similar to: fitting lognormal censored data

Hi, I am trying to get the maximum likelihood estimator for lognormal distribution with censored data;when we have left, interval and right censord. I built my code in R, by writing the deriving of log likelihood function and using newton raphson method but my estimators were too high " overestimation", where the values exceed the 1000 in some runing of my code. is there any one can

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

Hello, I have being trying to estimate the parameters of the?generalized?exponential distribution. The random number generation for the GE distribution is?x<-(-log(1-U^(1/p1))/b), where U stands for uniform dist. The data i have generated to estimate the parameters is right censored and the code is given below; The problem is that, the newton-Raphson approach isnt working and i do not know what

The cenmle function is used to fit two sets of censored data and test if they are significantly different. I can print out the results of the analysis on the screen but can't seem to figure out how to access these results in R and assign them to new variables, e.g., assign the slope calculated with cenmle to the variable m. Any suggestions? Tom -- View this message in context:

Hi, I would like to write a function that finds parameters of a log-normal distribution with a 1-alpha CI of (x_lcl, x_ucl): However, I don't know how to optimize for the two unknown parameters. Here is my unsuccessful attempt to find a lognormal distribution with a 90%CI of 1,20: prior <- function(x_lcl, x_ucl, alpha, mean, var) { a <- (plnorm(x_lcl, mean, var) - (alpha/2))^2 b

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

Dear R-helpers, I was testing the truncgof CRAN package, found something that looked like a bug, and did my job: contacted the maintainer. But he did not reply, so I am resending my query here. I installed package truncgof and run the example for function ad.test. I got the following output: set.seed(123) treshold <- 10 xc <- rlnorm(100, 2, 2) # complete sample xt <- xc[xc >=

HI I am trying to analyse data which is left-censored (i.e. has values below the detection limit). I have been using the NADA package of R to derive summary statistics and do some regression. I am now trying to carry out regression on paired data where both my X and Y have left-censored data within them. I have tried various commands in R: rega = cenreg(Cen(conc, cens_ind) ~ Gp_ident))? with

So I am trying to use the mle command (from stats4 package) to estimate a number of parameters using data but it keeps throwing up this error message: Error in solve.default(oout$hessian) : Lapack routine dgesv: system is exactly singular: U[1,1] = 0 This error sometimes indicates that the list of starting values is too far from optimum but this is unlikely since I picked values close to where

Hello, I wish to?censor 10% of my sample units of 50 from a Weibull distribution. Below is the code for it. I will need to know whether what i have done is correct and if not, can i have any suggestion to improve it? Thank you ?p=2;b=120 n=50 r=45 t<-rweibull(r,shape=p,scale=b) meantrue<-gamma(1+(1/p))*b meantrue cen<- runif(n-r,min=0,max=meantrue) cen Chris Guure Researcher,

Hello, maybe that my Question is a "beginner"-Question, but up to now, my research didn't bring any useful result. I'm trying to fit a distribution (e.g. lognormal) to a given set of data (ML-Estimation). I KNOW about my data that there is a truncation for all data below a well known threshold. Is there an R-solution for an ML-estimation for this kind of data-problem? As

Would someone be able to help with this question? I'm using the Gcmrec, Survrec, and Design packages to do a power analysis on simulated data. I'm receiving an error after using the Survr function that all data must have a censoring time even after using the gcmrec function: newdata<-addCenTime(olddata). My program is below. I'd greatly appreciate any help!

(1) ...need to speed up a monte-carlo sampling...any suggestions about how I can get R to use all 8 cores of a mac pro would be most useful and very appreciated... (2) spent the last few hours trying to get pnmath to compile under os- x 10.5.4... using gcc version 4.2.1 (Apple Inc. build 5553) as downloaded from CRAN, xcode 3.0... ...xcode 3.1 installed over top of above after

Dear all, I tried to use "step" function to do model selection, but I got an error massage. What I don't understand is that data as data.frame worked well for my other programs, how come I cannot make it run this time. Could you please tell me how I can fix it? ***************************************************************************************************

Dear Colleagues, I am attempting to develop an optimization routine for a river suspended sediment mixing model. I have Calcium and Magnesium concentrations (%) for sediments from 4 potential source areas (Topsoil, Channel Banks, Roads, Drains) and I want to work out, based on the suspended sediment calcium and magnesium concentrations, what are the optimal contributions from each source area to

Dear all, I am interested to evaluate reclassification using net reclassification improvement and Integrated Discrimination Index IDI after survival analysis (Cox proportional hazards using stcox). I search a R package or a R code that specifically addresses the categorical NRI for time-to-event data in the presence of censored observation and, if possible, at different follow-up time points. I

Hello, I want to find the parameters mu and sigma that minimize the following function. It's important, that mu and sigma are strictly positive. ----------------- optimiere = function(fmean,smean,d,x,mu,sigma) { merk = c() for (i in 1:length(d)) merk=c(merk,1/(d[i]^2)*(d[i]-1/(fmean*(1-plnorm(x[i],mu,sigma))))^2) return(sum(merk)) } ----------------- To do that I'm using the nlm

Hi, In another thread ("PBSmapping and shapefiles") I asked for an easy way to read "shapefiles" and transform them in data that PBSmapping could use. One person is exploring some ways of doing this, but it is possible I'll have to do this "manually". With package "maptools" I am able to extract the information I need from a shapefile but it is

Hello, I've the following setting: (1) Data from a source without truncation (x) (2) Data from an other source with left-truncation at threshold u (xu) I have to fit a model on these these two sources, thereby I assume that both are "drawn" from the same distribution (eg lognormal). In a MLE I would sum the densities and maximize. The R-Function could be:

Hi, Is there a way, preferably with R, to read shapefiles and transform them in a format that I could then use with package PBSmapping? I have been able to read such files into R with maptools' read.shape and plot it with plot.Map, but I'd like to bring the data to PBSmapping and plot from there. I also looked at the package shapefile, but it does not seem to do what I want