similar to: Minimizing a Function with three Parameters

In the logistic regression model, there is no residual log (pi/(1-pi)) = beta_0 + beta_1*X_1 + ..... But glm model will return residuals What is that? How to understand this? Can we put some residual in the logistic regression model by replacing pi with pi' (the estimated pi)? log (pi'/(1-pi')) = beta_0 + beta_1*X_1 + .....+ ei Thanks! [[alternative HTML version deleted]]

Hi all I know about producing a minimal example to show my problem. But I'm having trouble producing a minimal example that displays this behaviour, so please bear with me to begin with. Observe: I create an array called model.mat. Some details on this: > str(model.mat) num [1:18, 1:4] -0.170 -0.304 -2.617 2.025 -1.610 ... - attr(*, "dimnames")=List of 2 ..$ : chr

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

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

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)#

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

Hola, Tengo una base de datos con estructura jerárquica en la que quiero clasificar observaciones en distintas categorías. En el caso más simple, tengo una variable con dos categorías (variable Y1) y dentro de cada una de ellas hay otras dos categorías (variable Y2). Además tengo una variable explicativa cuantitativa discreta X. El banco de datos sería de este tipo: X Y1 Y2 5 0 1 9 0 0 2

Dear all I used R2WinBUGS package's bugs() function to generate MCMC results. Then I tried to save the simulation draws in R, using read.bugs() function. Here is a simple test: ###################### library(coda) library(R2WinBUGS) #fake some data to test beta0=1 beta1=1.5 beta2=-1 beta3=2 N=200 x1=rnorm(N, mean=0,sd=1) x2=rnorm(N, mean=0,sd=1) x3=rnorm(N, mean=0,sd=1) lambda2= exp(beta0+

Estimados Hace unos días envié un correo porque tenía problemas para guardar los gráficos en el disco rígido, utilizando R server 9, comentaba que el código antes funcionaba pero que tenía fallas. No encuentro mi mensaje en la lista para continuar el hilo, encontré el problema, no lo comprendo del todo pero cambiando jpg por png funciona, aparentemente hay un inconveniente para guardar en jpg.

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

Hi Listers, seems I have forgotten some basics re offset in glm: data: counts (y) from locations off different size (area), explanatory variable: x Model: y ~ x+offset(area) Predictions (pred) using Poisson errors plot(x,y) and points(x,pred) gives neat "line" of estimated values. However, for ease of understanding graphs are better using plot(x,y/area). Question: How to display

I am trying to make a x-axis and y-axis titles with both a special character and a subscript. I am not being able to do this. I think its just a placing of my parenthesis, but I've tried (seemingly) everything. Even more, when I try the blog users code it works. Is it because I?m using longitudinal data? Even more. Is it possible to colour each one of the 15 lines with a different

Is there a way to do hierarchical (bayesian) logistic regression in R, the way we do it in BUGS? For example in BUGS we can have this model: model {for(i in 1:N) { y[i] ~ dbin(p[i],n[i]) logit(p[i]) <- beta0+beta1*x1[i]+beta2*x2[i]+beta3*x3[i] } sd ~ dunif(0,10) tau <- pow(sd, -2) beta0 ~ dnorm(0,0.1) beta1 ~ dnorm(0,tau) beta2 ~ dnorm(0,tau) beta3 ~

Say I have a formula Y ~ 1 + X, where X is a categorical variable. A previous thread showed how to evaluate this model using the mle package from "stats4" (see below). But, the user had to create the data matrix, X, including the column of one's for the regression constant. Is there a way to nest the linear formula in the code below, so the data matrix doesn't explicitly

Hi, everyone, Night. I have three questions about multiple linear regression in R. Q1: y=rnorm(10,mean=5) x1=rnorm(10,mean=2) x2=rnorm(10) lin=lm(y~x1+x2) summary(lin) ## In the summary, 'Residual standard error: 1.017 on 7 degrees of freedom', 1.017 is the estimate of the constance variance? Q2: beta0=lin$coefficients[1] beta1=lin$coefficients[2] beta2=lin$coefficients[3]

Dear All, I am trying to simulate data for a spline/piecewise regression model. I am missing something fundamental in my simulation procedure because when I try to fit my simulated data using the Gauss-Newton method in SAS, I am getting some wacky parameter estimates. Can anyone please check my simulation code and tell me what mistake I am making in generating data for spline model? Thank you

Hi, I am using the integrate function in some simulations in R (tried ver 2.12 and 2.15). The problem I have is that the last few rows do not integrate correctly. I have pasted the code I used. The column named "integral" shows the output from the integrate function. The last few rows have no integration results. I tried increasing the doses, number of subjects, etc.... this error occurs

Dear All, I am trying to specify the following fixed effects model for lme: y ~ constant1 - beta1*(x - beta2) where y is the response, x is the independent variable, and the operators above are real arithmetic operations of addition, subtraction, and multiplication. I realize that this model is just a reparameterization of y=beta0+beta1*x, but I am using this parameterization because I am