similar to: K-S and X2 statistical tests-question

Dear R users, I try to write an rcode for a paranetric weibull model with unobserved heterogeneity. The data I have are giuven below. ( The data comes from Keiding and Klein artricle published in the Statistics in Medicine 1996) . While the simple weibull model runs perfectly ( I get -2*loglikelihood 1093,905), the weibull frailty model does not run well. The code I write is :

Hi R help list I'm simulating logistic regression data with a specified odds ratio (beta) and have a problem/unexpected behaviour that occurs. The datasets includes a lognormal exposure and diseased and healthy subjects. Here is my loop: ors <- vector() for(i in 1:200){ # First, I create a vector with a lognormally distributed exposure: n <- 10000 # number of study subjects

Apologies for the empty post that went to the list... On ??????, ???????????????????? 28, 2005 9:11, Paolo Bulla wrote: > Hello anyone, > I'm trying to install R on Mandriva 2006 distribution via rpm file with the > line > urpmi R-2.0.0-1mdk.i586.rpm ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ This is an older rpm that Michele Alzeta contributed for Mandrake 10.0 (and that was a long time

Hola Ruben, Sí precisamente es lo que comentas, en matemáticas no se suele llamar bucketización (este término se emplea más en informática) sino datos agrupados. Pero la idea es la que tu mismo dices. Respecto a las gráficas que has puesto, me han aclarado mucho sobre el tema, gracias. Si realizo lo mismo, por ejemplo con nbucket=1000 sigo obteniendo un p-valor de 1. Es decir, que casi le

Hello! I have just uninstalled the R 2.4.1 that i was using and installed the latest version (R 2.12.1). In order to read a text file, i was copying the text file to C/Program Files/R/R 2.4.1, and the previous version was able to read it from there. I did the same think for the new version (copy text file to C/Program Files/R/R 2.12.1) and i tried to open it: data <-

Hi, Does anyone know what the mean value of a lognormal distribution in base-2 is? I am simulating stochastic population growth and if I were working in base-e, I would do:lambda <- 1.1 #multiplicative growth rates <- 0.6 #stochasticity (std. dev)lognormal <- rlnorm(100000, log(lambda) - (s^2)/2, s)## or lognormal <- exp( rnorm( 100000, log(lambda) - (s^2)/2,

Dear all, I want to compare some different models for a dataset by QQ plots and AIC. I get the following AICs: - linear model: 19759.66 - GAMLSS model: 18702.7 - linear model with lognormal response: -7862.182 The QQ plots show that the lognormal model fits better than the linear model, but still much worse than the GAMLSS. So, in my opinion, the AIC of the lognormal model should be between the

Hi, I'm playing around with gamlss and don't entirely understand the sigma result from an attempted lognormal fit. In the example below, I've created lognormal data with mu=10 and sigma=2. When I try a gamlss fit, I get an estimated mu=9.947 and sigma=0.69 The mu estimate seems in the ballpark, but sigma is very low. I get similar results on repeated trials and with Normal and

Dear R users I use simulated data to evaluate a model by sampling the parameters in my model from lognormal distributions. I would like these (lognormal distributed) parameters to be correlated, that is, I would like to have pairwise samples of 2 parameters with a given correlation coefficient. I have seen that a covariance matrix can be fixed when generating random variables from a

Hi, I am trying to learn lognormal mixture models with EM. I was wondering how does one compute the log likelihood. The current implementation I have is as follows, which perform really bad in learning the mixture models. __BEGIN__ # compute probably density of lognormal. dens <- function(lambda, theta, k){ temp<-NULL meanl=theta[1:k] sdl=theta[(k+1):(2*k)]

Dear all, I'm trying to estimate the parameters of a lognormal distribution fitted from some data. The tricky thing is that my data represent the time at which I recorded certain events. However, in many cases I don't really know when the event happened. I' only know the time at which I recorded it as already happened. Therefore I want to fit the lognormal from the cumulative

Hello, I was interested in trying to write an R script to calculate a UCL for a lognormal distribution using the Chebyshev Inequality — MVUE Approach (based on EPA’s guidance found in http://www.epa.gov/oswer/riskassessment/pdf/ucl.pdf). This looks like it should be straight forward, but I am need to calculate an MVUE for the population mean and an MVUE for the population variance, which requires

Hi all, I have a data of lognormal distribution (sample size > 1,000,000). What I want to do is 1) to test if my dataset is a lognormal distribution or not (Histogram shows a nice normal distribution in log scale but I want to check) 2) two subsets from this dataset have same mean or not (like "t test" of normal distribution) What I tried are 1) ad.test{truncgof} of R, which is a

Hi, I have a right-censored (positive) random variable (e.g. failure times subject to right censoring) that is observed for N subjects: Y_i, I = 1, 2, ..., N. Note that Y_i = min(T_i, C_i), where T_i is the true failure time and C_i is the censored time. Let us assume that C_i is independent of T_i. Now, I would like to generate another random variable U_i, I = 1, 2, ..., N, which is

Dear all: First, apologies for cross-posting multiplicities and for a query that is more analytically related than S-language related. The bottom-line wish is: Could you please provide and advice, references, etc on S software approaches for fitting a distribution with density: p*g(x) + (1-p)*f(x) where g(x) is the familiar lognormal 2-parameter density and f(x) is Pareto as defined below?

I am trying to fit a lognormal distribution on interval-censored data. Some of my intervals have a lower bound of zero. Unfortunately, it seems like survreg() cannot deal with lower bounds of zero, despite the fact that plnorm(0)==0 and pnorm(-Inf)==0 are well defined. Below is a short example to reproduce the problem. Does anyone know why survreg() must behave that way? Is there an alternate

> -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch [SMTP:r-help-bounces at stat.math.ethz.ch] On Behalf Of Mark Miller > Sent: Thursday, November 17, 2005 10:16 AM > To: r-help at stat.math.ethz.ch > Subject: [R] Standard Error > > I have worked out that when I fit data I get an estimate and a standard error, > but all the definitions I can find

Dear List, I have read that a lognormal mixture model having a pdf of the form f(x)=w1*f1(x)+(1-w1)*f2(x) fits most data sets quite well, where f1 and f2 are lognormal distributions. Any pointers on how to create a function that would produce the 5 parameters of f(x) would be greatly appreciated. > version _ platform i386-pc-mingw32 arch i386 os

Dear, I'm very new to R Gui and I have to make an assignment on Gamma Regressions. Surfing on the web doesn't help me very much so i hope this forum may be a step forward. The question sounds as follows: The data set is in the library MASS first install library(MASS) then type data(mammals) attach(mammals) Assignment: Fit the gamma model and lognormal model for the mammals data.

Hi Again, I think I've solved my problem, but please tell me if you think I'm wrong, or you can see a better way! A plot of the integrand showed a very sharp peak, so I was running into the integrand "feature" mentioned in the note. I resolved it by limiting the range of integration as shown here: -------------------------------------------------- function (x, meanlog = 0,