# jacobian.full: Full square jacobian matrix for a system of ODEs (ordinary... In rootSolve: Nonlinear Root Finding, Equilibrium and Steady-State Analysis of Ordinary Differential Equations

## Description

Given a vector of (state) variables, and a function that estimates one function value for each (state) variable (e.g. the rate of change), estimates the Jacobian matrix (d(f(x))/d(x))

Assumes a full and square Jacobian matrix

## Usage

 ```1 2``` ```jacobian.full(y, func, dy = NULL, time = 0, parms = NULL, pert = 1e-8, ...) ```

## Arguments

 `y ` (state) variables, a vector; if `y` has a name attribute, the names will be used to label the Jacobian matrix columns. `func ` function that calculates one function value for each element of `y`; if an ODE system, `func` calculates the rate of change (see details). `dy ` reference function value; if not specified, it will be estimated by calling `func`. `time ` time, passed to function `func`. `parms ` parameter values, passed to function `func`. `pert ` numerical perturbation factor; increase depending on precision of model solution. `... ` other arguments passed to function `func`.

## Details

The function `func` that estimates the rate of change of the state variables has to be consistent with functions called from R-package `deSolve`, which contains integration routines.

This function call is as: function(time,y,parms,...) where

• `y` : (state) variable values at which the Jacobian is estimated.

• `parms`: parameter vector - need not be used.

• `time`: time at which the Jacobian is estimated - in general, `time` will not be used.

• `...`: (optional) any other arguments.

The Jacobian is estimated numerically, by perturbing the x-values.

## Value

The square jacobian matrix; the elements on i-th row and j-th column are given by: d(f(x)_i)/d(x_j)

## Note

This function is useful for stability analysis of ODEs, which start by estimating the Jacobian at equilibrium points. The type of equilibrium then depends on the eigenvalue of the Jacobian.

## Author(s)

Karline Soetaert <[email protected]>

`jacobian.band`, estimates the Jacobian matrix assuming a banded structure.

`hessian`, estimates the Hessian matrix.

`gradient`, for a full (not necessarily square) gradient matrix and where the function call is simpler.

`uniroot.all`, to solve for all roots of one (nonlinear) equation

`multiroot`, to solve n roots of n (nonlinear) equations

## Examples

 ``` 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 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91``` ```## ======================================================================= ## 1. Structure of the Jacobian ## ======================================================================= mod <- function (t = 0, y, parms = NULL,...) { dy1<- y[1] + 2*y[2] dy2<-3*y[1] + 4*y[2] + 5*y[3] dy3<- 6*y[2] + 7*y[3] + 8*y[4] dy4<- 9*y[3] +10*y[4] return(as.list(c(dy1, dy2, dy3, dy4))) } jacobian.full(y = c(1, 2, 3, 4), func = mod) ## ======================================================================= ## 2. Stability properties of a physical model ## ======================================================================= coriolis <- function (t, velocity, pars, f) { dvelx <- f*velocity[2] dvely <- -f*velocity[1] list(c(dvelx, dvely)) } # neutral stability; f is coriolis parameter Jac <- jacobian.full(y = c(velx = 0, vely = 0), func = coriolis, parms = NULL, f = 1e-4) print(Jac) eigen(Jac)\$values ## ======================================================================= ## 3. Type of equilibrium ## ======================================================================= ## From Soetaert and Herman (2009). A practical guide to ecological ## modelling. Using R as a simulation platform. Springer eqn <- function (t, state, pars) { with (as.list(c(state, pars)), { dx <- a*x + cc*y dy <- b*y + dd*x list(c(dx, dy)) }) } # stable equilibrium A <- eigen(jacobian.full(y = c(x = 0, y = 0), func = eqn, parms = c(a = -0.1, b = -0.3, cc = 0, dd = 0)))\$values # unstable equilibrium B <- eigen(jacobian.full(y = c(x = 0, y = 0), func = eqn, parms = c(a = 0.2, b = 0.2, cc = 0.0, dd = 0.2)))\$values # saddle point C <- eigen(jacobian.full(y = c(x = 0, y = 0), func = eqn, parms = c(a = -0.1, b = 0.1, cc = 0, dd = 0)))\$values # neutral stability D <- eigen(jacobian.full(y = c(x = 0, y = 0), func = eqn, parms = c(a = 0, b = 0, cc = -0.1, dd = 0.1)))\$values # stable focal point E <- eigen(jacobian.full(y = c(x = 0, y = 0), func = eqn, parms = c(a = 0, b = -0.1, cc = -0.1, dd = 0.1)))\$values # unstable focal point F <- eigen(jacobian.full(y = c(x = 0, y = 0), func=eqn, parms = c(a = 0., b = 0.1, cc = 0.1, dd = -0.1)))\$values data.frame(type = c("stable", "unstable", "saddle", "neutral", "stable focus", "unstable focus"), eigenvalue_1 = c(A[1], B[1], C[1], D[1], E[1], F[1]), eigenvalue_2 = c(A[2], B[2], C[2], D[2], E[2], F[2])) ## ======================================================================= ## 4. Limit cycles ## ======================================================================= ## From Soetaert and Herman (2009). A practical guide to ecological ## modelling. Using R as a simulation platform. Springer eqn2 <- function (t, state, pars) { with (as.list(c(state, pars)), { dx<- a*y + e*x*(x^2+y^2-1) dy<- b*x + f*y*(x^2+y^2-1) list(c(dx, dy)) }) } # stable limit cycle with unstable focus eigen(jacobian.full(c(x = 0, y = 0), eqn2, parms = c(a = -1, b = 1, e = -1, f = -1)))\$values # unstable limit cycle with stable focus eigen(jacobian.full(c(x = 0, y = 0), eqn2, parms = c(a = -1, b = 1, e = 1, f = 1)))\$values ```

rootSolve documentation built on May 29, 2017, 3:28 p.m.