similar to: AIC for lognormal model

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

Hi, I'm a bit puzzled by the gamlss fitting of exponential and lognormal data. Gamlss seems to think that exponentially distributed data fits better with a lognormal distribution, and vice versa. For example, X <- rexp(1000) Gexp <- gamlss(X~1,family=EXP) # X~1 is X tilde 1 GAMLSS-RS iteration 1: Global Deviance = 2037.825 GAMLSS-RS iteration 2: Global Deviance = 2037.825 Glno

I'm attempting to do model selection with AIC, using a glm and a lognormal distribution, but: fit1<-glm(BA~Year,data=pdat.sp1.65.04, family=gaussian(link="log")) ## gives the same result as either of the following: fit1<-glm(BA~Year,data=pdat.sp1.65.04, family=gaussian) fit1<-lm(BA~Year,data=pdat.sp1.65.04) fit1 #Coefficients: #(Intercept) Year2004 # -1.6341

Hi together, I always get an error message with using ylim in termplot(), like this: > x<-(1:10) > y<-(10:1) > l<-lm(y~x) > termplot(l,ylim=c(1,2)) Is this a bug, or is there another possibility to do that? Especially, I would like to use term.plot() for gamlss objects. Thanks for your help! Andreas -- Echte DSL-Flatrate dauerhaft f?r 0,- Euro*!

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 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

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.

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

Hi, What does it mean to say Lognormal distribution with a mean of 1.03E-6 with a range factor of 100 ? How can I find the lognormal distribution paramters from this information? Thanks, Tims [[alternative HTML version deleted]]

Hi, I am beginner with R and need firm guidance with my problem. I have seen some other threads discussing the subject of right censored data, but I am not sure whether or not this problem can be regarded as such. Data: I have a vector with laboratory test data (strength of wood specimens, example attached as txt-file). This data is the full sample. It is a common view that this kind of data

Hi R Users, I'm going to estimate via. ML the parameters in Poisson Lognormal model. The model is: x | lambda ~ Poisson(lambda) lambda ~ Lognormal(a,b) Unfortunately, I haven't found a useful package allowing for such estimation. I tried to use "poilog" package, but there is no equations and it's hard to understand what exactly this package really does. Using it I get the

I am trying to fit a lognormal distribution to a set of data and test its goodness of fit with regard to predicted values. I managed to get so far: > y <- c(2,6,2,3,6,7,6,10,11,6,12,9,15,11,15,8,9,12,6,5) > library(MASS) > fitdistr(y,"lognormal",start=list(meanlog=0.1,sdlog=0.1)) meanlog sdlog 1.94810515 0.57091032 (0.12765945) (0.09034437) But I would

Dear All, I have two questions regarding distribution fitting. I have several datasets, all left-truncated at x=1, that I am attempting to fit distributions to (lognormal, weibull and exponential). I had been using fitdistr in the MASS package as follows: fitdistr<-(x,"weibull") However, this does not take into consideration the truncation at x=1. I read another posting in this

Dear R-Helpers: I have tried everything I can think of and hope not to appear too foolish when my error is pointed out to me. I have some real data (18 points) that look linear on a log-log plot so I used them for a comparison of lm() and survreg. There are no suspensions. survreg.df <- data.frame(Cycles=c(2009000, 577000, 145000, 376000, 37000, 979000, 17420000, 71065000, 46397000,

I am trying to plot a lognormal density curve on top of an existing histogram. Can anybody suggest a simple way to do this? Even if someone could just explain how to plot a regular normal density curve on top of an existing histogram, it would be a big help. Also, is there some way to search through the R-help archives other than simple browsing? Thank you so much. Your help and time is greatly

Dear all, I am using gamlss (Package gamlss version 4.0-0, R version 2.10.1, Windows XP Service Pack 3 on a HP EliteBook) to relate bird counts to habit variables. However, most models fail because “the global deviance is increasing” and I am not sure what causes this behaviour. The dataset consists of counts of birds (duck) and 5 habit variables measured in the field (n= 182). The dependent

Full_Name: Anthony Gichangi Version: 1.90 OS: Windows XP Pro Submission from: (NULL) (130.225.131.206) The function rlnorm generates negative values for lognormal distribution. x- rlnorm(1000, meanlog = 0.6931472, sdlog = 1) Regards Anthony

Hi and thanks in advance to everyone who will be able to help me. I have 2 independent lognormal distributions with different means and sd. I want to plot them togheter in a 3D plot. How can i do this??? I'm getting crazy... Thanks Ale -- View this message in context: http://r.789695.n4.nabble.com/3-D-plot-of-a-bidimensional-lognormal-distribution-tp4633866.html Sent from the R help

I'm trying to produce empirical bayes estimates based on the lognormal model in disease mapping Is there a way this can be done in R? thanks Oarabile