similar to: score test for logistic regression

I keep getting the 'memory.size' error message when I run a program I have been writing. It always it cannot allocate a vector of a certain size. I believe the error comes in the code fragement below where I have multiple arrays that could be taking up space. Does anyone know a good way around this? w1 <- outer(xk$xk1, data[,x1], function(y,z) abs(z-y)) w2 <- outer(xk$xk2,

Hi, I'm trying to get maximum likelihood estimates of \alpha, \beta_0 and \beta_1, this can be achieved by solving the following three equations: n / \alpha + \sum\limits_{i=1}^{n} ln(\psihat(i)) - \sum\limits_{i=1}^{n} ( ln(x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} 1/(psihat(i)) - (\alpha+1) \sum\limits_{i=1}^{n} ( 1 / (x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} (

Consider a data frame (x) with 2 variables, x1 and x2, having equal values. It looks like: x1 x2 1 1 2 2 3 3 Now, consider a second data frame (xk): xk1 xk2 0.5 0.5 1.0 0.5 1.5 0.5 2.0 0.5 0.5 1 1.0 1 1.5 1 2.0 1 0.5 1.5 1.0 1.5 1.5 1.5 2.0 1.5 0.5 2 1.0 2 1.5 2 2.0 2 I have written code to calculate some differences between these

useR's, I am trying trying to find out if there is a faster way to do a certain computation. I have successfully used FOR loops and the apply function to do this, but it can take some time to fully compute, but I was wondering if anyone may know of a different function or way to do this: > x [1] 1 2 3 4 5 > xk [1] 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 I want to do:

Hi, I am doing a retrospective analysis on a cohort from a designed trial, and I am fitting the model fit<-glmD(survived ~ Covariate*Therapy + confounder,myDat,X=TRUE, Y=TRUE, family=binomial()) My covariate has three levels ("A","B" and "C") and therapy has two (treated and control), confounder is a continuous variable. Also patients were randomized to

My data: I have raw data points that form a logit style curve as if they were a time series. Which is to say they form 3 distinct lines with 3 distinct slopes in backwards z pattern. A certain class of my data looks essentially flat to the eye with marginal oscillation. What is important to me is the x value at which the state change is occurring, in other words, the break point Use of

Dear R experts, I'm new to R. It seems to be a simple question but I just can't find a way to do it. Please help me. I have two data sets x and y as shown in the following. I want to compare the first two columns in x and y, find the matched ones and assign the relative value from column 2 of y to generate the third column of x. Any help wil be appreciated. Thanks. > x [,1]

Hi all-- We are planning an intervention study for adolescent alcohol use, and I am planning to use simulations based on a hurdle model (using the hurdle() function in package pscl) for sample size estimation. The simulation code and power code are below -- note that at the moment the "power" code is just returning the coefficients, as something isn't working quite right. The

I use "while" loop but it produces an errro. I have no idea about this. Error in "[<-"(`*tmp*`, i, value = numeric(0)) : nothing to replace with The problem description is The likelihood includes two parameters to be estimated: lambda (=beta0+beta1*x) and alpha. The algorithm for the estimation is as following: 1) with alpha=0, estimate lambda (estimate beta0

Dear Users, I am new to both of things, so do not blame me too much... I am busy with semiparametric regression and use WinBUGS to sample posteriors. The code to call Winbugs is as follows: data <- list("y","X","n","m") #My variables inits.beta <- rep(0,K) inits.beta0 <- 0 inits <-

Hello all, I got some severe iscsi connection loss on my dom0 (Gentoo 2.6.20-xen-r6, xen 3.1.1). Happening several times a day. open-iscsi version is 2.0.865.12. Target iscsi is the open-e DSS product. Here is a snip of my messages log file: May 5 16:52:50 ying connection226:0: iscsi: detected conn error (1011) May 5 16:52:51 ying iscsid: connect failed (111) May 5 16:52:51 ying iscsid:

HI >i have a little problem please help me to solve it >this is the code in R: >> beta0 [1] 64.90614 > beta1 [1] 17.7025 > beta [1] 17 64 >her beta<- c(beta0, beta1) thank you in advance hafida -- View this message in context: http://r.789695.n4.nabble.com/Decimal-number-tp4639428.html Sent from the R help mailing list archive at Nabble.com.

Dear list, After running for a while, it crashes and gives the following error message: can anybody suggest how to deal with this? Error in if (ratio0[i] < log(runif(1))) { : missing value where TRUE/FALSE needed ################### original program ######## p2 <- function (Nsim=1000){ x<- c(0.301,0,-0.301,-0.602,-0.903,-1.208, -1.309,-1.807,-2.108,-2.71) # logdose

Dear all, I am trying to evaluate the optimisation behaviour of a function. Originally I have optimised a model with real data and got a set of parameters. Now I am creating simulated data sets based on these estimates. With these simulations I am estimating the parameters again to see how variable the estimation is. To this end I have written a loop which should generate a new simulated data

Hello R-Experts, I am facing the problem that I have to estimate several parameters for a lot of different dependent variables. One single regression looks something like this: y = beta0 + beta1 * x1 + beta2 * x2 + beta3 * x1 * x2 + beta4 * x4 + beta5 * lag(x4,-1) where y is the dependent variable and xi are the independent ones. Important to me are the different estimates of betai and their

Dear all,I am a new user of WinBUGS and need your help. After running the following code, I got parameters of beta0 through beta4 (stats, density), but I don't know how to get the prediction of the last value of h, the variable I set to NA and want to model it using the following code.Does anyone can given me a hint? Any advice would be greatly appreciated.Best

useR's, I would like to know if there is a way to avoid using FOR loops to perform the below calculation. Consider the following data: > x [,1] [,2] [,3] [1,] 4 11 1 [2,] 1 9 2 [3,] 7 3 3 [4,] 3 6 4 [5,] 6 8 5 > xk Var1 Var2 Var3 1 -0.25 1.75 0.5 2 0.75 1.75 0.5 3 1.75 1.75 0.5 4 2.75 1.75 0.5 5 3.75 1.75

Hi, I am trying to covert a Winbugs code into R code. Here is the winbugs code model{# model’s likelihoodfor (i in 1:n){time[i] ~ dnorm( mu[i], tau ) # stochastic componenent# link and linear predictormu[i] <- beta0 + beta1 * cases[i] + beta2 * distance[i]}# prior distributionstau ~ dgamma( 0.01, 0.01 )beta0 ~ dnorm( 0.0, 1.0E-4)beta1 ~ dnorm( 0.0, 1.0E-4)beta2 ~ dnorm( 0.0, 1.0E-4)#

Dear All, I am trying to fit a structural time series model using the StructTS function (package stats) with only 2 seasons (summer and winter). More than 2 seasons work fine but with 2 seasons I get this error: > fit <- StructTS(y.ts, type="BSM") Error in T[cbind(ind + 1L, ind)] <- 1 : subscript out of bounds I have looked at Prof. Ripley's 2002 RNews article but cannot

On Mon, Jun 22, 2020 at 4:02 PM John Hubbard <jhubbard at nvidia.com> wrote: > > On 2020-06-22 15:33, Yang Shi wrote: > > On Mon, Jun 22, 2020 at 3:30 PM Yang Shi <shy828301 at gmail.com> wrote: > >> On Mon, Jun 22, 2020 at 2:53 PM Zi Yan <ziy at nvidia.com> wrote: > >>> On 22 Jun 2020, at 17:31, Ralph Campbell wrote: > >>>> On