The error msg says it all if you know how to read it.
> When I run the optimization (given that I can't find parameters that
> fit the data by eyeball), I get the error:
> ```
> Error in chol.default(object$hessian) :
> the leading minor of order 1 is not positive definite
Your Jacobian (deriv
Hello,
I am trying to fit a Richards model to some cumulative incidence data
of infection. I got this example:
```
rich = function(p, x) {
a = p["curvature"]
k = p["finalPop"]
r = p["growthRate"]
y = r * x * (1-(x/k)^a)
return(y)
}
ricky = function(p, x, y) p$r * x * (1-(x/p$k)^p$a) -y
#
> On May 5, 2018, at 1:19 AM, Troels Ring wrote:
>
> Dear friends - I'm having troubles with nlme fitting a simplified model as
> shown below eliciting the error
>
> Error in chol.default((value + t(value))/2) :
> the leading minor of order 1 is not positive definite -
>
> I have seen the t
Dear friends - I'm having troubles with nlme fitting a simplified model
as shown below eliciting the error
Error in chol.default((value + t(value))/2) :
the leading minor of order 1 is not positive definite -
I have seen the threads on this error but it didn't help me solve the
problem.
Th
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