similar to: Generating log transformed random numbers

Displaying 20 results from an estimated 6000 matches similar to: "Generating log transformed random numbers"

2014 Oct 15
2
Test K-S con distribuciones LogNormales
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
2008 Apr 16
1
Confidence intervals of log transformed data
Hi I have a general statistics question on calculating confidence interval of log transformed data. I log transformed both x and y, regressed the transformed y on transformed x: lm(log(y)~log(x)), and I get the following relationship: log(y) = alpha + beta * log(x) with se as the standard error of residuals My question is how do I calculate the confidence interval in the original scale of x
2009 Apr 04
2
threshold distribution
Dear ALL I have a list of data below 0.80010 0.72299 0.69893 0.99597 0.89200 0.69312 0.73613 1.13559 0.85009 0.85804 0.73324 1.04826 0.84002 1.76330 0.71980 0.89416 0.89450 0.98670 0.83571 0.73833 0.66549 0.93641 0.80418 0.95285 0.76876 0.82588 1.09394 1.00195 1.14976 0.80008 1.11947 1.09484 0.81494 0.68696 0.82364 0.84390 0.71402 0.80293 1.02873 all of them are ninty. Nowaday, i try to find a
2012 Sep 29
5
Generating by inverting function
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
2003 Aug 28
2
ks.test()
Dear All I am trying to replicate a numerical application (not computed on R) from an article. Using, ks.test() I computed the exact D value shown in the article but the p-values I obtain are quite different from the one shown in the article. The tests are performed on a sample of 37 values (please see "[0] DATA" below) for truncated Exponential, Pareto and truncated LogNormal
2005 Mar 12
1
MLE for two random variables
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:
2005 Jun 08
1
Fitting Theoretical Distributions to Daily Rainfall Data
Dear List Members, I need a bit help about fitting some theoretical distributions (such as geometric, exponential, lognormal or weibull distribution) to the following *dry spell*, *wet spell*, *cycles (Wet-Dry or Dry-Wet)* from my meteorological (daily rainfall) data http://www.angelfire.com/ab5/get5/R.rainfall.txt only for rainy seasen (july - september) of 14 years only:
2009 Apr 03
2
Geometric Brownian Motion Process with Jumps
Hi, I have been using maxLik to do some MLE of Geometric Brownian Motion Process and everything has been going fine, but know I have tried to do it with jumps. I have create a vector of jumps and then added this into my log-likelihood equation, know I am getting a message: NA in the initial gradient My codes is hear # n<-length(combinedlr) j<-c(1,2,3,4,5,6,7,8,9,10)
2012 Aug 29
2
Estimation parameters of 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
2012 Aug 31
3
fitting 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
2005 Jun 29
2
MLE with optim
Hello, I tried to fit a lognormal distribution by using optim. But sadly the output seems to be incorrect. Who can tell me where the "bug" is? test = rlnorm(100,5,3) logL = function(parm, x,...) -sum(log(dlnorm(x,parm,...))) start = list(meanlog=5, sdlog=3) optim(start,logL,x=test)$par Carsten. [[alternative HTML version deleted]]
2003 Apr 09
3
plotting the lognormal density curve
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
2005 Jan 31
2
ML-Fit for truncated distributions
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
2007 Sep 07
1
How to obtain parameters of a mixture model of two lognormal distributions
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
2011 Nov 01
1
low sigma in lognormal fit of gamlss
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
2003 Aug 05
1
error message in fitdistr
Hi R lovers Here is a numerical vector test > test [1] 206 53 124 112 92 77 118 75 48 176 90 74 107 126 99 84 114 147 99 114 99 84 99 99 99 99 99 104 1 159 100 53 [33] 132 82 85 106 136 99 110 82 99 99 89 107 99 68 130 99 99 110 99 95 153 93 136 51 103 95 99 72 99 50 110 37 [65] 102 104 92 90 94 99 76 81 109 91 98 96 104 104 93 99 125 89
2010 Nov 19
2
simple loop problemo (Geo brownian motion)
I would like to plot multiple random walks onto the same graph. My p variable dictates how may random walks there will be. par(mfrow=c(1,1)) p <- 100 N <- 1000 S0 <- 10 mu <- 0.03 sigma <- 0.2 nu <- mu-sigma^2/2 x <- matrix(rep(0,(N+1)*p),nrow=(N+1)) y <- matrix(rep(0,(N+1)*p),nrow=(N+1)) t<- (c(0:N))/N for (j in 1:p) { z <- rnorm(N,0,1) x[1,j] <- 0 y[1,j]
2010 Sep 09
5
Help on simple problem with optim
Dear all, I ran into problems with the function "optim" when I tried to do an mle estimation of a simple lognormal regression. Some warning message poped up saying NANs have been produced in the optimization process. But I could not figure out which part of my code has caused this. I wonder if anybody would help. The code is in the following and the data is in the attachment. da <-
2013 Apr 16
2
Strange error with log-normal models
Hi, I have some data, that when plotted looks very close to a log-normal distribution. My goal is to build a regression model to test how this variable responds to several independent variables. To do this, I want to use the fitdistr tool from the MASS package to see how well my data fits the actual distribution, and also build a generalized linear model using the glm command. The summary
2002 Dec 10
1
Lognormal distribution
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