similar to: Help on probability distribution question

Dear?All, ? a few weeks ago I have posted a question on the R help listserv that?some of you have responded to with a great solution, would like to thank you for that? again.?I thought I would reach out to you with the issue I am trying to solve now. I have posted the question a few days ago, but probably it was not?clear enough, so I thought i try it again.?At times I have a multivariate example

Dear All,   is there a way to set low and high limits to a simulation with rlnorm()?   as an example:  a <-rlnorm(500,0.7,1)     I get the summary of   Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1175 1.0590 2.1270 3.4870 4.0260 45.3800 I would like to set limits so that the simulated values minimum would be greater then 0.5 and maximum of less than 30. If during simulation a

Dear All,   I often have to work with certain models in which I try to "reproduce" a distribution the best I can with very little known information avaible. Is there a package or function in R that could best reproduce a probability distribution using only the mean, median and SD values availble without knowing the actual distribution type to begin with and/or the covariance matrix (for

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 am using the following commands to generate a given dataset:   a <-c(0.348,0.007,0.503,0.58,0.21) cov <-c(0.0448,0,0,0,0,0.0001,0.0001,0,0,0,-0.0055,-0.0005,0.0495,0,0,0.0218,0.0009,-0.0253,0.1103,0,-0.0102,-0.0007,0.00631,0.0067,0.0132) b <-matrix(cov,nrow=5, ncol = 5, byrow = TRUE,dimnames = NULL) g <-mvrnorm(10000,a,b)   is there a way to place limits on the simulated

Dear All,   thanks to Berend, my question posted yesturday was solved succesfully here: http://r.789695.n4.nabble.com/hep-on-arithmetic-covariance-conversion-to-log-covariance-td4646068.html . I posted the question with the assumption of using the results with rlnorm.rplus() from compositions. Unfortunatelly, I am not getting reasonable enough outcome. Am I applying the results wrongfully? The

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 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

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

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

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]]

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?

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

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,   wondering if someine can access the link to the randsamp code referenced in the R-help archive here: http://www.mail-archive.com/r-help@stat.math.ethz.ch/msg75645.html ? I have tried but for whatever reason I can not get trough. My problem seems to be similar to what the author originally described there, so having access to it would be great. Else, if you have any thougths on sampling

I have some data where the frequency is heavily weighted on the lower end. So I have lots of low values with very few higher values. I would like to find breakpoints that cover the data with as much detail as possible. I find that if I use hist() to automatically find the breaks for me it finds breaks that are too coarse for the low values. I have tried the other algorithms (like 'Scott'

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:

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,