similar to: all.equal: possible mismatch between behaviour and documentation

Dear Jon, thank you for raising the issue, >>>>> Jon Clayden <jon.clayden at gmail.com> >>>>> on Tue, 28 Jul 2015 12:14:48 +0100 writes: > Sorry; minor clarification. The actual test criterion in the example I > gave is of course abs((0.1-0.102)/0.1) < 0.01, not abs(0.1) < 0.01. In > any case, this does not match (my reading of) the docs,

Sorry; minor clarification. The actual test criterion in the example I gave is of course abs((0.1-0.102)/0.1) < 0.01, not abs(0.1) < 0.01. In any case, this does not match (my reading of) the docs, and the result is not `TRUE`. Regards, Jon On 28 July 2015 at 11:58, Jon Clayden <jon.clayden at gmail.com> wrote: > Dear all, > > The documentation for `all.equal.numeric` says

Hi guys, I'm trying to use the the integral function to estimate the area under a PDF and a crossing curve. first I stated the function with several vectors in it: fn=function(a,b,F,mu,alpha,xi) { x<-vector() fs<-function(x) { c <- (mu+(alpha*(1-(1-F)^xi)/xi)) tmp <- (1 + (xi * (x - mu))/alpha) ((as.numeric(tmp > 0) * (tmp^(-1/xi - 1) *

Hi, This might seem like a simple question but at the moment I am stuck for ideas. The columns of my matrix in which some data is stored are of this form: X1 Y1 X2 Y2 X3 Y3 ... Xn Yn with n~100. I would like to look at just the X values (i.e. odd column numbers). Is there an easy way to loop round extracting only these columns? Any help would be appreciated.

Greetings All. Once again, I am probably missing something fairly accessible, but since I can't find it I'd welcome advice! I have a dataframe derived from a text file of data in tabular format. For one of the variables, say X, I want to select the subsets which in which X equals a particular value. Such values are given in the text file like: 2.3978953, and each such value will occurr

Dear R-help, Here's a simple example of nonlinear curve fitting where nls seems to get the answer wrong on a very simple exponential fit (my R version 2.7.2). Look at this code below for a very basic curve fit using nls to fit to (a) a logarithmic and (b) an exponential curve. I did the fits using self-start functions and I compared the results with a more simple fit using a straight lm()

Hi, I have written a small C++ function and compile it. However in R I can't see the function I have defined in C++. I have read some web-pages about Rcpp and C++ but it is a bit confusion for me. Anyway, This is the C++-code: #include <Rcpp.h> using namespace Rcpp; // [[Rcpp::export]] List compute_values_cpp(int totalPoints = 1e5, double angle_increment = 0.01, int radius =

Full_Name: Brad Christoffersen Version: 2.3.1 OS: Windows XP Submission from: (NULL) (128.196.193.132) Why is the difference between two numbers so different from the "mean relative difference" output from the all.equal() function? Is this an artifact of the way R stores numerics? I could not find this problem as I searched through the submitted bugs. But I am brand new to R so I

Hi, I have the following problem. It has two parts. 1. I need to calculate the stationary probabilities of a Markov chain, eg if the transition matrix is P, I need x such that xP = x in other words, the left eigenvectors of P which have an eigenvalue of one. Currently I am using eigen(t(P)) and then pick out the vectors I need. However, this seems to be an overkill (I only need a single

Hi all, Running simulations, I'm generating market response to 2 factors X&Y.. There is no closed form for the market response.. The results are store in a matrix Z(X <- seq(.02,.98,.02), Y <- seq(.01,.19,.01)).. For optmization purpose I need to approximate the values for any factor X in 0,02-0,98 and Y in 0,01-0,19 How can I do it ? For one factor : Xn-1 < x <= Xn

.Call("compute_values_cpp") Also, if you were passing arguments to the C++ function you would need to declare the function differently. Do a search on "Rcpp calling C++ functions from R" HTH, Eric On Sun, Dec 3, 2017 at 3:06 AM, Martin M?ller Skarbiniks Pedersen < traxplayer at gmail.com> wrote: > Hi, > > I have written a small C++ function and compile it.

Hi to all, how could I to rotate automatically a data sheet which was imported by read.xls? x1 x2 x3 .... xn y1 1 4 7 ... xn/y1 y2 2 5 8 .... xn/y2 y3 3 6 9 ....xn/y2 yn ... ... ... Xn/Yn to y1 y2 y3 .... yn x1 1 2 3 ..... Yn/x1 x2 4 5 6 .... Yn/x2 x3 7 8 9 .... Yn/x2 xn ... ... ... ..... Yn/xn Kind regards Knut

Dears useRs, I am using the mle function but this gives me the follow erros that I don't understand. Perhaps there is someone that can help me. thank you for you atention. Bernardo. > erizo <- read.csv("Datos_Stokes_1.csv", header = TRUE) > head(erizo) EDAD TALLA 1 0 7.7 2 1 14.5 3 1 16.9 4 1 13.2 5 1 24.4 6 1 22.5 > TAN <-

The programming language mosml comes with foldr that 'accumulates' a function f over a list [x1,x2,...,xn] with initial value b as follows foldr f b [x1,x2,...,xn] = f(x1,...,f(xn-1,f(xn,b))...) Observe that "list" should have same elements so in R terminology it would perhaps be appropriate to say that the accumulation takes place over a 'vector'. I wonder if R

Hi, I have been trying to do numerical differentiation using R. I found some old S code using Richardson Extrapolation which I managed to get to work. I am posting it here in case anyone needs it. ######################################################################## richardson.grad <- function(func, x, d=0.01, eps=1e-4, r=6, show=F){ # This function calculates a numerical approximation

Can someone offer some advice on how to properly evaluate a SYMSXP from a .Call ? I have the following in R: variable xn, with an attribute "mu" which references the variable mu in the global environment. I know "references" is a loose term; mu was defined in this fashion as a way to implement deferred binding: foo <- function(x,mu) { attr(x,"mu") <-

I'm not sure what the process is for this, but can I get the following all.equal function put in the R base? Paul Gilbert ____ all.equal <- function(obj1, obj2,...) {UseMethod("all.equal")} all.equal.default <- function(obj1, obj2, tolerance=.Machine$double.eps) {if(mode(obj1) != mode(obj2) ) return("modes differ.") if (length(obj1) !=

Hi I need advice regarding constraint optimization with large number of variables. I need to solve the following problem max f(x1,...,xn) x1,..xn x1=g1(x1,...,xn) . . xn=gn(x1,...,xn) I am using Rdonlp2 package which works well until 40 variables in my case. I need to solve this problem with over 300 variables. In this case Rdonlp2 is very very slowly. I know

Hi. I am using the Matching package for propensity score matching. For each treated unit, I want to find all control units whose propensity scores lie within a certain distance from the treated unit. The sample code is as follows: > library(Matching) > x <- rnorm(100000) > y <- rnorm(100000) > z <- rbinom(100000,1,0.002) > logit.reg <-

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