similar to: help on setting boundaries for generating random numbers

Hello, my question is about the data handling. I have a data set that is lined as: 4 1 17 1 1 -5.1536 -0.1668 -2.3412 -0.5062 0.9621 0.3640 0.3678 -0.5081 -0.2227 0.8142 -0.0389 -0.0445 -0.0578 -0.1175 -0.1232 0.8673 -0.1033 -0.0796 -0.0341 -0.1716 -0.1801 -0.7014 0.6578 0.5611 4 1 17 2 1 -5.1536 -0.1668 -2.3412 -0.5062 0.9621 0.3640 0.3678 -0.5081 -0.2227 0.8142 -0.0389 -0.0445

Hi! I 've a problem to have a lognorm distribution with mean=1 and var (or sigma)=1. rlnorm(1000,0,0) rlnorm(1000,1,1) rlnorm(1000,0,1) .... ? Can you help me?

(Sent this to r-help back in October, got no comments, forgot to re-submit it as a bug report.) There are a few inconsistencies, at least, in some of the functions that generate random deviates from particular distributions (I think they're bugs because they're inconvenient, but maybe someone can make an argument for the current behavior). If people think these are really bugs I can

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

Dear all, Is there a way to generate K numbers of integer (K = 10^6). The maximum value of the integer is 200,000 and minimum is 1. And the occurrences of this integer follows a lognormal distribution. - Gundala Viswanath Jakarta - Indonesia

Hi, folks, As seen in the following codes: x1=rlnorm(10) x2=rlnorm(10,mean=2) y=rlnorm(10,mean=10)### Fake dataset linmod=lm(log(y)~log(x1)+log(x2)) After the regression, I would like to know the mean of y. Since log(y) is normal and y is lognormal, I need to know the mean and variance of log(y) first. I tried mean (y) and mean(linmod), but either one is what I want. Any tips? Thanks in

Dear all, I'm having a few problems trying to reshape a data frame. I tried with reshape{stats} and melt{reshape} but I was missing something. Any help is very welcome. Please find details below: ################################# # data in its original shape: indiv <- rep(c("A","B"),c(10,10)) level.1 <- rpois(20, lambda=3) covar.1 <- rlnorm(20, 3, 1) level.2

rlnorm takes two 'shaping' parameters: meanlog and sdlog. meanlog would appear from the documentation to be the log of the mean. eg if the desired mean is 1 then meanlog=0. So to generate random values that fit a lognormal distribution I would do this: rlnorm(N , meanlog = log(mean) , sdlog = log(sd)) But when I check the mean I don't get it when sdlog>0. Interestingly I

Hi list, Does anyone know the code behind rlnorm(n, meanlog = 0, sdlog = 1)? I am going to write it in c#. thanks Alireza [[alternative HTML version deleted]]

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]]

Dear All, I would like to be able to plot histograms/densities on a semi-log or log-log scale. I found several suggestions online http://tolstoy.newcastle.edu.au/R/help/05/09/12044.html https://stat.ethz.ch/pipermail/r-help/2002-June/022295.html http://www.harding.edu/fmccown/R/#histograms Now, consider the code snippet taken from http://www.harding.edu/fmccown/R/#histograms # Get a random

Dear R Expert   allow me to ask a quick qestion: I have a mean value of 6 and a SD of 3 describing my distribution. I would like to "convert" this distribution into a log normal distribution that would best describe it when resimulated using log normal distribution. Currently I am using another software to estimate the respective mean and SD on the log scale and the results are: 1.6667

Dear All,   I have a questions I would like to ask about and wonder if you have any thoughts to make it work in R.   1. I work in the field of medicine where physiologic variables are often simulated, and they can not have negative values. Most often the assumption is made to simulate this parameters with a normal distribution but in the "log-domain" to avoid from negative values to be

I am trying to run a confirmatory factor analysis using the SEM package. My data are ordinal. I have read http://socserv.mcmaster.ca/jfox/Misc/sem/SEM-paper.pdf. When I apply the hetcor function, I receive the following error: Error in checkmvArgs(lower = lower, upper = upper, mean = mean, corr = corr, : at least one element of 'lower' is larger than 'upper' Example:

Dear friends, I am trying to understand and implement instrumental variables regression using R. I found a small (simple) example here which purportedly illustrates the mechanics (using 2-stage least-squares): http://www.r-bloggers.com/a-simple-instrumental-variables-problem/ Basically, here are the R commands (reproducible example) from that site: # ------ begin R library(AER)

Dear all I have a question about quantiles standard error, partly practical partly theoretical. I know that x<-rlnorm(100000, log(200), log(2)) quantile(x, c(.10,.5,.99)) computes quantiles but I would like to know if there is any function to find standard error (or any dispersion measure) of these estimated values. And here is a theoretical one. I feel that when I compute median from given

Hello I am trying to find an automated way of fitting a mixture of normal and log-normal distributions to data which is clearly bimodal. Here's a simulated example: x.1<-rnorm(6000, 2.4, 0.6)x.2<-rlnorm(10000, 1.3,0.1)X<-c(x.1, x.2) hist(X,100,freq=FALSE, ylim=c(0,1.5))lines(density(x.1), lty=2, lwd=2)lines(density(x.2), lty=2, lwd=2)lines(density(X), lty=4) Currently i am using

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

Dear all, I have a code that generates data vectors within R. For example assume: z <- rlnorm(1000, meanlog = 0, sdlog = 1) Every time a vector has been generated I would like to export it into a csv file. So my idea is something as follows: for (i in 1:100) { z <- rlnorm(1000, meanlog = 0, sdlog = 1) write.csv(z, "c:/z_i.csv") Where "z_i.csv" is a filename that is

Hello, I wonder if anyone has a function for calculating confidence intervals for ratios. I have mortality rates calculated for several groups from sample data -- but these can be treated as ratios?). I am entertaining the idea of bootstrapping them. Any help would be greatly appreciated. TIA, Marwan ---------------------------------------------------------------------------- ------------