Hi All,
I am glad to share a new R package, named 'ForecastTB'. It is an automated
testbench to compare the performance of forecasting methods for univariate
time series.
The details are as follows:
CRAN: https://CRAN.R-project.org/package=ForecastTB
Ivan Krylov [krylov.r...@gmail.com] said:
> Instead you can either close over X:
>
> X <- c(...)
> holly <- function(p) (p$a * X^2) / (p:b^2 + X^2)
> # function holly now "contains" the vector X
That would not be an accurate statement as written.
The function only contains an unevaluated call
(Adding R-help back to Cc:)
On Tue, 30 Jun 2020 14:44:29 +0200
Luigi Marongiu wrote:
> Ok, I tried with:
> ```
> holly <- function(p) {
> y = (p$a * p$x^2) / (p$b^2 + p$x^2)
> return(y)
> }
> X = 1:60
> A = 3261
> B = 10
> X = c(8, 24, 39, 63, 89, 115, 153, 196, 242, 287,
Addendum:
the optimization actually got a worse outcome than the original
eyeball estimation:
```
actual <- c(8, 24, 39, 63, 89, 115, 153, 196, 242, 287,
344, 408, 473,
546, 619, 705, 794, 891, 999, 1096, 1242, 1363,
1506, 1648, 1753,
1851,
No, I got the same. I reckon the problem is with X: this was I scalar,
I was providing a vector with the actual values.
Ho can mle2 optimize without knowing what are the actual data? and
what values should I give for X?
Thank you
On Tue, Jun 30, 2020 at 2:06 PM Eric Berger wrote:
>
> I have no
I have no problem with the following code:
library(bbmle)
holling <- function( a, b, x ) {
a*x^2 / (b^2 + x^2)
}
A=3261
B=10
X=30
foo <- mle2( minuslogl=holling, start=list(a=A,b=B,x=X) )
foo
# Call:
# mle2(minuslogl = holling, start = list(a = A, b = B, x = X))
# Coefficients:
#a
On Tue, 30 Jun 2020 13:53:10 +0200
Luigi Marongiu wrote:
> function(a, b, x) {
> y = (a * x^2) / (b^2 + x^2)
> return(y)
> }
Take a look at the examples in ?nls.lm. The first argument of the
function must be the parameter list/vector; the rest of the arguments
are passed from ... in nls.lm
Sorry for the typo, but I have the same error if using b instead of h:
```
> O = mle2(minuslogl = holling, start = list(a = A, b = B))
> Error in minuslogl(a = 3261, b = 10) :
argument "x" is missing, with no default
# let's add x
X = c(8, 24, 39, 63, 89, 115, 153, 196, 242, 287,
But I get the same error even if I run:
```
parms = list(a=3261, b=10, x=CH$Cum_Dead[1:60])
O = nls.lm(parms, holling,
lower=NULL, upper=NULL, jac = NULL)
> Error in fn(par, ...) : argument "x" is missing, with no default
```
the function to be optimized is:
```
function(a, b, x) {
y
You create objects A, B, and K, but then use A, B, and X in the call to
nls.lm().
I have no idea if nls.lm is the right tool for your job, but I do know that
you need to use the same names.
Sarah
On Tue, Jun 30, 2020 at 6:01 AM Luigi Marongiu
wrote:
> Hello,
> I am trying to optimize a
Hi Luigi,
I took a quick look.
First error:
You wrote
O = mle2(minuslogl = holling, start = list(a = A, h = B, x = X))
it should be b=B (h is not an argument of holling())
The error message gave very precise information!
Second error:
You wrote
O = mle2(minuslogl = nll, start = list(a = A, h =
Hello,
I am trying to optimize a function with the function nls.lm from the
package minpack.lm. But I can't get it right:
```
A = 3261
B = 10
K = c(8, 24, 39, 63, 89, 115, 153, 196, 242, 287, 344, 408, 473,
546, 619, 705, 794, 891, 999, 1096, 1242, 1363, 1506, 1648,
Hello,
I would like to optimize the function:
```
holling = function(a, b, x) {
y = (a * x^2) / (b^2 + x^2)
return(y)
}
```
I am trying to use the function mle2 from bbmle, but how do I need to
feed the data?
If I give `holling` as function to be optimized, passing the starting
values for `a`,
> PIKAL Petr
> on Thu, 25 Jun 2020 14:45:09 + writes:
> Thanks.
> I try to spread R to some other people and I use 4.0.0 - version.string R
> Under development (unstable) (2020-03-08 r77917) nickname Unsuffered
> Consequences whereas they use R 3.6.3
>
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