I'm trying to fit the differential equation using the least squares method (FME package). However, I keep getting this error that I don't know how to tackle.
The reproducible example:
times = seq(0, 4, by = 0.5)
dat = data.frame(time = seq(1,4),
Tick = c(128, 52.5, 28, 121))
N = 10
tick.model <- function(time, y, params, ...) { #here we begin a function with three arguments
with(as.list(c(y, params)),{
dTick <- (30 - s.t*Tick)*Tick*0.3*N - delta.t*Tick
return(list(c(dTick)))
})
}
y = c(Tick = 82.375)
cost1 <- function(p) {
out <- ode(y, times, tick.model, p)
modCost(out, dat, weight = "none")
}
params <- c(s.t=0.1, delta.t = 1)
fit = modFit(f = cost1, p = params, lower = rep(0,2),
upper = c(10, 5))
summary(fit)
The result comes out like this:
Parameters:
Estimate Std. Error t value Pr(>|t|)
s.t 0.3641876 NA NA NA
delta.t 0.0001417 NA NA NA
Residual standard error: 60.92 on 2 degrees of freedom
Error in cov2cor(x$cov.unscaled) : 'V' is not a square numeric matrix
In addition: Warning message:
In summary.modFit(fit) : Cannot estimate covariance; system is singular
Also, the fitted model doesn't look nice
.
I have no idea what I could have done wrong.
