Look into R's scoping rules. E.g.,
https://bookdown.org/rdpeng/rprogdatascience/scoping-rules-of-r.html.
* When a function looks up a name, it looks it up in the environment in
which the function was defined.
* Functions in a package are generally defined in the package's environment
(although
dear members,
I am using the RSelenium package which uses the
function selenium() from the wdman package. The selenium function contains the
function java_check at line 12. If I try to run it, it throws an error:
> javapath <- java_check()
Error in java_check() :
Muchas gracias Carlos y Manuel por el aporte, me ayudó bastante.
On Thu, 19 Jan 2023 at 04:12, Carlos Ortega
wrote:
> Hola,
>
> Sí, he cambiado de forma de hacerlo, así lo tienes directo en un
> data.table...
> Y sale el 2000 que no sé porqué no salía antes.
>
>
If the equations are in the form shown in your post then take the log of
both sides, expand the logs and replace log(whatever) with new variables so
now the equations are in linear form and are easy to solve.
__
R-help@r-project.org mailing list -- To
Dear Troels,
Are you aware of the caracas package for computer algebra in R? The package
provides an interface to the SymPy package in python via the reticulate package.
I have no idea if the package can be helpful in your connection, but please
report succeses and failures back.
Best regards
There's at least one package that can do zero-inflated gamma regression
(Rfast2::zigamma). I'm not sure it's ML, though.
On Thu, Jan 19, 2023 at 10:17 AM Jeff Newmiller
wrote:
> Beware of adding a constant... the magnitude of the constant used can have
> an outsized impact on the answer
If you need to solve a nonlinear system of equations you could have a look at
the
CRAN Task View: Numerical Mathematics:
https://cran.r-project.org/view=NumericalMathematics
Specifically look in the subsection "Root Finding and Fixed Points".
Berend Hasselman
> On 19 Jan 2023, at 10:41,
Another situation for the presence of 0 is about dosage when
concentration is below the detection limit. It is not necessary to
discretize the data. We propose a method here:
Salvat-Leal I, Cortés-Gómez AA, Romero D, Girondot M (2022) New method
for imputation of unquantifiable values using
Hi Jeff - that is definitely not fair, this is a highly respected
scientist but old me are probably not clever enough to explain problem
which I think is not too difficult. Basically, it is a series of
consecutive statements of H and Mg and K combining to ATP and ADP,
creatin and creatinP
Beware of adding a constant... the magnitude of the constant used can have an
outsized impact on the answer obtained. See e.g.
https://gist.github.com/jdnewmil/99301a88de702ad2fcbaef33326b08b4
On January 19, 2023 3:49:29 AM PST, peter dalgaard wrote:
>Not necessarily homework, Bert. There's a
A crude but often informative approach is to treat the nonlinear equations as a
nonlinear least squared problem. This is NOT a generally recommended solution
technique,
but can help to show some of the multiple solutions. Moreover, it forces some
attention
to the problem. Unfortunately, it
But it is simultaneously an example of why some researchers like black box
solvers... a system of dozens of nonlinear equations can potentially have many
or even infinite solutions. If the researcher is weak in math, they may have no
idea which solutions are possible and having a tool like
Hi Tim - the results from this paper have had big impact on quite
complicated problems of physiology so I thought I might like to
understand how they came by, calculating them myself. Writing up the
statements from which the inferences in the paper were generated using
Mathematica were meant
This is a poster child for why we like open source software. "I dump numbers
into a black box and get numbers out but I cannot verify how the numbers out
were calculated so they must be correct" approach to analysis does not really
work for me.
Tim
-Original Message-
From: R-help On
Thanks, Valentin for the suggestion. I'm not sure I can go that way. I
include below the statements from the paper containing the knowledge on
the basis of which I would like to know at specified [H] the
concentration of each of the many metabolites given the constraints. I
have tried to
Gracias Carlos
te cuento que el factor debe multiplicar a la columna
en lo que envías ... está multiplicando a cada fila consecutivamente
le metí un poco de cabeza y logré hacerlo con furrr::future_imap
Hello Troels,
As fair as I understand you attempt to numerically solve a system of non linear
equations
in multiple variables in R. R does not provide this functionality natively, but
have you tried
multiroot from the rootSolve package:
To all users of the rgl package:
CRAN has just accepted an update of rgl to version 1.0.1.
This release deprecates a number of functions which have been in the
package for a very long time, and this may affect your scripts. Here
are the news items about it:
* The long promised
Not necessarily homework, Bert. There's a generic issue with MLE and rounded
data, in that gamma densities may be 0 at the boundary but small numbers are
represented as 0, making the log-likelihood -Inf.
The cleanest way out is to switch to a discretized distribution in the
likelihood, so
Hi friends - I hope this is not a misplaced question. From the
literature (Kushmerick AJP 1997;272:C1739-C1747) I have a series of
Mathematica equations which are solved together to yield over different
pH values the concentrations of metabolites in skeletal muscle using the
Mathematica
Buenas,
Creo que el siguiente código hace lo que pides, cambiando Chile y las columnas
por los de tu caso.
library(carData)
data(Chile)
indice <- sort(unique(Chile$population))
f <- function(valor, data, indice) {
sum(data[indice == valor], na.rm = TRUE)
}
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