search for: liks

Hi, With the following Sweave minimal file: ---<--------------------cut here---------------start------------------->--- \documentclass{article} \usepackage{Sweave} \begin{document} <<binom-sim>>= thetas <- seq(0, 1, by=0.001) prior <- rep(1, length(thetas)) / length(thetas) lik <- dbinom(1, 1, thetas) lik.p <- prior * lik post <- lik.p / sum(lik.p)

Hi the list, I would need some advice on something that looks like a FAQ: the possibility of providing vectors to optim() function. Here is a stupid and short example summarizing the problem: -------------------------------- example 1 ------------ 8< ---------------------- library(stats4) data <- rnorm(100,0,1) lik1 <- function(m, v, data) { N <- length(data) lik.mean <-

Dear people, I'm trying to do some analysis of a data using the models by Royle & Donazio in their fantastic book, particular the following function: http://www.mbr-pwrc.usgs.gov/pubanalysis/roylebook/panel4pt1.fn that applied to my data and in the console is as follows: > `desman.y` <- structure(c(3L,4L,3L,2L,1L), .Names = c("1", "2", "3",

I am sorry if this question is vague or uninformed. I am just learning R and struggling. I am using the book Hierarchical Modeling and Inference in Ecology and they provide examples of R code. I have the following code from the book but when I run it I don't get any output. I cannot get the values of 'out' to show up. Basically, I just want to see my estimates for b0,

Hello, I'm working with an array; I'm trying to make it so that an array of dim(42,2,2) has names whose length corresponds to that of the array, and am hoping someone with experience with this can see what I'm not doing correctly: data11 = array(0,c(41,2,2)) y = lsoda(x0,times,fhn$fn.ode,pars)#This is make.fhn() from colloc infer package# y = y[,2:3]

Hi, I have reconstructed ancestral character states on a phylogeny using MuSSE in the diversitree package and plotted the character state probabilities as pie charts on the nodes. I would, however, like to colour the character states of my extant species, i.e. the tip labels, the same colours as my pie charts, such that all species in state 1 are e.g. blue, species in state 2 red and species in

Hello, I am trying to bootstrap a function that extracts the log-likelihood value and the nls coefficients from an nls object. I want to sample my dataset (pdd) with replacement and for each sampled dataset, I want to run nls and output the nls coefficients and the log-likelihood value. Code: x<-c(1,2,3,4,5,6,7,8,9,10) y<-c(10,11,12,15,19,23,26,28,28,30) pdd<-data.frame(x,y)

I'd like to have a class A that computes a likelihood, and a subclass B that computes the same likelihood by sometimes throws in an additional term (B includes measurement error). So B's likelihood needs to call A's, and then (sometimes) multiply by an additional term. It sounds as if, in the S3 scheme, NextMethod is supposed to do this: like.A <- function(stuff) compute value

Hi, My name is Laura. I'm a PhD student at Queen's University Belfast and have just started learning R. I was wondering if somebody could help me to see where I am going wrong in my code for estimating the parameters [mu1, mu2, lambda1] of a 2-phase Coxian Distribution. cox2.lik<-function(theta, y){ mu1<-theta[1] mu2<-theta[2] lambda1<-theta[3]

hi folks, I'm trying to build a table that contains information about a series of General Linear Models in order to calculate Akaike weights and other measures to compare all models in the series. i have an issue with indexing models and extracting the information (loglikehood, AIC's, etc.) that I need to compile them into the table. Below is some sample code that illustrates my

Hello, I am trying to use optim() on a function involving a summation. My function basically is a thinned poisson likelihood. I have two parameters and in most cases optim() does a fine job of getting the minima. I am simulating my data based on pre specified parameters, so I know what I should be getting. However when my true parameters fall in a particular range, optim() gives

Dear R users, When I run the code below, I get the error "Error in integrate(integrand, 0, Inf) : non-finite function value". The code works if the function returns only "sum(integ)". However, I want to add "cmh" to it. When I add "cmh" I get that error. I can't figure out why this is happening because my integrate function has nothing to do with

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Hi: I'm trying to estimate a model which involves the estimation of double integrals, so I'm using adapt procedure. I need to calculate the integrals trough my 2000 size database, so I do it using a loop. My code correctly estimates the integral for the first row, but for the second R crashes. I tried changing the order of the data but the result is the same so I guess there is something

Hi all, I'm trying to write a MH algorithm in R for a standard normal distribution, I've been trying for a good week or so now with multiple attempts and have finally given up trying to do it on my own as I'm beginning to run out of time for this, would somebody please tell me what is wrong with my latest attempt: n=100 mu=0 sigma=1 lik<-function(theta) exp(((theta-mu)^2)/2*sigma)

Hi there, I ran the following code: vols=read.csv(file="C:/Documents and Settings/Hugh/My Documents/PhD/Swaption vols.csv" , header=TRUE, sep=",") X<-ts(vols[,2]) #X dcOU<-function(x,t,x0,theta,log=FALSE){ Ex<-theta[1]/theta[2]+(x0-theta[1]/theta[2])*exp(-theta[2]*t) Vx<-theta[3]^2*(1-exp(-2*theta[2]*t))/(2*theta[2]) dnorm(x,mean=Ex,sd=sqrt(Vx),log=log) }

hi , lik <- function(nO, nA, nB, nAB){ loglik <- function(par) { p=par[1] q=par[2] r <- 1 - p - q if (c(p,q,r) > rep(0,3) && c(p,q,r) < rep(1,3) ) { -(2 * nO * log (r) + nA * log (p^2 + 2 * p * r) + nB * log (q^2 + 2 * q * r) + nAB * (log(2) +log(p) +log(q))) } else NA } loglik }

Dear R users, I am trying to find the mle that involves integration. I am using the following code and get an error when I use the nlm function d<-matrix(c(1,1,0,0,0,0,0,0,2,1,0,0,1,1,0,1,2,2,1,0),nrow=10,ncol=2) h<-matrix(runif(20,0,1),10) integ<-matrix(c(0),nrow=10, ncol=2) ll<-function(p){ for (k in 1:2){ for(s in 1:10){ integrand<-function(x)

Dear R-List, to assess the model fit for 2PL-models, I tried to mimic the bootstrap-approach chosen in the GoF.rasch()-function. Not being a statistician, I was wondering whether the following simplification (omit the "chi-squared-expressed model fit-step") would be appropriate: GoF.ltm <- function(object, B = 50, ...){ liFits <- list() for(i in 1:B){ rndDat <-

Hello, I'm hoping someone with a wide experience with R may be able to see what the program is trying to tell me. I've got an array: y1=rnorm(41,0.2) y2=rnorm(41,0.2) y3=rbind(y1,y2) data11<-array(0,c(41,2,2)) data11[,1,]=y3 data11[,2,]=y3 rownames(data11)<-rownames(data11, do.NULL = FALSE, prefix = "Obs.") colnames=c("V","R")