similar to: Numerical instability in new R Windows development version

Hi everyone, ? I am trying to get a point of intersection between a polyline and a straight line ?.. and get the x and y coordinates of this point. For exemplification consider this: ? ? set.seed(123) ? k1 <-rnorm(100, mean=1.77, sd=3.33) ?k1 <- sort(k1) q1 <- rnorm(100, mean=2.37, sd=0.74) q1 <- sort(q1, decreasing = TRUE) plot(k1, q1, xlim <- c((min(k1)-5),

Dear all, I am a new user to R and I am using pracma and nloptr libraries to minimize a numerical integration subject to a single constraint . The integrand itself is somehow a complicated function of x and y that is computed through several steps. i formulated the integrand in a separate function called f which is a function of x &y. I want to find the optimal value of x such that the

I have been told by the CRAN administrators that the following code generated an error on 64-bit Fedora Linux (gcc, clang) and on Solaris machines (sparc, x86), but runs well on all other systems): > fn <- function(x, y) ifelse(x^2 + y^2 <= 1, 1 - x^2 - y^2, 0) > tol <- 1.5e-8 > fy <- function(x) integrate(function(y) fn(x, y), 0, 1,

I tried to update my package {pracma} on CRAN from 2.4.2 (2022-09-21) to version 2.4.4 (2023-11-08). This package reverse depends / imports / suggests on 350 packages on CRAN and 25 packages on Bioconductor. The only changes are small corrections on some help files, a new function for stereographic projection, and `gcd` and `Lcm` require integer inputs now (these functions are not used in the

Dear R-users, I'm wondering how to calculate this double integral in R: int_a^b int_c^y g(x, y) dx dy where g(x,y) = exp(- alpha (y - x)) * b Thanks for answering! Cheers, Alui [[alternative HTML version deleted]]

Is there packaged code to convert geographical coordinates (e.g., longitude, latitude, elevation) to Cartesian coordinates in 3-space? I can see how to do this using 1. a spherical-to-Cartesian conversion like pracma::sph2cart(tpr) http://cran.r-project.org/web/packages/pracma/ 2. a geographical-to-spherical conversion. This seems to involve (in roughly increasing order of difficulty or

Dear list, [cross-posting from Stack Overflow where this question has remained unanswered for two weeks] I'd like to perform a numerical integration in one dimension, I = int_a^b f(x) dx where the integrand f: x in IR -> f(x) in IR^p is vector-valued. integrate() only allows scalar integrands, thus I would need to call it many (p=200 typically) times, which sounds suboptimal. The

Hi all, I am using the 'gaussHermite' function from the 'pracma' library ############ CODES ########### library(pracma) cc=gaussHermite(10) cc$x^2 cc$x^5 cc$x^4 ############ CODES ########### as far so good. However, it does NOT work for any NON integer values, say ############ CODES ########### cc$x^(2.5) cc$x^(-2.5) ############ CODES ########### But just think about it

Dear R-help, I am looking for ideas and presentations of new and advanced data visualization methods. As an example of what I am searching for, the 'Many Eyes' pages at http://manyeyes.alphaworks.ibm.com/manyeyes/ may provide a good paradigm. I would be interested even if it will not be easy to implement such examples in R, e.g. because of the interactive nature of these graphical

Given a data frame with a continuous variable and a factor. I would like to generate a histogram of the continuous variable, where each bar is filled with different colors according to the percentage of factor values falling into this region of the continuous variable. I looked into packages like 'lattice' and 'ggplot2'. Searching R-help revealed that 'histogram' is

Hi all, Here is an integration function require(pracma) # for 'quadinf' myint=function(j) { quadinf(function(x) (1/(1+exp(-x)))^j*(1-1/(1+exp(-x)))^(k-j)*dnorm(x,mu,casigma),-Inf,Inf) } in any optimization routine. It works fine most of the time but failed with some particular sets of values, say one of the following: k=20 mu=-1.978295 casigma=0.008326927 >

Several of the methods I use for analyzing large data sets, such as WinGamma: determining the level of noise in data Relief-F: estimating the influence of variables depend on finding the k nearest neighbors of a point in a data frame or matrix efficiently. (For large data sets it is not feasible to compute the 'dist' matrix anyway.) Seeing the proposed solution to "[R] distance

Hi, I have noticed that there is a change in the use of ellipses or . in R versions 2.6.1 and later. In versions 2.5.1 and earlier, the . were always at the end of the argument list, but in 2.6.1 they are placed after the main arguments and before method control arguments. This results in the user having to specify the exact (complete) names of the control arguments, i.e. partial matching is

Hello, I want to do regression or missing value imputation by knn. I searched r-help mailing list. This question was asked in 2005. ksmooth and loess were recommended. But my case is different. I have many predictors (p>20) and I really want try knn with a given k. ksmooth and loess use band width to define neighborhood size. This contrasts to knn's variable band width via fixing a

Hi all, Is there any other packages to do numerical integration other than the default 'integrate'? Basically, I am integrating: integrate(function(x) dnorm(x,mu,sigma)/(1+exp(-x)),-Inf,Inf)$value The integration is ok provided sigma is >0. However, when mu=-1.645074 and sigma=17535.26 It stopped working. On the other hand, Maple gives me a value of 0.5005299403. It is an

While working on 'random walk' applications, I got interested in optimizing noisy objective functions. As an (artificial) example, the following is the Rosenbrock function, where Gaussian noise of standard deviation `sd = 0.01` is added to the function value. fn <- function(x) (1+rnorm(1, sd=0.01)) * adagio::fnRosenbrock(x) To smooth out the noise, define another

Dear R-helpers: I am interested in discretization methods for numerical attributes, as they are reported in the 'machine learning' community. For example, the work of Fayyad & Irani (IJCAI-93), Kononenko, entropy-based approaches, MDL principle, the C4.5 approach, etc. I am especially interested in those methods that take a factor as goal target into account for discretizing

Dear all, I tried running the following syntax but it keeps running for about 4 hours and then i got the following errors: Error in if (is.na(f0$objective)) { : argument is of length zero In addition: Warning message: In is.na(f0$objective) : is.na() applied to non-(list or vector) of type 'NULL' Here is the syntax itself: library('nloptr') library('pracma') #

Dear All, I am having trouble in using R function "constrOptim" to do constraint optimization. It seems that "constrOptim" calls function "optim" when it does the optimization, and "optim" allows us to set "method" to be "SANN" if we want to use simulated annealing. In "optim", the function allows us to set gradient to be

Dear R Users, I am looking for some guidance on setting up an optimisation in R with non-linear constraints. Here is my simple problem: - I have a function h(inputs) whose value I would like to maximise - the 'inputs' are subject to lower and upper bounds - however, I have some further constraints: I would like to constrain the values for two other separate function f(inputs) and