similar to: Help with lsei() from package limSolve()

Hi guys, I am having problem loading a package that I have installed. I have searched some old thread but they were no help in terms of solving the problem. I uninstalled every possible component of R and installed R 2.13 and followed the R-faqs installation steps. Then I installed the package (lsei) from local zip file which was installed successfully but can not be loaded and returns the error

Dear All, Which package/function could i use to solve following linear least square problem? A over determined system of linear equations is given. The nnls-function may would be a possibility BUT: The solving is constrained with a inequality that all unknowns are >= 0 and a equality that the sum of all unknowns is 1 The influence of the equations according to the solving process is

Dear All, have a general question about coefficients estimation of the mixed model. I simulated a very basic model: Y|b=X*\beta+Z*b +\sigma^2* diag(ni); b follows N(0,\psi) #i.e. bivariate normal where b is the latent variable, Z and X are ni*2 design matrices, sigma is the error variance, Y are longitudinal data, i.e. there are ni

I have a general question about coefficients estimation of the mixed model. I simulated a very basic model: Y|b=X*\beta+Z*b +\sigma^2* diag(ni); b follows N(0,\psi) #i.e. bivariate normal where b is the latent variable, Z and X are ni*2 design matrices, sigma is the error variance, Y are longitudinal data, i.e. there are ni

Dear R-users, i try to solve to following linear programm in R 0 * x_1 + 2/3 * x_2 + 1/3 * x_3 + 1/3 * x_4 = 0.3 x_1 + x_2 + x_3 + x_4 = 1 x_1, x_2, x_3, x_4 > 0, x_1, x_2, x_3, x_4 < 1 as you can see i have no objective function here besides that i use the following code. library(lpSolve) f.obj<-c(1,1,1,1) f.con<-matrix(c(0,2/3,1/3,1/3, 1,1,1,1,

Hola a todos, Simplemente comentar que me tengo encontrado con muchos problemas de optimización. Mi recomendación general, en el caso multidimensional y si el tiempo de computación es importante, sería buscar un algoritmo diseñado para el tipo de problema (evitar los algoritmos más generales tipo optim si puede haber problemas de mínimos locales). Algunos casos que tengo resuelto con R

Dear R users, I'm trying to estimate the matrix of regression parameters. I need to do it numerically, so I used optim and nls. I got the initial parameter estimates from least squares, and input them into those functions. But when I run the optim function, it stops in 30 seconds and shows 'convergence=1'. And if I use the nlm function, then it runs for a while, and finally stops

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

Dear All I have a constrained optimisation problem, I want to maximise the following function t(weights) %*% CovarianceMatrix %*% weights for the weights, subject to constraints on each element within the weights & the weights vector summing to 1. i.e. weights = (x1, x2, x3), where x1 is within some given range (a +b, a - b). I have tried to do this using the optim function in R,

I use smooth.spline(x, y) in package modreg and I would like to get value of penalized log likelihood and preferable also its two parts. To make clear what I am asking for (and make sure that I am asking for the right thing) I clarify my problem trying to use the same notation as in help(smooth.spline): I want to find the natural cubic spline f(x) such that L(f) = \sum_{k=1}{n} w[k](y[k] -

Suppose I have two variables, x and y. For a fixed number of knots, I want to create a spline transformation of x such that a loss function is minimized. Presumably, this loss function would be least squares, i.e. sum (f(x)-y)^2. The spline transformations would be linear, quadratic or cubic. I know I can solve this problem using some optimization function in R, but I was wondering if anyone

Hi R, A quick question.... How can I optimize the objective function constrained to quadratic constraints? Which function of R is useful for quadratic constraints? Many Thanks, Shubha This e-mail may contain confidential and/or privileged i...{{dropped:13}}

Dear useRs, upon request, the VGAM package (currently version 0.7-1) has been officially released on CRAN (the package has been at my website http://www.stat.auckland.ac.nz/~yee/VGAM for a number of years now). VGAM implements a general framework for several classes of regression models using iteratively reweighted least squares (IRLS). The key ideas are Fisher scoring, generalized linear and

Hi, I am getting the following error when I'm running jags.model() > meas1 <- jags.model(file="measurement.bug",data=dat.test) syntax error, unexpected '}', expecting ',' or ')' Error in jags.model(file = "measurement.bug", data = dat.test) : Parse error on line 1 Below is my JAGS model. Please cc me as I'm a digest subscriber if

> > >> > LVI is one of those analyses with quadratic runtime, but has a cutoff to > its search depth so that it is technically not quadratic. So increased > inlining could easily exacerbate it more than non-"quadratic" passes. > (increased inlining would also cause a general slowdown too). > > LVI is only quadratic because of the way we've built it

I'm using R 2.8.1 on Ubuntu 8.10. I'm writing partly to ask what's wrong, partly to tell other users who search that there is a work around. The General Social Survey is a long standing series of surveys provided by NORC (National Opinion Research Center). I have downloaded some years of the survey data in SPSS format (here's the site:

How do I calculate the confidence interval for the value x given by the intersection of two quadratics (i.e. parabolas)? I fit two quadratics of the form: y = C1 + B1*x + A1*x^2 y = C2 + B2*x + A2*x^2 to two sets of points N1 and N2. I test for whether they intersect, if they do then I calculate the roots of: 0 = (C1 - C2) + (B1 - B2)*x + (A1 - A2)*x^2 to determine where they intersect

A number of users have spotted a terminal problem with the 'debug' package under R2.4.0, along the lines of > mtrace(x) > x() Error in attr(value, "row.names") <- rlabs : row names must be 'character' or 'integer', not 'double' This arose from a bug in 'rbind.data.frame' in R2.4.0 itself. The bug is fixed in R2.4.0 patched, so the

Dear all, I have a model with simple terms, quadratic effects, and interactions. I am wondering what to do when a variable is involved in a significant interaction and in a non-significant quadratic effect. Here is an example d = data.frame(a=runif(20), b=runif(20)) d$y = d$a + d$b^2 So I create both an simple effect of a and a quadratic effect of b. m = lm(y ~ a + b + I(a^2) + I(b^2) +

Hi, I am trying to fit a Johnson SB distribution using fitdist function in fitdistrplus Library. I have defined the Johnson SB distribution from ( http://www.ntrand.com/johnson-sb-distribution/) . But it gives me the follwing errors. Any help would be appreciated #xi = xi #lambda =l #delta =d #gamma = g djohn = function(x,xi,l,d,g) (d/(l*sqrt(2*pi)*((x-xi)/l)*(1-((x-xi)/l))))*exp[-0.5*(g +