similar to: Need to help to get value for bigger calculation

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]]

The density of a lognormal should be 0 for negative arguments, but > dlnorm(-1) [1] NaN Warning message: NaNs produced in: dlnorm(x, meanlog, sdlog, log) A simple fix is to change dlnorm's definition to: function (x, meanlog = 0, sdlog = 1, log = FALSE) .Internal(dlnorm(x*(x>0), meanlog, sdlog, log)) It might be faster to put the same sort of adjustment into the internal code, but

Hello, I am trying to compute the density of a variable k that is either (1) Normally distributed; (2) Log-Normally distributed; or (3) follows proportional error distribution. I tried to search R-help and the answer for normal distribution was easy to find (please see 1c). I am not sure if my formula for dlnorm is correct (please see 2c below)? I really don't know what function to use for the

Another newbie question I got the 1 minute spine interpolation and 15 mean aggregation working with many thanks to Gabor Grothendieck using Zoo functions. I got a tip from Hasan Diwan to look at xts but it seemed I would make better progress using code from Gabor. Now I'm having trouble plotting this zoo object. I'm thinking I want a function to "split" the zoo object back to

Hi, I have some data on the effect of cycle shape (categorical) and frequency (continuous) on the efficiency of muscle contraction. My minimum adequate model is: m15<-glm(efficiency~cycle.shape*freq, family=quasipoisson) However, I wish to know where significant differences lie between specific combinations of treatments. I guess I want an equivalent of a post hoc test following an

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, I do not have a clue with is the following NOT working like I expect to do... (and I cannot find any answer at CRAN)... # This one is for my sample > x _ seq(3,10) # This two for parameters > a _ seq(2,4) > b _ seq(2,5) # This one for the likelihood of a sample >f _ function(a,b) { + prod(dlnorm(x,meanlog=a,sdlog=b)) + } > outer(a,b,f) [,1] [,2]

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

Dear All Can R compute the expected value of a random variable? Thanks in advance, Paul

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

Hello, Using the following lines of code, I created the following graph:

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

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

Hi R lovers! I'd like to know how to use the parameter 'start' in the function fitdistr() obviously I have to provide the initial value of the parameter to optimize except in the case of a certain set of given distribution Indeed according to the help file for fitdistr " For the following named distributions, reasonable starting values will be computed if `start'

?Hi, I was wondering if anyone has managed to get the preload_app and SIGUSR2 combination working well with upstart? My issue currently is that when I perform a SIGUSR2 against the master, check everything''s working and close off the old master, upstart then attempts to restart it because the original PID/binary are no longer running. What is the correct way of handling this? In my

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 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]]

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

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

Hi, I'm new at this, I'm very confused, and I think I'm missing something important here. In our pet example we have this: > fm <- lme(Orthodont) > plot(Orthodont) > plot(augPred(fm, level = 0:1)) which gives us a trellis plot with the females above the males, starting with "F03", "F04", "F11", "F06", etc. I thought the point of