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Dear All, Apologies if this is too simple for this list. Let us assume that you have an instrument measuring particle distributions. The output is a set of counts {n_i} corresponding to a set of average sizes {d_i}. The set of {d_i} ranges from d_i_min to d_i_max either linearly of logarithmically. There is no access to further detailed information about the distribution of the measured sizes, but

Hi, I would appreciate if someone could help me on track with this problem. I want to compute some parameters from a system of equations given a number of sample observations. The system looks like this: sum_i( A+b_i>0 & A+b_i>C+d_i) = x sum_i( C+d_i>0 & C+d_i>A+b_i) = y sum_i( exp(E+f_i) * ( A+b_i>0 & A+b_i>C+d_i) = z A, C, E are free variables while the other

...urn 1; else return 0; free(M1_STAT); free(M2_STAT); } double Count_Edges(int *X, int n) { double temp; int i, j; temp = 0; for(i = 1; i < n; i++) { for(j = 0; j < i; j++) { temp += X[i * n + j]; } } return temp; } double Cal_GWD(int *X, int n) { int *M3, *Degree, *D_Y; int i, j; double theta = 0.25; double GWD = 0.00; M3 = (int *)calloc(n, sizeof(int)); Degree = (int *)calloc(n, sizeof(int)); D_Y = (int *)calloc((n - 1), sizeof(int)); for(i = 0; i < n; i++) { for(j = 0; j < n; j++) { M3[j] = X[i * n + j]; } Degree[i] = Cal_Degree(M3,...

...************************* ************************** stochastic.c ******************************** #include <R.h> #include <Rinternals.h> #include <R_ext/Mathlib.h> #include <R_ext/Random.h> #include <math.h> double MS(double z, double y, double mu ){ double d_y; int k; d_y=sqrt(-8*log(y)); k=(int)floor( (mu+d_y/2-z)/d_y ); return z+k*d_y; } double norm(double mu,double sigma,double x){ double pi=3.141592679565; return 1/sqrt(2*pi*sigma)*exp(-(x-mu)*(x-mu)/sigma/sigma/2); } void stepzero(double *X0, double...

...at her parent, under the periodic boundary conditions (i.e. the square = a torus). My attempt so far is: set.seed(1) library(spatstat) n_parent <- 2 n_daughter <- 4 r = 0.1 cnt <- n W <- disc(radius=3, centre=c(0,0)) i <- 1 while(i <= n_daughter){ d_x <- runif(1) d_y <- runif(1) if (d_x ^2+d_y^2 <r) { i = i+1}} #need a condition here such that (d_x,d_y) lies in B(0,r) #where B(0,r) is a ball of center origin and of radius r In above we should obtain one ball with n_daughters. Next step is to generate parent centers and distribute the n_daughters i...

Hi all Consider the family function, as used by glm. The help page says the value of the family object is a list, one element of which is the following: dev.resids function giving the deviance residuals as a function of (y, mu, wt). But reading any of the family functions (eg poisson) shows that dev.resids is a function that computes the *square* of the deviance residuals (at least, by

Hi all, I am doing some experiment studies... It seems to me that with different combination of 5 parameters, the end results ultimately converged to two scalars. That's to say, some combinations of the 5 parameters lead to one end result and some other combinations of the 5 parameters lead to the other end result (scalar). I am thinking of this is sort of something like clustering or

...My attempt so far is: > > > set.seed(1) library(spatstat) > > n_parent <- 2 > > n_daughter <- 4 > > r = 0.1 > > cnt <- n > > W <- disc(radius=3, centre=c(0,0)) i <- 1 > > while(i <= n_daughter){ > > d_x <- runif(1) > > d_y <- runif(1) > > if (d_x ^2+d_y^2 <r) { > > i = i+1}} > > > #need a condition here such that (d_x,d_y) lies in B(0,r) > > #where B(0,r) is a ball of center origin and of radius r > > In above we should obtain one ball with n_daughters. Next step is to generat...

Dear all, I fitted a non-parametric model using GAM function in R. i.e., gam(y~s(x1)+s(x2)) #where s() is the smooth function Then I obtained the coefficients(a and b) for the non-parametric terms. i.e., y=a*s(x1)+b*s(x2) Now if I want to use this estimated model to do optimization or sensitivity analysis, I am not sure how to incorporate the smooth function since s() may not

--MP_/kvy20nVajVG/n.8m=_ZjLAX Content-Type: text/plain; charset=US-ASCII Content-Transfer-Encoding: 7bit Content-Disposition: inline On Tue, 7 Oct 2008 19:31:03 +0800 Berwin A Turlach <berwin at maths.uwa.edu.au> wrote: > The attached patch (against the current SVN version of R) implements > the latter strategy. With this patch applied, "make check > FORCE=FORCE" passes

--MP_/Rxf/JAvsQx5JLkhZFc9Jmn4 Content-Type: text/plain; charset=US-ASCII Content-Transfer-Encoding: 7bit Content-Disposition: inline G'day Greg, On Mon, 6 Oct 2008 19:50:13 +0200 (CEST) Greg.Snow at imail.org wrote: > This is a low priority bug that has been around for a while, but I > came across it again while alpha testing 2.8. Indeed, that bug must have been around since

As some of you R-devel readers may know, the plot() method for "lm" objects is based in large parts on contributions by John Maindonald, subsequently "massaged" by me and other R-core members. In the statistics litterature on applied regression, people have had diverse oppinions on what (and how many!) plots should be used for goodness-of-fit / residual diagnostics, and to my