# [R] Error in chol.default

J C Nash pro|jcn@@h @end|ng |rom gm@||@com
Thu Jul 16 19:09:41 CEST 2020

```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 (derivatives of the residual function w.r.t. the parameters)
is singular -- rather spectacularly so.

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
# values
Y <- c(41,   41,   41,   41,   41,   41,   45,   62,  121,  198,  275,
288,  859, 1118)
X <- 1:14
a = 1
k = 83347
r = 40
fit = rich(c(curvature=a, finalPop=k, growthRate=r), X)
plot(Y ~ X,
col = "red", lwd = 2,
main = "Richards model",
xlab = expression(bold("Days")),
ylab = expression(bold("Cases")))
points(X, fit, type = "l", lty = 2, lwd = 2)
library("minpack.lm")
o <- nls.lm(par = list(a=a, k=k, r=r), fn = ricky, x = X, y = Y)
summary(o)
print(o1)
library(nlsr)
xy=data.frame(x=X, y=Y)
rcky2 = y ~ r * x * (1-(x/k)^a) -y
o1 <- nlxb(rcky2, start = c(a=a, k=k, r=r), data=xy, trace=TRUE)

You should find

> o1
nlsr object: x
residual sumsquares =  3161769  on  14 observations
after  8    Jacobian and  13 function evaluations
name            coeff          SE       tstat      pval      gradient    JSingval
a                294.113            NA         NA         NA           0       31.86
k                  83347            NA         NA         NA           0           0
r                74.9576            NA         NA         NA  -6.064e-05           0
>
Note the singular values of the Jacobian. Actual zeros!

Even so, nlsr had managed to make some progress.

nls() and nls.lm() use approximate derivatives. Often that's fine (and it is more flexible
in handling awkward functions), but a lot of the time it is NOT OK.

JN

```