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Hello, I need to integrate the absolute difference between two lines measured on different points. # For example : x <- seq(0, 1, 1/100) f_x <- runif(101) + x y <- seq(0, 1, 1/23) f_y <- runif(24) + (1 - y) plot(x, f_x, type="l") lines(y, f_y) Then I would like to compute Integral( | f_x - f_y | )dx. (This is not the same as | Integral(f_x)dx - Integral(f_y)dx |.) Computing this integral looks non trivial. I guess I should interpolate the points of f_y over x an...

Dear R-list members, I've had a hard time trying to solve a non-linear system (nls) of equations which structure for the equation i, i=1,...,4, is as follows: f_i(d_1,d_2,d_3,d_4)-k_i(l,m,s) = 0 (1) In the expression above, both f_i and k_i are known functions and l, m and s are known constants. I would like to estimate the vector d=(d_1,d_2,d_3,d_4) which is solution

Hi, I am solving a projection pursuit regression problem, of the form y = \sum_i f_i (a_i^T x), where a_i are unknown directions, while f_i are unknown univariate link functions. The following is known about each f_i: 1. f_i (0) = 0  (that is, each f_i passes through the origin) 2. f_i is monotonic. Is there a way to ensure that the function ppr() in R produces solutions that respect the

Dear all, Given a LME model (following the notation of Pinheiro and Bates 2000) y_i = X_i*beta + Z_i*b_i + e_i, is it possible to extract the variance-covariance matrix for the estimated beta_i hat and b_i hat from the lme fitted object? The reason for needing this is because I want to have interval prediction on the predicted values (at level = 0:1). The "predict.lme" seems to

Hello, I want to solve a nonlinear 3SLS problem with "nlsystemfit()". The equations are of the form y_it = f_i(x,t,theta) The functions f_i(.) have to be formulated as R-functions. When invoking "nlsystemfit()" I get the error Error in deriv.formula(eqns[[i]], names(parmnames)) : Function 'f1' is not in the derivatives table

...), the computation of such a convolution is quite fast. (B) Characteristic function: X will be approximated with Y=X+Z, where Z is normal N(0,sigma) with small sigma. Y has a density (which it is impossible to compute directly) but the characteristic function (_continuous_ Fourier transform) cf_Y of Y can easily be computed analytically (without knowing the density of Y) Now let s be a numeric vector. I want to get the density f_Y(s) of Y evaluated along s. The proper way of doing this would be to apply the inverse continuous Fourier transform to the function cf_Y at each point in s. Thi...

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

...){ mat <- rbind(like.mat(score,items, (prof_cut-gauss_numer$nodes)), dnorm(prof_cut-gauss_numer$nodes, mean=mu, sd=sigma)) } else { mat <- rbind(like.mat(score,items, (gauss_numer$nodes+prof_cut)), dnorm(gauss_numer$nodes+prof_cut, mean=mu, sd=sigma)) } f_y <- rbind(apply(mat, 2, prod), exp(gauss_numer$nodes), gauss_numer$weights) sum(apply(f_y,2,prod)) } class.denom <- function(score,items, mu=0, sigma=1){ gauss_denom <- gauss.quad.prob(49, dist='normal', mu=mu, sigma=sigma) mat <- rbind(like.mat(score,items,gauss_den...

Hello, i got an warning message in the following code: f<-1:100 t<-1:100 b<-100 ll2 <- function(b,f,t) { t<-cumsum(t) tn<-t[length(t)] i<-seq(along=f) s1<-(tn*exp(-b*tn)*sum(f[i]))/(1-exp(-b*tn)) s2<-sum((f[i]*(t[i]*exp(-b*t[i])-t[i-1]*exp(b*t[i-1])))/(exp(-b*t[i-1])-exp(-b*t[i]))) s1-s2 } ll2(b,f,t) i think, the problem here is, that t[0] doesn't

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

Dear R users, I am a mathematics postdoc at UC Berkeley. I have written a package in a Computational Algebra System named Singular http://www.singular.uni-kl.de to compute the Maximum Likelihood of a given probability distribution over several discrete random variables. This package gives exact answers to the problem. But more importantly, it gives All MLE solutions. My understanding is that

