search for: summand

Hello, I'd like to take all possible sub-summands of a vector in the quickest and most efficient way possible. By "sub-summands" I mean for each sub-vector, take its sum. Which is to say: if I had the vector x<-1:4 I'd want the "sum" of x[1], x[2], etc. And then the sum of x[1:2], x[2:3], etc. And then...so on. T...

Hello, Can anyone give me some suggestion in term of calculating the sum below. Is there a function in R that can help doing it faster? x1, x2, ...xn where xi can be 0 or 1. I want to calculate the following: sum{ beta[a+sum(xi), b+n-sum(xi) ]* [ (1-x1)dnorm(0,1)+x1dnorm(2,1) ]* [ (1-x2)dnorm(0,1)+x2dnorm(2,1) ]* ...* [ (1-xn)dnorm(0,1)+xndnorm(2,1) ] } The sum in the beginning is over all

...laws with the following proposition: `+.uneval` <- function(e1, e2) { dup <- names(e1) %in% names(e2) if (any(dup)) { duplist <- paste(sQuote(names(e1)[dup]), collapse = ", ") msg <- sprintf(ngettext(length(dup), "element %s occurs in both summands - second one gets precedence", "elements %s occur in both summands - second one gets precedence"), duplist) warning(msg, domain = NA) } res <- c(e1[!dup], e2) class(res) <- "uneval" res } Any thoughts on that? Kind...

Hello I want to calculate natural logarithm of sum of combinations as follow: (R code) { com_sum=choose(2000000,482)*choose(1000000,118)+choose(2000000,483)*choose(1000000,117)+...+choose(2000000,i)*choose(1000000,600-i)+...+choose(2000000,600)*choose(1000000,0) #calculate the sum result=log(com_sum) #calculate the log of the sum } But

Hello I have a time-comsuming program which need to simplify, I have tested the annotated program as follow: > #define function which will be call > calsta <- function(c, n=100000) + { + i <- seq(from=0, length=c) + logx <- lchoose(NT-n, CT-i) + lchoose(n, i) + logmax <- max(logx) + logmax + log(sum(exp(logx - logmax))) + } > CT=6000 #assignment to CT >

...ameters with unscaled data. So the problem is when I try to train the classifier with the option "scale=F" the result is quite poor, so training with scaled data is essential. The rescaling of the support vectors is of course quite an easy task, but what about the parameters coefs and the summand rho? Are they influenced by the optimisation with scaled data (I think they have to...)? And is there any possiblility to rescale these values? I appreciate your help! Greetings! Matthias -- View this message in context: http://r.789695.n4.nabble.com/Scaling-in-SVM-tp3664588p3664588.html Sent from...

...using p/d/qnorm. The tests' primary values follow a bimodal distribution, which is modelled by a mixture of two normal distributions: p * dnorm ((x - u1) / s1) / s1 + (1 - p) * dnorm ((x - u2) / s2) / s2) {written like in Venables & Ripley, 2nd Ed., p. 288}. So, the first and second summand would correspond to truly negative and positive status, respectively, whereas the test would be termed negative or positive with respect to a varying x cut off value. How could one possibly formulate using p/d/qnorm e.g. the specificity: f(x)= number of positive tests at cut off x / total number o...

Hi, I would like to apply the L-BFGS optimization algorithm to compute the MLE of a multilevel multinomial Logistic Regression. The likelihood formula for this model has as one of the summands the formula for computing the likelihood of an ordinary (single-level) multinomial logit regression. So I would basically need the R implementation for this formula. The L-BFGS algorithm also requires computing the partial derivatives of that formula in respect to all parameters. I would appreciat...

...___________________________________________________________ The function `cwilcox` has three inputs: k, m and n. To establish the test statistics `cwilcox` determines the number of possible sums of natural numbers with the following restrictions: - the sum of the elements must be k, - the elements (summands) must be in a decreasing (or increasing) order, - every element must be less then m and - there must be at most n summands. The problem with the evaluation of this function `cwilcox(k,m,n)` is that it requires a recursion where in fact a three-dimensional array has to be evaluated (see the code a...

Could anyone help me with the following issue. Using the GSL library in R I define the following code: ######### library(gsl); S<-function(n) { r<-0:n; ans<-sum(gsl_sf_choose(n,r)*(-1)^r*2^(2*r)*gamma_inc(6-2*r,2)) ans } ######### >SS(10) yields 34.91868 >SS(40) yields 5.340422 >SS(60) yields 180.3162 Doing the same computations in maple I get

...) pair should most probably differ, so I can order them, but > calsta(918,100000)-calsta(718,100000) [1] 0 > calsta(718,90000)-calsta(718,90000) [1] 0 > calsta(718,90000)-calsta(918,100000) [1] 0 # As Eik pointed out in another post, "calsta constantly returns 57003.6 for c>38 (the summands in # sum(exp(logx - logmax)) will become 0 for c>38)" (when n= 10,000) I think there are two ways to solve this problem: 1.Reducing the calcuation formulae algebraically get a conciser kernel for comparing. I think this is the perfect method and I failed many times, though this is not t...

Dear list, I am trying to follow an example that estimates a 2x2 markov transition matrix across several periods from aggregate data using restricted least squares. I seem to be making headway using solve.QP(quadprog) as the unrestricted solution matches the example I am following, and I can specify simple equality and inequality constraints. However, I cannot correctly specify a constraint

...______________ >> The function `cwilcox` has three inputs: k, m and n. To establish the test >> statistics `cwilcox` determines the number of possible sums of natural >> numbers with the following restrictions: >> - the sum of the elements must be k, >> - the elements (summands) must be in a decreasing (or increasing) order, >> - every element must be less then m and >> - there must be at most n summands. >> >> The problem with the evaluation of this function `cwilcox(k,m,n)` is that >> it requires a recursion where in fact a three-dimension...

Dear friends. In Carlin and Louis "Bayes and emperical Bayes methods.." 1996 the classical example of 12 independent tosses of a fair coin producing 9 heads and 3 tails is given. If the situation is seen as a fixed sample of 12, a binomial lieklihood is used, and Carlin et al reports a probability of 0.075. Using sum(dbinom(9:12,12,.5)) I obtain 0.073 Likewise, if the experiment is

...s separate code for the case of multiple response variables. The latter does provide a formula interface, but the interpretation of the formulae is inconvenient. For example - if y1 is an expression containing "+" then it would have to be protected by I() to prevent the summands in the expression being plotted separately. - you can't replace one of the response variables by a constant, as this will be evaluated as a vector of length 1. It's a lot easier with cbind() , for example plot(cbind(z1 -zhat + bias, 0) ~ x) gets you a plot of (z1-zhat...

Folks, This is so simple is driving me crazy. It's not really an R question is more an Statistics question. I applied a chemical in 3 different events during a growing season. I took 3 samples in each event to estimate mean applied chemical. Thus, I can easily estimate means and st.err by event. I can then estimate the total chemical applied for the whole season by adding the 3 means

Hi, I'm quite new to the R-project. I was suggested to look into it because I am trying to solve the "Subset sum" problem", which basically is: Given a set of integers and an integer s, does any non-empty subset sum to s? (See http://en.wikipedia.org/wiki/Subset_sum_problem) I have been searching the web for quite some time now (which is how I eventually discovered that my

http://bugs.freedesktop.org/show_bug.cgi?id=24820 Summary: [nv15] Vertical screen corruption in X when KMS is enabled Product: xorg Version: git Platform: x86 (IA32) OS/Version: Linux (All) Status: NEW Severity: normal Priority: medium Component: Driver/nouveau AssignedTo: