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

`ODEmorris.ODEnetwork`

performs a sensitivity analysis for objects of
class `ODEnetwork`

using the Morris screening method.
Package `ODEnetwork`

is required for this function to work.

1 2 3 4 5 |

`mod` |
[ |

`pars` |
[ |

`times` |
[ |

`binf` |
[ |

`bsup` |
[ |

`r` |
[ |

`design` |
[ |

`scale` |
[ |

`ode_method` |
[ |

`parallel_eval` |
[ |

`parallel_eval_ncores` |
[ |

`...` |
further arguments passed to or from other methods. |

If the object of class `ODEnetwork`

supplied for `mod`

doesn't
include any events, the solution of the ODE network is determined
analytically using `simuNetwork`

. In the presence
of events, `simuNetwork`

uses
`ode`

to solve the ODE network numerically.

The sensitivity analysis is done for all state variables and all
timepoints simultaneously using `morris`

from the
package `sensitivity`

.

For non-ODE models, values for `r`

are typically between 10 and 50.
However, much higher values are recommended for ODE models (the default is
`r = 500`

).

List of class `ODEmorris`

of length `2 * nrow(mod$state)`

containing in each element a matrix for one state variable (all components
of the 2 state variables are analyzed independently). The matrices
themselves contain the Morris screening results for all timepoints (rows:
`mu, mu.star`

and `sigma`

for every parameter; columns:
timepoints).

In situations where the solution of the ODE model has to be determined
numerically, it might be helpful to try another ODE-solver if the
evaluation of the model function takes too long, (argument
`ode_method`

). The `ode_method`

s `"vode"`

, `"bdf"`

,
`"bdf_d"`

, `"adams"`

, `"impAdams"`

and `"impAdams_d"`

might be faster than the default `"lsoda"`

.

If `morris`

throws a warning message stating
"In ... keeping ... repetitions out of ...", try using a bigger number of
`levels`

in the `design`

argument (only possible for OAT design).

Frank Weber

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ```
##### A network of 4 mechanical oscillators connected in a circle #####
# Definition of the network using the package "ODEnetwork":
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)
# The parameters to be included in the sensitivity analysis and their lower
# and upper boundaries:
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))
# Setting of the initial values of the state variables:
lfonet <- setState(lfonet, state1 = rep(2, 4), state2 = rep(0, 4))
# The timepoints of interest:
LFOtimes <- seq(25, 150, by = 2.5)
# Morris screening:
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)
``` |

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