similar to: tests of lognormal distribution

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 >=

Dear Carlos, Duncan and everyone You may have already sorted the matter by now, but since I have not seen anything posted since Duncan's reply, here I go. I apologize in advance for the spam, if it turns out I've missed some post. I think the test and the implementation of the truncgof package are just fine. I've done Carlos' experiment (repeatedly generating samples and testing

hello! i don't want to test my sample data for normality, but exponential- lognormal- or gammadistribution. as i've learnt the anderson-darling-test in R is only for normality and i am not supposed to use the kolmogorov-smirnov test of R for parameter estimates from sample data, is that true? can you help me, how to do this anyway! thank you very much! nadja

Hi, all ecdf function (Empirical Cumulative Distribution Function) in "stats" package counts from smaller values to larger values. However, I want to draw it by counting from larger value to smaller values and I couldn't find options for this purpose. How can I draw ecdf or ecdf like graph by counting from larger values to smaller values. Thank you in advance. Hyunchul Kim

Hello all, Would somebody be kind enough to show me how to do a KS test in R for a lognormal distribution with ESTIMATED parameters. The R function ks.test()says "the parameters specified must be prespecified and not estimated from the data" Is there a way to correct this when one uses estimated data? Regards, Kwabena. -------------------------------------------- Kwabena Adusei-Poku

Hi, I am trying to learn lognormal mixture models with EM. I was wondering how does one compute the log likelihood. The current implementation I have is as follows, which perform really bad in learning the mixture models. __BEGIN__ # compute probably density of lognormal. dens <- function(lambda, theta, k){ temp<-NULL meanl=theta[1:k] sdl=theta[(k+1):(2*k)]

Hi, all I drew a barplot with > barplot(data1, beside=TRUE) and then, I want to labels with hist() like x-axis ticks. How can I do this? Thanks in advance, Hyunchul

Dear all, I'm trying to estimate the parameters of a lognormal distribution fitted from some data. The tricky thing is that my data represent the time at which I recorded certain events. However, in many cases I don't really know when the event happened. I' only know the time at which I recorded it as already happened. Therefore I want to fit the lognormal from the cumulative

Hi, all How to format and join strings ? For example, like following short python examples. ********* name1 = 'sample-plot' filename = '%s.png' % name1 inputdir = '/path/to/dir' os.path.join(inputdir, filename) ********** Best, Hyunchul Kim [[alternative HTML version deleted]]

Hi, What does it mean to say Lognormal distribution with a mean of 1.03E-6 with a range factor of 100 ? How can I find the lognormal distribution paramters from this information? Thanks, Tims [[alternative HTML version deleted]]

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

Hi and thanks in advance to everyone who will be able to help me. I have 2 independent lognormal distributions with different means and sd. I want to plot them togheter in a 3D plot. How can i do this??? I'm getting crazy... Thanks Ale -- View this message in context: http://r.789695.n4.nabble.com/3-D-plot-of-a-bidimensional-lognormal-distribution-tp4633866.html Sent from the R help

I'm attempting to do model selection with AIC, using a glm and a lognormal distribution, but: fit1<-glm(BA~Year,data=pdat.sp1.65.04, family=gaussian(link="log")) ## gives the same result as either of the following: fit1<-glm(BA~Year,data=pdat.sp1.65.04, family=gaussian) fit1<-lm(BA~Year,data=pdat.sp1.65.04) fit1 #Coefficients: #(Intercept) Year2004 # -1.6341

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

Hi, I am beginner with R and need firm guidance with my problem. I have seen some other threads discussing the subject of right censored data, but I am not sure whether or not this problem can be regarded as such. Data: I have a vector with laboratory test data (strength of wood specimens, example attached as txt-file). This data is the full sample. It is a common view that this kind of data

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,

Hi,R-list! I am a newbie to the R and what I want to know mostly now is as follow: Using "rnorm()",we could get the random numbers with normal distribution, but how to generate the random numbers with the lognormal distribution? Thank you in advance! 仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭guangxing at ict.ac.cn 仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭2005-11-18

Dear List, I am trying to fit Truncated Lognormal to a data set that is 'truncated' from above a certain value, say, 0.01. Below is what I was able to come up with. I would appreciate it if you could review and make any necessary changes. # This is modified off the code for 'dtnorm' of library(msm). dtlnorm <- function (n, mean = 0, sd = 1, lower = -Inf, upper = Inf) {

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,

Dear all, I want to compare some different models for a dataset by QQ plots and AIC. I get the following AICs: - linear model: 19759.66 - GAMLSS model: 18702.7 - linear model with lognormal response: -7862.182 The QQ plots show that the lognormal model fits better than the linear model, but still much worse than the GAMLSS. So, in my opinion, the AIC of the lognormal model should be between the