similar to: Summing functions of lists

Hello, I am trying to compute MLE for non-Gaussian AR(1). The error term follows a difference poisson distribution. This distribution has one parameter (vector[2]). So in total I want to estimate two parameters: the AR(1) paramter (vector[1]) and the distribution parameter. My function is the negative loglikelihood derived from a mixing operator. f=function(vector)

The algorithm does make a differece. You can use Kahan's summation algorithm (https://en.wikipedia.org/wiki/Kahan_summation_algorithm) to reduce the error compared to the naive summation algorithm. E.g., in R code: naiveSum <- function(x) { s <- 0.0 for(xi in x) s <- s + xi s } kahanSum <- function (x) { s <- 0.0 c <- 0.0 # running compensation for lost

Full_Name: Nick Efthymiou Version: R1.5.0 and above OS: Red Hat Linux Submission from: (NULL) (162.93.14.73) With the introduction of the functions rowSums(), colSums(), rowMeans() and colMeans() in R1.5.0, function "SEXP do_colsum(SEXP call, SEXP op, SEXP args, SEXP rho)" was added to perform the fast summations. We have an excellent opportunity to improve the accuracy by

Dear all, I would be grateful if anyone can help me with the following: My aim is to compute explicitely the sum S=A+B where A=sum(exp(c_i/d)), i=1,...,n; B, c_i, and d are real numbers with -Inf<B,c_i<+Inf; and d>0. The problem is that when c_i/d >710 (for some i) R is setting exp(c_i/d) to be equal to +Inf and hence the whole summation S. So in simple cases where for example c_i=8

Dear R experts I need to write a function that incorporates double summation, the problem being that the upper limit of the second summation is the index of the first summation, i.e: sum_{j=0}^{x} sum_{i=0}^{j} choose(i+j, i) where x variable or constant, doesn't matter. The following code obviously doesn't work: f=function(x) {j=0:x; i=0:j; sum( choose(i+j,i) ) } Can you help? Thanks

Hello, I have to compute a multiple summation (not an integration because the independent variables a are discrete) for all the values of a function of several variables f (x_1,...,x_n), that is sum ... sum f(x_1,...,x_n) x_1 x_n have you some suggestion? Is it possible? I know that for multiple integration there is the function adapt, but it has at most n=20. In my case n depends on the

The chisq.test on line 57 contains following code: STATISTIC <- sum(sort((x - E)^2/E, decreasing = TRUE)) However, based on book "Accuracy and stability of numerical algorithms" available from: http://ftp.demec.ufpr.br/CFD/bibliografia/Higham_2002_Accuracy%20and%20Stability%20of%20Numerical%20Algorithms.pdf Table 4.1 on page 89, it is better to sort the data in increasing order

Probability <- function(N, f, m, b, x, t) { #N is the number of lymph nodes #f is the fraction of Dendritic cells (in the correct node) that have the antigen #m is the number of time steps #b is the starting position (somewhere in the node or somewhere in the gap between nodes. It is a number between 1 and (x+t)) #x is the number of time steps it takes to traverse the gap #t is the number

Hello T I want to order some calculation "result", there will be lots of "result" that need to calculate and order PS: the "result" is just a intermediate varible and ordering them is the very aim # problem: # For fixed NT and CT, and some pair (c,n). order the pair by corresponding result # c and n are both random variable CT<-6000 #assignment to CT

Hi r-users, How do we evaluate the summation of (1/m!) from 0 to infinity (for example). Any help is very much appreciated. Thank you.

Dear R users... I made the R-code for this double summation computation http://www.nabble.com/file/p19213599/doublesum.jpg ------------------------------------------------- Here is my code.. sum(sapply(1:m, function(k){sum(sapply(1:m, function(j){x[k]*x[j]*dnorm((mu[j]+mu[k])/sqrt(sig[k]+sig[j]))/sqrt(sig[k]+sig[j])}))})) ------------------------------------------------- In fact, this is

I would like to code the following in R: a1(b1+b2+b3) + a2(b1+b3+b4) + a3(b1+b2+b4) + a4(b1+b2+b3) or in summation notation: sum_{i=1, j\neq i}^{4} a_i * b_i I realise this is the same as: sum_{i=1, j=1}^{4} a_i * b_i - sum_{i=j} a_i * b_i would appreciate some help. Thank you. -- View this message in context: http://r.789695.n4.nabble.com/summation-coding-tp4646678.html Sent from the R

Dear R users, I try to compute this summation, http://www.nabble.com/file/p25054272/dd.jpg where f(y|x) = Negative Binomial(y, mu=exp(x' beta), size=1/alp) http://www.nabble.com/file/p25054272/aa.jpg http://www.nabble.com/file/p25054272/cc.jpg In fact, I tried to use "do.call" function to compute each u(y,x) before the summation, but I got an error, "Error in X[i, ]

Here's a simple example of the type of function I'm trying to write, where the first argument is a list of functions: myfun <- function(funlist, vec){ tmp <- lapply(funlist, function(x)do.call(x, args = list(vec))) names(tmp) <- names(funlist) tmp } > myfun(list("Summation" = sum, prod, "Absolute value" = abs), c(1, 4, 6, 7)) $Summation [1]

Hi all, I'm trying to model some data where the y is defined by y = summation[1 to 50] B1 * x + B2 * x^2 + B3 * x^3 Hopefully that reads clearly for email. Anyway, if it wasn't for the summation, I know I would do it like this lm(y ~ x + x2 + x3) Where x2 and x3 are x^2 and x^3. However, since each value of x is related to the previous values of x, I don't know how to do this.

Hi all, I have a very quick question on how to use the summation sign in R for the function. Here?s a basic example: the function is sum(i=1 to 5)log(1-xi^2) Id be grateful if someone knows how to do this without writing it out 5 times - I am looking sth along the lines of the following: computeR <- function(x) { return (-sum(log(1-x^2)) }^ thank you vm in advance! -- View this

Dear R users... I made the R-code for this triple summation computation http://www.nabble.com/file/p20517134/a.jpg ------------------------------------------------- Here is my code.. x=seq(.1,1,.1); l=10 y=seq(1,10); m=10 z=seq(.1,1,.1); n=10 sum(sapply(1:l, function(i) {sum(sapply(1:m, function(j) {sum(sapply(1:n, function(k){exp(x[i]*y[j]*z[k] )/gamma(y[j]+1)}))^(1.5) }))}))

There has been a recent thread on R-help on this, which resurrected concepts from bug reports PR#1228 and PR#6743. Since the discussion has included a lot of erroneous 'information' based on misunderstandings of floating-point computations, this is an attempt to set the record straight and explain the solutions adopted. The problem was that var(rep(0.02, 10)) was observed to be (on

Hello, I am trying to write FORTRAN code to do the same as some R code I have. I get (small) differences when using the sum function in R. I know there are numerical routines to improve precision, but I have not been able to figure out what algorithm R is using. Does anyone know this? Or where can I find the code for the sum function? Regards, Rampal Etienne

Dear Will, This is exactly what I find. My point is thus that the sum function in R is not a naive sum nor a Kahansum (in all cases), but what algorithm is it using then? Cheers, Rampal On Tue, Feb 19, 2019, 19:08 William Dunlap <wdunlap at tibco.com wrote: > The algorithm does make a differece. You can use Kahan's summation > algorithm