similar to: Two 3D cones in one graph

Hi, I want to impute the missing values in my data set multiple times, and then combine the results (like multiple imputation, but manually) to get a mean of the parameter(s) from the multiple imputations. Does anyone know how to do this? I have the following script: y1 <- rnorm(20,0,3) y2 <- rnorm(20,3,3) y3 <- rnorm(20,3,3) y4 <- rnorm(20,6,3) y <- c(y1,y2,y3,y4) x1 <-

Dear R experts,   I'm running the following stationary bootstrap programming to find the parameters estimate of a linear model:     X<-runif(10,0,10) Y<-2+3*X a<-data.frame(X,Y) coef<-function(fit){   fit <- lm(Y~X,data=a)    return(coef(fit)) }  result<- tsboot(a,statistic=coef(fit),R = 10,n.sim = NROW(a),sim = "geom",orig.t = TRUE)   Unfortunately, I got this

Hello all... I am just starting to teach myself Bayesian methods, and am interested in learning how to use UMacs. I've read the documentation, but the single example is a bit over my head at the level I am at right now. I was wondering if anyone has any simple examples they'd like to share. I've successfully done a couple of simple gibbs examples, but have had a hard time

Greetings R users, maybe there is someone who can help me with this problem: I'm trying to fit this discontinous model : GE<-data.frame( Ci<-c(81,87,91,111,159,173,295,453,629,984), A<-c(-0.9,1.2,3.5,8.3,13.1,14.4,22.9,27.3,29.6,32.6) ) rhs <- function(Ci, Vcmax, J, Rd) { ? ?R <-0.008314472 ? ?Tleaf <-25 ? ?Kc <-exp(38.05-79.43/(R*(Tleaf+273.15))) ? ?Ko

Dear R users: I have the following matrix. 0 1 1 0 1 0 2 1 0 3 0 0 I would like to spread the matrix such that whenever the row sum is greater than 1 the row is repeated the number of times given by the row sum. Furthermore I would like to split the following cases: 0 1 1 such that it map to the following matrix 0 1 0 0 0 1 such that each row adds up to 1. I have no problems with cases

Dear R users, I have two nonlinear equations, f1(x1,x2)=0 and f2(x1,x2)=0. I try to use optim command by minimize f1^2+f2^2 to find x1 and x2. I found the optimal solution changes when I change initial values. How to solve this? BTW, I also try to use grid searching. But I have no information on ranges of x1 and x2, respectively. Any suggestion to solve this question? Thanks, Kate

Let us consider the current uniform sampling without replacement algorithm. It resides in function do_sample in https://svn.r-project.org/R/trunk/src/main/random.c Its complexity is obviously O(n), where the sample is selected from 1...n, since the algorithm has to create a vector of length n. So when the sample size is much lesser than n, the algorithm is not effective. Algorithms with

Hello! I know that probably my question is rather simple but I' m a very beginner R-user. I have to numerically integrate the product of two function A(x) and B(x). The integretion limits are [X*; +inf] Function A(x) is a pdf function while B(x)=e*x is a linear function whose value is equal to 0 when the x < X* Moreover I have to iterate this process for different value of X* and for

Dear R users, I'm trying to implement a self-defined function with multiple arguments, one of which is an array, but I find that the result is a single value instead of an array. This is the example I'm working on: # define integration limit vector > Mabslim <- c(-17.95, -16.65, -17.27, -17.62, -16.76, -17.07, -17.02) # Define function > schech<-function(x,alpha,xstar)

Hello, I'm a first semester statistics student<http://r.789695.n4.nabble.com/Question-regarding-error-quot-x-and-y-lengths-differ-quot-td4665773.html#>and I am using R for roughly the third time ever. I am following a tutorial and yet I still get the error "x and y lengths differ." I am very new to this program, and I have searched for solutions, but because I do not understand

Hi, This code: n=40 x=17 phat=x/n SE=sqrt(phat*(1-phat)/n) zstar=qnorm(0.995) E=zstar*SE phat+c(-E,E) Gives this result: [1] 0.2236668 0.6263332 The TI Graphing calculator gives the same result. Whereas this test: prop.test(x,n,conf.level=0.99,correct=FALSE) Give this result: 0.2489036 0.6224374 I'm wondering why there is a difference. D. -- View this message in context:

Dear All, I am trying to convert some S-plus code that I have to run MCMC into R-code. The program works in S-plus, but runs slowly. I have managed to source the program into R. R recognizes that the program is there; for example, it will display the code when I type the function name at the prompt. However, the program will not run. When I try to run the program, I get the following error

Folks, Based on http://www.biostat.wustl.edu/archives/html/s-news/1999-06/msg00125.html I thought I should experiment with using survreg() to estimate tobit models. I start by simulating a data frame with 100 observations from a tobit model > x1 <- runif(100) > x2 <- runif(100)*3 > ystar <- 2 + 3*x1 - 4*x2 + rnorm(100)*2 > y <- ystar > censored <- ystar <= 0

I am running my code in a loop and it does not work but when I run it outside the loop I get the values I want. n <- 1000; # Sample size fitglm <- function(sigma,tau){ x <- rnorm(n,0,sigma) intercept <- 0 beta <- 0 ystar <- intercept+beta*x z <- rbinom(n,1,plogis(ystar)) xerr <- x + rnorm(n,0,tau) model<-glm(z ~ xerr, family=binomial(logit))

I am new to R and I am trying to do a monte carlo simulation where I generate data and interject error then test various cut points; however, my output was garbage (at x equal zero, I did not get .50) I am basically testing the performance of classifiers. Here is the code: n <- 1000; # Sample size fitglm <- function(sigma,tau){ x <- rnorm(n,0,sigma) intercept <- 0 beta

I start by doing a simple gaussian tobit by MLE: x1 <- runif(1000) # E() = 0.5 x2 <- runif(1000)*2 # E() = 1 x3 <- runif(1000)*4 # E() = 2 ystar <- -7 + 4*x1 + 5*x2 + rnorm(1000) # is mean 0 y <- ystar censored <- ystar <= 0 y[censored] <- 0 library(AER) m <- tobit(y ~ x1 + x2, left=0, data=D) summary(m) Which gives: Call:

I am running 1.3.1 on a Windows (NT 4.0) machine. I've fit a nonlinear model intended to predict crop yield from nutrient information, and want to use the profile function. If I type say, profile(simparj.fm) I get the following error message: "Error in prof$getProfile(): number of iterations exceeded maximum of 5.25515e-308" I used the profiler function to profile simparj,fm step

Dear all, I'm using zeroinfl() from the pscl-package for zero inflated Poisson regression. I would like to calculate (aproximate) prediction intervals for the fitted values. The package itself does not provide them. Can this be calculated analyticaly? Or do I have to use bootstrap? What I tried until now is to use bootstrap to estimate these intervals. Any comments on the code are welcome.

library(ROCR) n <- 1000 fitglm <- function(iteration,intercept,sigma,tau,beta){ x <- rnorm(n,0,sigma) ystar <- intercept+beta*x z <- rbinom(n,1,plogis(ystar)) xerr <- x + rnorm(n,0,tau) model<-glm(z ~ xerr, family=binomial(logit)) *int*<-coef(model)[1] *slope*<-coef(model)[2] # when add error you are suppose to get slightly bias slope. However when I change

Dear R experts, I'm planning to write some kind of multivariate regression function where I would like to use a formula method. My question is if there is anywhere some detailed introduction how to program formulas in R? A bit more about what I need: I would like to start implementing a multivariate hierarchical regression model and to keep it simple in the beginning I would restrict