similar to: Pasting a list of parameters into a function

The mle2 function (bbmle library) gives an example something like the following in its help page. How do I access the coefficients, standard errors, etc in the summary of "a"? > x <- 0:10 > y <- c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8) > LL <- function(ymax=15, xhalf=6) + -sum(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE)) > a <- mle2(LL,

Hi there I'm trying to find the mle fo a multinomial model ->*L(N,h,S?x)*. There is only *N* I want to estimate, which is used in the number of successes for the last cell probability. These successes are given by: p^(N-x1-x2-...xi) All the other parameters (i.e. h and S) I know from somewhere else. Here is what I've tried to do so far for a imaginary data set:

Hi I have the following formula (I hope it is clear - if no, I can try to do better the next time) h(x, a, b) = integral(0 to pi/2) ( ( integral(D/sin(alpha) to Inf) ( ( f(x, a, b) ) dx ) dalpha ) and I want to do an mle with it. I know how to use mle() and I also know about integrate(). My problem is to give the parameter values a and b to the

I wish to make modifications to the plot.pedigree function in the kinship package. My attempts to contact the maintainer have been unsuccessful, but my question is general, so specifics of the kinship package might not be an issue. My first attempt was to make a new function Plot.pedigree in the .GlobalEnv which mostly achieved what I wanted to. However, I'm sure that's not the tidiest

Hi, I found the following on Windows 2000/NT R Version 1.9.1 (2004-06-21) (also Version 1.9.0): The S4 group "Math" doesn't work as documented; i.e., "log", "log10", "gamma" and "lgamma" are included in the documentation but don't work. See example code below. Moreover, what about 'genericForPrimitive' which is used in

hello, if i load the RSQLite package in addition to the RPostgreSQL package, i get various errors when trying to use RPostgreSQL functions. here is an example transcript showing one such error: ========================================================== R version 2.15.0 (2012-03-30) [...] > packageVersion('RPostgreSQL') [1] 0.3.2 > packageVersion('RSQLite') [1] 0.11.1

Hi, I've noticed an issue with S4 methods and namespaces which only arises in particular, difficult to reproduce configurations. One example is the ggbio package in Bioconductor, which currently emits these warnings when its namespace is loaded: ---------------------- library(ggbio) Loading required package: ggplot2 Attaching package: ‘ggbio’ The following object(s) are masked from

With the exception of "L-BFGS-B", all of the other optim() methods return the value of the function when they are given a trivial function (i.e., one with no variable arguments) to optimize. I don't think this is a "bug" in L-BFGS-B (more like a response to an undefined condition), but it leads to a bug in stats4::mle -- a spurious error saying that a better fit has been

My question has two parts. The first is with regard to the frame or environment in which generic functions are defined in packages. It seems as though they are defined (i.e. exist as objects) in frame 1, even when defined in a package. The following is a short example: setClass("track",representation(x="numeric",y="numeric")) plotTrack <- function(x,y,...)

Hi All, When carrying out hist(samples,breaks=50,probability=TRUE), the column values are considerably greater than 1, which seams very unreasonable. The plot is attached. I think the column value of the hist plot should correspond to x$counts/sum(x$counts) (x=hist(samples,breaks=50,probability=TRUE)). The size of data is a little bit larger, causing failure of uploading. If you need the

Hello: I wish to again express my appreciation to all who have contributed to making R what it is today. At this moment, I'm particularly grateful for whoever modified the 'mle' code so data no longer need be passed via global variables. I remember struggling with this a couple of years ago, and I only today discovered that it is no longer the case. I'd

Hi, How can we set the environment for the minuslog function in mle()? The call in this code fails because the "ll" function cannot find the object 'y'. Modifying from the example in ?mle: library(stats4) ll <- function(ymax=15, xhalf=6) { -sum(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE)) } fit.mle <- function(FUN, x, y) { loglik.fun <- match.fun(FUN)

>>>>> Steve Martin >>>>> on Mon, 18 Dec 2023 07:56:46 -0500 writes: > Does mFUN() really need to be a function of x and the NA values of x? I > can't think of a case where it would be used on anything but the non-NA > values of x. > I think it would be easier to specify a different mFUN() (and document this > new argument)

Does mFUN() really need to be a function of x and the NA values of x? I can't think of a case where it would be used on anything but the non-NA values of x. I think it would be easier to specify a different mFUN() (and document this new argument) if the function has one argument and is applied to the non-NA values of x. zapsmall <- function(x, digits = getOption("digits"),

>>>>> Laurent Gautier >>>>> on Sun, 15 Sep 2019 15:01:09 -0400 writes: > In case a search engine leads someone with the same issue > here, I am documenting the point I reached: > I can reproduce the issue with a small example when > forcing R to not load any package at startup time (using > an Renviron file): ``` package <-

Hi all mailing listers, I wanna get the last several elments of vector. e.g. x <- c(1,2,3,.....,78, 79, 80) How can I implement to assign last three elements to y, y <- c(78, 79, 80) ? In Matlab, It can easily achieve by y=x(end-2:end) Thanks Huaping Wan [[alternative HTML version deleted]]

Forgive me for I do not fully comprehend the idea of classes and methods but I was wondering if someone could help explain why the function args () behaves the way it does. Why does args(cut) show the simplified version instead of the more complete one as in help("cut"). This is true for few other functions (e.g. plot, rep). > args(cut) function (x, ...) Sometime I can get

Hi! This is a repost of an earlier message (with a clearer example demonstrating the problem I ran into). If you run the mle example in stats4 library(stats4) x <- 0:10 y <- c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8) ll <- function(ymax=15, xhalf=6) -sum(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE)) (fit <- mle(ll)) plot(profile(fit),

[posted to R-devel, no discussion: resubmitting it as a bug, just so it gets logged appropriately] Sorry to report further difficulties with nls and profiling and constraints ... the problem this time (which I didn't check for in my last round of testing) is that the nls profiler doesn't seem to respect constraints that have been set when using the port algorithm. See test code

Hi I'm trying to maximize a joint likelihood of 2 likelihoods (Likelihood 1 and Likelihood 2) in mle2, where the parameters I estimate in Likelihood 2 go into the likelihood 1. In Likelihood 1 I estimate the vector logN with length 37, and for the Likelihood 2 I measure a vector s of length 8. The values of s in Lieklihood 2 are used in the Likelihood 1. I have 2 questions: ##1 I manage