search for: d_i

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 at least you know enough to plot n(d_i) (number of counts as a function of particle size). If you can fit the {n...

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 letters represent values given from sample observations. The equations involve counts of the number of fulfilled conditions and therefore non-linea...

Hello, My name is Ick Hoon Jin and I am Ph. D. student in Texas A & M Univ.. When I run the C embedded in R in the Linux system, I confront the following error after 6,000 iteration. By googling I found this error is from the problem in C. *** caught segfault *** address (nil), cause 'memory not mapped' My C code is following:

This had a VERY WRONG `To:' address and went straight into my "spam box" into which I don't look too often .. Martin ------- start of forwarded message ------- Message-ID: <Pine.A41.4.05.10008212237300.18590-100000 at fisher.stats.uwo.ca> MIME-Version: 1.0 From: Xiaoqiang Li <lxq at stats.uwo.ca> To: Majordomo-Owner at stat.math.ethz.ch Subject: r-help Date: Mon,

Generate a clustered pattern in [0; 1]2 as follows: (a) Generate nc, say 20, independent cluster centres (which can be called parents) that are distributed i.i.d. uniformly in the unit square; (b) then n daughters are assigned i.i.d. uniformly to these parents and such that each daughter is located i.i.d. uniformly in a disk of radius r = 0:1 centred at her parent, under the periodic boundary

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

Hi it smells like a homework, this list has no homework policy. Anyway, maybe the sample approach could be used. Generate x,y coordinates - ?expand.grid sample n centers - ?sample select all points within defined area - ?point.in.polygon, package sp select desired number of points - ?sample Cheers Petr > -----Original Message----- > From: R-help [mailto:r-help-bounces at r-project.org]

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

...z$e <- NULL function(x, deriv = 0) { deriv <- as.integer(deriv) @@ -114,18 +113,25 @@ ## where dx := (u[j]-x[i]); i such that x[i] <= u[j] <= x[i+1}, ## u[j]:= xout[j] (unless sometimes for periodic spl.) ## and d_i := d[i] unless for natural splines at left - .C("spline_eval", - z$method, - as.integer(length(x)), - x=as.double(x), - y=double(length(x)), - z$n, - z$x, - z$y, - z$b, - z$c, - z$d, - PACKAGE="stats")$y + res <- .C("spline_eval", +...

...z$e <- NULL function(x, deriv = 0) { deriv <- as.integer(deriv) @@ -114,18 +113,31 @@ ## where dx := (u[j]-x[i]); i such that x[i] <= u[j] <= x[i+1}, ## u[j]:= xout[j] (unless sometimes for periodic spl.) ## and d_i := d[i] unless for natural splines at left - .C("spline_eval", - z$method, - as.integer(length(x)), - x=as.double(x), - y=double(length(x)), - z$n, - z$x, - z$y, - z$b, - z$c, - z$d, - PACKAGE="stats")$y + res <- .C("spline_eval", +...

...AOV" situation, something probably pretty rare nowadays in real practice, but common of course in teaching ANOVA. As you may remember, in a balanced design, all observations have the same leverages h_{ii}, and the plot R_i vs h_ii is really not so useful. In that case, the cook distances CD_i = c * R_i ^2 and so CD_i vs i {the old "4-th plot in plot.lm"} is graphically identical to R_i^2 vs i. Now in that case (of identical h_ii's), I think one would really want "R_i vs i". Question to the interested parties: Should there be an automatism ``when h_...