similar to: Estimation parameters of lognormal censored data

Hi , I am trying to get some estimator based on lognormal distribution when we have left,interval, and right censored data. Since, there is now avalible pakage in R can help me in this, I had to write my own code using Newton Raphson method which requires first and second derivative of log likelihood but my problem after runing the code is the estimators were too high. with this email ,I provide

In hvm enviroment, acpi failed. why? centos5.1 =================================================== [root@hvm001 ~]# xm dmesg __ __ _____ _ ____ ___ ____ _ ____ \ \/ /___ _ __ |___ / / | |___ \ / _ \___ \ ___| | ___| \ // _ \ \047_ \ |_ \ | | __) |_| (_) |__) | / _ \ |___ \ / \ __/ | | | ___) || |_ / __/|__\__, / __/ | __/ |___) | /_/\_\___|_| |_| |____(_)_(_)_____| /_/_____(_)___|_|____/

Hello, I am trying to generate random survival times by inverting the function,  S(t)= exp(b*F(t)), where b is constant and F(t) is some cumulative distribution function, let say that F(t) is cdf of normal distribution or any others distributions.   as we know that S(t) has uniform distribution on  (0,1) so we can write that U= exp(b*F(t)), where U is uniform (0,1). Now to generat the time t, we

I'm writing a function and keep getting the following error message. myfunc <- function(lst) { lst <- list(roots = c("car insurance", "auto insurance"), roots2 = c("insurance"), prefix = c("cheap", "budget"), prefix2 = c("low cost"), suffix = c("quote", "quotes"), suffix2 = c("rate",

Dear Colleagues, I have a dataframe with variables: [1] "ID" "category" "a11" "a12" "a13" "a21" [7] "a22" "a23" "a31" "a32" "b11" "b12" [13] "b13" "b21"

I'm running samba 2.0.5a on a Sun Sparc 5 with Solaris 2.6 and trying to use smbclient to copy an entire directory tree to a Windows NT 4.0 box. I'm using the recurse command and can create first level directories but I am unable to create new subdirectories in any of them. For example I created the following directory structure on the Sun: 1 % ls -R .: d1/ d2/ ./d1: f11 f12

I have a really long functions, and at the end of the function, I am using a if statement to tag certain keywords based on whether they have certain values contained in them. However, the if statement doesn't seem to work. When I had split up the commands into various functions, it worked fine, but I'm not sure what going on now that it's combined into a single function. myfunc

Hi! I have following data which is left truncated say at 10. I am trying to estimate the parameters of the Truncated Lognormal distribution to this data as given below. (I have referred to R code appearing in an earlier post - http://finzi.psych.upenn.edu/Rhelp10/2008-October/176136.html) library(MASS) x <- c(600.62,153.05,70.26,530.42,3440.29,97.45,174.51,168.47, 116.63,36.51, 219.77,

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

I have a repetative task in R and i'm trying to find a more efficient way to perform the following task. lst <- list(roots = c("car insurance", "auto insurance"), roots2 = c("insurance"), prefix = c("cheap", "budget"), prefix2 = c("low cost"), suffix = c("quote", "quotes"),

From: Jyri Sarha <jsarha at ti.com> I optimized Speex resampler for NEON capable ARM CPUs. The first patch should speed up resampling on any platform that can spare the increased memory usage. It would be nice to have these merged to the master branch. Please let me know if there is anything I can do to help the the merge. The patches have been rebased on top of master branch in

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

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

Dear all I am struggling to fit data which form something like CDF by lognorm. Here are my data: proc <- c(0.9, 0.84, 0.5, 0.16, 0.1) size <- c(0.144, 0.172, 0.272, 0.481, 0.583) plot(size, proc, xlim=c(0,1), ylim=c(0,1)) fit<-nls(proc~SSfpl(size, 1, 0, xmid, scal), start=list(xmid=0.2, scal=.1)) lines(seq(0,1,.01), predict(fit, newdata=data.frame(sito=seq(0,1,.01))), col=2) I tried

On 21 September 2012 09:28, Sebastien DELDON-GNB <sebastien.deldon at st.com> wrote: > declare <16 x float> @llvm.arm.neon.vmaxs.v16f32(<16 x float>, <16 x float>) nounwind readnone > > llc fails with following message: > > SplitVectorResult #0: 0x2258350: v16f32 = llvm.arm.neon.vmaxs 0x2258250, 0x2258050, 0x2258150 [ORD=3] [ID=0] > > LLVM ERROR: Do not

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

Buenos días, *Muchas gracias, todas las aportaciones han sido bien útiles.* Las he tenido en cuenta y he pasado los datos con el R, siguiendo el siguiente comando: *modeloRTUN2<-lm(AVE.~ Tariff + d1 + d2 + d3 + d4 + d5 + d6 + d7 + d8 + d9+ d10 + d11+ d12+ d13+ d14+ d15+ d16+ d17+ d18+ d19+ d20 +d21 + Tariff*d1 + Tariff*d2 + Tariff*d10)* *summary(modeloRTUN2)* Siendo: AVE. = Variable

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 >=

* fitdistr? * it seems unusual (to me) to fit directly to the data with lognormal... fitting a normal to the log of the data seems more in keeping with the assumptions associated with that distribution. -- Sent from my phone. Please excuse my brevity. On July 10, 2017 7:27:47 AM PDT, PIKAL Petr <petr.pikal at precheza.cz> wrote: >Dear all > >I am struggling to fit data which form

How about proc <- c(0.9, 0.84, 0.5, 0.16, 0.1) size <- c(0.144, 0.172, 0.272, 0.481, 0.583) plot(size, proc, xlim=c(0,1), ylim=c(0,1)) fit<-nls(proc~plnorm(size, log(xmid), sdlog, lower=FALSE), start=list(xmid=0.2, sdlog=.1)) summary(fit) lines(fitted(fit)~size) -pd > On 10 Jul 2017, at 16:27 , PIKAL Petr <petr.pikal at precheza.cz> wrote: > > Dear all > > I am