...(aboveQ==FALSE){ mat <- rbind(like.mat(x,b, (prof_cut-gauss_numer$nodes)), dnorm(prof_cut-gauss_numer$nodes, mean=mu, sd=sigma)) } else { mat <- rbind(like.mat(x,b, (gauss_numer$nodes+prof_cut)), dnorm(gauss_numer$nodes+prof_cut, mean=mu, sd=sigma)) } f_y <- rbind(apply(mat, 2, prod), exp(gauss_numer$nodes), gauss_numer$weights) sum(apply(f_y,2,prod)) } class.denom <- function(x,b, mu=0, sigma=1){ gauss_denom <- gauss.quad.prob(49, dist='normal', mu=mu, sigma=sigma) mat <- rbind(like.mat(x,b,gauss_denom$nodes),gauss_...

Thanks for sharing the questions and responses! Is it possible to appreciate how much the coefficients matter in one or the other model? Say, using Biau's example, using coxph, as.factor(grade2 == "high")TRUE gives hazard ratio 1.27 (rounded). As clinician I can grasp this HR as 27% relative increase. I can relate with other published results. With survreg the Weibull model gives a

A question about: Kalman in R, time series and deJong-Penzer statistic - how to compute it using available artefacts of KalmanXXXXX? Background. in the paper http://www.lse.ac.uk/collections/statistics/documents/researchreport34.pdf 'Diagnosing Shocks in TIme Series', de Jong and Penzer construct a statistic (tau) which can be used to locate potential shocks. [p15, Theorem 6.1 and

Hi out there, imagine you have a dataset (x,y) with errors f, so that each y_i is y_i +- f_i. This is the normal case for almost all measurements, that one quantity y can only be measured with a certain accuracy. > x<-c(1,2,3) > y<-c(1.1,0.8,1.3) > f<-c(0.2,0.2,0.2) > plot(x,y) #whereas every y has the uncertainty of f If I now perform a nls-fit (and force the data

Hi out there, imagine you have a dataset (x,y) with errors f, so that each y_i is y_i +- f_i. This is the normal case for almost all measurements, since one quantity y can only be measured with a certain accuracy f. > x<-c(1,2,3) > y<-c(1.1,0.8,1.3) > f<-c(0.2,0.2,0.2) > plot(x,y) #whereas every y has the uncertainty of f If I now perform a nls-fit (and force the data

...39;Lat') plot(Raw$Lon,Raw$Lat,type="l",main = "lowess(cars)") # data should be or filtared or ...? #expected answer: # ver 1 ------------------------------------------------------------------------------ #Raw$Lon_v2<- f_x (x=Raw$Lon, y=Raw$Lat) #Raw$Lat_v2 <- f_y (x=Raw$Lon, y=Raw$Lat) #lines(Raw$Lon_v2,Raw$Lat_v2,col=2) # ver 2 ------------------------------------------------------------------------------ op <- par(mfrow = c(2,1), mgp = c(2,.8,0), mar = .1+c(3,3,3,1)) plot(Raw$Lon,Raw$Lat,type="l",main = "lowess()") f_k...

I wish to generate a vector of uniformly distributed data with a defined correlation to another vector The only function I have been able to find doing something similar is corgen from the library ecodist. The following code generates data with the desired correlation to the vector x but the resulting vector y is normal and not uniform distributed library(ecodist) x <- runif(10^5) y

>> You found it. The SHL32 (not SHR32) line fixes the problem. It must be >> doing a 16-bit shift, then extending the result (which is reasonable). >> As >> it happens, that it the same macro which gave us trouble last May >> (25th/26th), when the C55 build was more subtlely broken. > > Yes, that's what I finally remembered. I think I've fixed all

Dear R help list, I am modeling some survival data with coxph and survreg (dist='weibull') using package survival. I have 2 problems: 1) I do not understand how to interpret the regression coefficients in the survreg output and it is not clear, for me, from ?survreg.objects how to. Here is an example of the codes that points out my problem: - data is stc1 - the factor is dichotomous