(1)`] kind of the plot, choose between `"sep"` and `"trajec"` (see details).
`colors_pars`	[`character(>= k)`] vector of the colors to be used for the `k` different parameters. Must be at least of length `k` (only the first `k` elements will be used, though). If `NULL` (the default), `rainbow(k)` is used.
`main_title`	[`character(1)`] title for the plot. If `kind = "sep"`, this is the overall title for the two separate plots. If `NULL` (the default), a standard title is generated.
`legendPos`	[`character(1)`] keyword for the legend position, either one of those specified in `legend` or `"outside"` (the default), which means the legend is placed under the plot (useful, if there are many parameters in the model).
`type`	[`character(1)`] plot type, i.e. `"p", "l", "b", "c", "o", "s", "h"` or `"n"`. Defaults to `"l"`.
`...`	additional arguments passed to `plot.default`.

Morris sensitivity indices are plotted for one state variable (chosen by argument state_plot) and the parameters named in pars_plot. If no parameters are named in pars_plot, the sensitivity indices for all parameters are plotted. There are two kinds of plots:

kind = "sep": separate plots of the Morris sensitivity indices μ* and σ against time
kind = "trajec": plot of μ* against σ

TRUE (invisible; for testing purposes).

Not all plotting arguments can be passed by ..., for example xlab and ylab are fixed.

Stefan Theers, Frank Weber

ODEmorris, morris

##### Lotka-Volterra equations #####
LVmod <- function(Time, State, Pars) {
  with(as.list(c(State, Pars)), {
    Ingestion    <- rIng  * Prey * Predator
    GrowthPrey   <- rGrow * Prey * (1 - Prey/K)
    MortPredator <- rMort * Predator
    
    dPrey        <- GrowthPrey - Ingestion
    dPredator    <- Ingestion * assEff - MortPredator
    
    return(list(c(dPrey, dPredator)))
  })
}
LVpars  <- c("rIng", "rGrow", "rMort", "assEff", "K")
LVbinf <- c(0.05, 0.05, 0.05, 0.05, 1)
LVbsup <- c(1.00, 3.00, 0.95, 0.95, 20)
LVinit  <- c(Prey = 1, Predator = 2)
LVtimes <- c(0.01, seq(1, 50, by = 1))
set.seed(7292)
# Warning: The following code might take very long!

LVres_morris <- ODEmorris(mod = LVmod,
                          pars = LVpars,
                          state_init = LVinit,
                          times = LVtimes,
                          binf = LVbinf,
                          bsup = LVbsup,
                          r = 500,
                          design = list(type = "oat", 
                                        levels = 10, grid.jump = 1),
                          scale = TRUE,
                          ode_method = "lsoda",
                          parallel_eval = TRUE,
                          parallel_eval_ncores = 2)
my_cols <- c("firebrick", "orange2", "dodgerblue", 
             "forestgreen", "black")
plot(LVres_morris, kind = "sep", colors_pars = my_cols)
plot(LVres_morris, pars_plot = c("rGrow", "rMort"), state_plot = "Predator", 
     kind = "trajec", colors_pars = my_cols[2:3])


##### A network of 4 mechanical oscillators connected in a circle #####
M_mat <- rep(2, 4)
K_mat <- diag(rep(2 * (2*pi*0.17)^2, 4))
K_mat[1, 2] <- K_mat[2, 3] <- 
  K_mat[3, 4] <- K_mat[1, 4] <- 2 * (2*pi*0.17)^2 / 10
D_mat <- diag(rep(0.05, 4))
library("ODEnetwork")
lfonet <- ODEnetwork(masses = M_mat, dampers = D_mat, springs = K_mat)
LFOpars <- c("k.1", "k.2", "k.3", "k.4",
             "d.1", "d.2", "d.3", "d.4")
LFObinf <- c(rep(0.2, 4), rep(0.01, 4))
LFObsup <- c(rep(20, 4), rep(0.1, 4))
lfonet <- setState(lfonet, state1 = rep(2, 4), state2 = rep(0, 4))
LFOtimes <- seq(25, 150, by = 2.5)
set.seed(283)
# Warning: The following code might take very long!

LFOres_morris <- ODEmorris(mod = lfonet,
                           pars = LFOpars,
                           times = LFOtimes,
                           binf = LFObinf,
                           bsup = LFObsup,
                           r = 500,
                           design = list(type = "oat", 
                                         levels = 10, grid.jump = 1),
                           scale = TRUE,
                           parallel_eval = TRUE,
                           parallel_eval_ncores = 2)
plot(LFOres_morris, pars_plot = paste0("k.", 1:4), state_plot = "x.2",
     kind = "sep", colors_pars = my_cols)