Unkown state variable error when modelling events using deSolve - r

Question

I am trying to model a disturbance event in a generalized Lotka-Volterra model, where at time t, 1 is added to the variable e. I keep on running into the following error:
Error in checkevents(events, times, Ynames, dllname) :
unknown state variable in 'event': e
My model is the following:
lvg<-function(t, N, e, param){
e <- 0
dNdt <- N * r + N * (a %*% N) - N * e
list(c(dNdt))
}
where N is the population size of species i, r is the growth rate, a is the interaction matrix, and e is the event. r and a are specified as prior parameters, the event is specified in a dataframe. A simplified version is as follows:
#set parameters
S<- 10 # number of species
r <- rep(1.1, S) # growth rates
a <- matrix (nrow = S, ncol = S) #interaction matrix
a[lower.tri(a)] <- -0.001
a[upper.tri(a)] <- -0.001
diag(a) <- -0.01
parms <- list (r, a) #put parameters in a list
N0 <- rep(100, S) #initial values for species abundances
ts<-seq(0, 100, 1) # time steps for solver
#create data frame for event
eventdat <- data.frame(var = c("e", "e"), time = c(10, 20), value = c(1, 1), method = c("add"))
lvout<-lsoda(N0, ts, lvg, parms, events = list(data = eventdat))

Answer 1

Here an approach with an event function instead of an event table, that is in general more flexible in the case here simpler. Note also that the number of states and values of parameters were changed to get a more typical L&V model:
library(deSolve)
## multi-species Lotka-Volterra
lvg <- function(t, N, param) {
with(param, {
dNdt <- r * N + N * (a %*% N)
list(c(dNdt))
})
}
## simplified to 4 species, you can add more
S <- 4
N0 <- c(1,1,1,1)
## parameter list
parms <- list(
r = c(r1 = 0.5, r2 = 0.5, r3 = -0.5, r4 = -0.5),
a = matrix(c(
0.0, 0.0, -0.5, 0.0, # prey 1
0.0, 0.0, 0.0, -0.2, # prey 2
0.5, 0.0, 0.0, 0.0, # predator 1; eats prey 1
0.0, 0.2, 0.0, 0.0), # predator 2; eats prey 2
nrow = 4, ncol = 4, byrow = TRUE),
e = rep(0.5, S)
)
ts <- seq(0, 100, 1) # time steps for solver
te <- c(20, 40) # event times
## event function is more flexible than an event table
eventfun <- function(t, N, param){
with (as.list(param), {
N <- N - N * e
return(c(N))
})
}
## simulation without events
lvout<-lsoda(N0, ts, lvg, parms)
plot(lvout)
## simulation with events
lvout<-lsoda(N0, ts, lvg, parms, events = list(func = eventfun, time = te))
plot(lvout)