similar to: solving for beta0 in a logsitic regression

Dear friends. I have a paper (details below) examining the risk of renal failure after an operation. A logistic regression was done, and the coefficients to two regressors (age and creatinine) plus intercept with standard errors are given. These coefficients must be dependent in estimation, and when no details are given, I thought how I could most informatively get an impression as to how

Hi Is there some function R that multiplies each coefficient by the standard deviation of the corresponding variable and produces a ranking? Stephen -- No virus found in this outgoing message. Checked by AVG Anti-Virus. [[alternative HTML version deleted]]

Hello, 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 and beta1 via GLM) 2) with lambda, estimate alpha via ML estimation 3) with updataed alpha, replicate 1) and 2) until alpha is converged to a value I coded 1) and 2) (it works), but faced some

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} (

Dear All I'm attempting to speed up my model building procedure, but need some help with the loops I've created...please bear with me through the explanation! My basic model call is something like: m0sulf.nlme<-nlme(conc~beta0*exp(-beta1*day)+beta2*exp(-beta3*day), data=m0sulf, fixed=(beta0+beta1+beta2+beta3~1),

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

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

To whom it may concern: I am trying to better understand the functionality of 'R' when making arima predictions to avoid any "Black Box" disadvantages. I'm fitting a seasonal arima model using the following command (having already loaded 'stat' package). arimaSeason <- arima(Data,order=c(1,0,1),seasonal=list(order=c(1,0,1),period=12)) I can then generate

Background: I administer a cluster of RedHat EWS 3U4 Linux workstations at a university. I built R 2.1.0 from source: ./configure \ --prefix=/sscc/opt/R-2.1.0 \ --with-blas=no \ 2>&1 \ | tee NUInstall.configure R is now configured for i686-pc-linux-gnu Source directory: . Installation directory: /sscc/opt/R-2.1.0 C compiler:

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 <-

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

Hi Carlos, Jakob, The PBQP allocator was designed to support a very wide range of constraints, and can handle something like this easily. Say you have 4 of these orX/irX registers, then for any pair of virtual registers used in such an add instruction you would add the following constraint matrix to the PBQP instance: [ 0 inf inf inf ] [ inf 0 inf inf ] [ inf inf 0 inf ] [ inf inf inf 0

Respected Sir, With reference to my mail to you and the reply mail by you dated 9th and 16th January, 2013, I am sending the reproducible code in the attached document named " MODIFIED ANS ". I am also attaching the txt file named "hazModel", which is required to save in my documents folder to run the program. The file also contains the error message

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

On Sep 7, 2010, at 3:01 AM, Carlos Sanchez de La Lama wrote: > The machine I am targeting has some special requirements for some > operations, say: > > ADD or1, ir1, r5 > > would add ir1 (input reg 1) and r5 and put the result in or1 (output reg > 1). The point id that input and output regs have to go paired (this > meaning an addition of ir1 with whatever always goes to

Hi all, The machine I am targeting has some special requirements for some operations, say: ADD or1, ir1, r5 would add ir1 (input reg 1) and r5 and put the result in or1 (output reg 1). The point id that input and output regs have to go paired (this meaning an addition of ir1 with whatever always goes to or1, or an in general irX + whatever goes to orX). AFAIK, InstrInfo.td only allow

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

David's answers were correct. You are looking deep into the code when there is no reason to to so. 1. h(t|(X=x,Z=z)) = exp(Beta0 + XZBeta1) Most statisticians will tell you that this is an unwise model. The reason is that if you replace X with "X+1" the fit changes, which is almost never desirable. What if someone coded your dummy variable as 1/0 instead of 0/1 -- wouldn't