similar to: Help in R package

Hi, I need to write two inequalities depend on cumulative distribution (CDF) of beta binomial distribution where alpha and beta are unknown and need to find them. CDF of betabinomial(2,10,alpha,beta) <0.3<=CDF of betabinomial(3,10,alpha,beta) and CDF of betabinomial(5,10,alpha,beta) <0.8<=CDF of betabinomial(6,10,alpha,beta) How I can do that using r studio package? I tried to do

On Jun 5, 2009, at 9:26 AM, Mai, Haohui wrote: > By static array bounds checking, I mean eliminating array bounds > checking > which can be proved ``safe'' at compile-time. Even though SAFECode does have such a pass, there are some tradeoffs with the current version: 1. It uses an external solver (Omega), which is one more dependence for LLVM in general. I don't have

Jose - Before implementing SNK and Duncan's, you may want to be aware of some criticisms of these methods: >From Hsu (1996), "Newman-Keuls multiple range test is not a confident inequalities method and cannot be recommended." "Duncan's multiple range test is not a confident inequalities method and cannot be recommended either. In the words of Tukey (1991),

Dear R users, I?m trying to optimize simultaneously two binomials inequalities (used in acceptance sampling) which are nonlinear solution, so there is no simple direct solution. Please, let me explain shortly the the problem and the question as following. The objective is to obtain the smallest value of 'n' (sample size) satisfying both inequalities: (1-alpha) <= pbinom(c, n, p1)

Hi All, I'm trying to do the following constrained optimization example. Maximize x1*(1-x1) + x2*(1-x2) + x3*(1-x3) s.t. x1 + x2 + x3 = 1 x1 >= 0 and x1 <= 1 x2 >= 0 and x2 <= 1 x3 >= 0 and x3 <= 1 which are the constraints. I'm expecting the answer x1=x2=x3 = 1/3. I tried the "constrOptim" function in R and I'm running into some issues. I first start off

Dear guRus, I am doing a logistic regression using restricted cubic splines via rcs(). However, the fitted probabilities should be nondecreasing with increasing predictor. Example: predictor <- seq(1,20) y <- c(rep(0,9),rep(1,10),0) model <- glm(y~rcs(predictor,n.knots=3),family="binomial") print(1/(1+exp(-predict(model)))) The last expression should be a nondecreasing

Hi, i need to minimize a quadratic function with boundary condidtions and one equality condition. In order to do that i converted the equality constraint into 2 inequality constaints and passed everything cia constrOptim, as the manual said: everything included in the ... will be passed to Optim that will pass it back to fn in case it does not need it. My code is the following: mat <-

Hello all, I'm trying to find all combinations of 4 numbers that satisfy 4 criteria, inside of a matrix (62 x 25). I've found a way to do this using for loops, but it is extremely slow because it involves checking every possible combination of numbers (567300) to see if the criteria are satisfied. Do you think there is a faster method of doing this? Or some functions that might help me

On Wed, 18 Apr 2012 18:16:47 -0700 Preston Briggs <preston.briggs at gmail.com> wrote: > Hi Sanjoy, > > Here's a version of Banerjee and Wolfe's Exact SIV test: > https://sites.google.com/site/parallelizationforllvm/weak-siv-test > It assumes you've already filtered out the easy cases handled by ZIV, > strong SIV, etc. > > I'm not confident about my

Dear All, I would like to minimise a nonlinear function subject to linear inequality constraints as part of an R program. I have been using the constrOptim function. I have tried all of the methods that come with Optim, but nothing finds the correct solution. If I use the correct solution as the vector of starting values, though, my program does output the correct solution and optimum - the

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,

Dear R users, I?m trying to optimize simultaneously two binomials inequalities used to acceptance sampling, which are nonlinear solution, so there is no simple direct solution. Please, let me explain shortly the the problem and the question as following. The objective is to obtain the smallest value of 'n' (sample size) satisfying both inequalities: (1-alpha) <= pbinom(c, n, p1)

Hi all, I''ve installed paperclips but paperclip is not saving the files my model has has_attached_file :attachment,:styles => { :medium => "300x300>", :thumb => "100x100>" } and i have db migration as class AddAttachmentToPolicies < ActiveRecord::Migration def self.up add_column :policies,

Dear, I am using the solnp command (package Rsolnp) for a problem with equality and inequality constraints. I am not getting convergence for my problem but apart that I get 1 warning per iteration saying: ?In cbind(temp, funv) : number of rows of result is not a multiple of vector length (arg 1)?. I checked for equality and inequality functions and they seem fine to me. Where this

By static array bounds checking, I mean eliminating array bounds checking which can be proved ``safe'' at compile-time. Well, even though there are a lot of approaches of doing this, I believe that having an implementation of ABCD would very useful. It would be even more useful if LLVM can have a general framework for doing static array bounds checking -- just like the aliasing framework.

i want to estimate parameters with maximum likelihood method with contraints (contant numbers). for example sum(Ai)=0 and sum(Bi)=0 i have done it without the constraints but i realised that i have to use the contraints. Without constraints(just a part-not complete): skellamreg_LL=function(parameters,z,design) { n=length(z); mu=parameters[1]; H=parameters[2]; Apar=parameters[3:10];

Hi All, I have a doubt regarding join tables I''m having 2 models 1)Fac 2)Cont and both models have " has and belong to many" relationships so there are 3 tables 1)facs 2)conts 3)conts_facs then i''m fetching the data in controller as @conts=Cont.find(:all]) @cfacs=Fac.all(:joins=>:conts, :select=>"facs.name") but i dont know how to get the

Dear R users, I?m trying to optimize simultaneously two binomials inequalities used to acceptance sampling, which are nonlinear solution, so there is no simple direct solution. The 'n' represents the sample size and the 'c' an acceptance number or maximum number of defects (nonconforming) in sample size. The objective is to obtain the smallest value of 'n' (sample size)

Vikram S. Adve wrote: > On Jun 5, 2009, at 9:26 AM, Mai, Haohui wrote: > > >> By static array bounds checking, I mean eliminating array bounds >> checking >> which can be proved ``safe'' at compile-time. >> > > Even though SAFECode does have such a pass, there are some tradeoffs > with the current version: > > 1. It uses an

Hello , I need to estimate the parameters of generalized poisson regression model. I found that I could use : vglm(formula, family,.., data), but I dont know how to interpret the output!!! min 1Q Median 3Q Max elogit (lambda) -.66 -.61 -.46 -.06 35.33 log(theta) -10.2 -.02 .11 .64 1.2