search for: dlnorm

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 t...

In theory, dlnorm(x, ...) == dnorm(log(x), ...)/x, x>0. Unfortunately, when sd=0, dlnorm and plnorm return NaN, while dnorm returns (if(x != mean)0 else Inf) and pnorm returns (if(x<mean)0 else 1). [A numerical optimization, maxLik{maxLik}, reported the NaNs for me.] help('dlnorm') says, "dl...

Dear All, Consider the following code: pdf(file="figure.pdf",family="URWPalladio") curve(dlnorm(x,0,1.5),0,10,xlim=c(0,10),ylim=c(0,0.85),axes=F,xlab="",ylab="f") segments(exp(-1.5^2),0,exp(-1.5^2),dlnorm(exp(-1.5^2),0,1.5),lty="dashed") segments(1,0,1,dlnorm(1,0,1.5),lty="dashed") segments(exp(1.5^2),0,exp(1.5^2),dlnorm(exp(1.5^2),0,1.5),lty="dash...

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]]

I've found some funny behavior in the dlnorm() function that I believe to be a bug. For example, the following command: > dlnorm( c(5e-323, 5e-324, 5e-325), 0, 0.3 ) Produces the output below, including the NaN value that should (ideally would be) zero. [1] 0 NaN 0 Warning message: In dlnorm(c(4.94065645841247e-323, 4.94065645841247...

...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 density of k with a proportional error distribution. Please help, Mahesh 1. Normal distribution: *a) *k=qk+h, hÎN(0, w) *b) *then kÎN(qk, w) *c) *density of a variable k: dnorm(x,mean=qk,sd=w,l...

...n 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: function(x,xu,u,mu,sigma) dlnorm(x,mu,sigma)+(dlnorm(xu,mu,sigma)/(plnorm(u,mu,sigma))) So I tried to use the function FITDISTR for the MLE. But unfortunately it only accepts ONE random variable. Then I tried to combine x and xu in a vector, but that doesn't work, too, because the length of x and xu differs. Does anybody has...

Hello R-Experts, I want to calculate values like 15^200 or 17^300 in R. In normal case it can calculate the small values of b (a^b). I have fixed width = 10000 and digits = 22 but still answers are Inf. How to deal the cases like these? Thanks in advance. Regards, rehena [[alternative HTML version deleted]]

...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] [,3] [,4] [1,] 2.301348e-21 2.301348e-21 2.301348e-21 2.301348e-21 [2,] 2.301348e-21 2.301348e-21 2.301348e-21 2.301348e-21 [3,] 2.301348e-21 2.301348e-21 2.301348e-21 2.301348e-21 # interestingly, the foll...

...u99 -dynamiclib -Wl,- headerpad_max_install_names -mmacosx-version-min=10.4 -undefined dynamic_lookup -single_module -multiply_defined suppress -L/usr/local/ lib -o pnmath.so bd0.o beta.o chebyshev.o choose.o d1mach.o dbeta.o dbinom.o dcauchy.o dchisq.o dexp.o df.o dgamma.o dgeom.o dhyper.o dlnorm.o dlogis.o dnbeta.o dnbinom.o dnchisq.o dnf.o dnorm.o dnt.o dpois.o dt.o dunif.o dweibull.o fmax2.o fmin2.o ftrunc.o gamma.o gamma_cody.o i1mach.o imax2.o imin2.o lbeta.o lgamma.o lgammacor.o mlutils.o pbeta.o pbinom.o pcauchy.o pchisq.o pexp.o pf.o pgamma.o pgeom.o phyper.o plnorm.o plogis...

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

...#- require(stats) mapply(function(x, meanlog, sdlog, ...){ lower <- min(max(0, x-sdlim*sqrt(x)), exp(meanlog-sdlim*sdlog)) upper <- max(x+sdlim*sqrt(x), exp(meanlog+sdlim*sdlog)) integrate(function(t, x, meanlog, sdlog) exp(dpois(x,t,TRUE) + dlnorm(t, meanlog, sdlog, TRUE)), lower = lower, upper = upper, x = x, meanlog = meanlog, sdlog = sdlog, rel.tol = rel.tol, ...)$value }, x, meanlog, sdlog, ... ) } ----...

...they achieve a unit distance in another distribution of longer times (d2), cf example below. If I have a mixed sample that contain X% of fast walkers and (100-X)% of slow walkers, *is it possible to use it to estimate d2, as well as X?* R example: walk.fast = sample(seq(1,5,length.out=1000), prob=dlnorm(seq(1,5,length.out=1000)), replace=T) walk.slow = sample(seq(1,5,length.out=1000), prob=dlnorm(seq(1,5,length.out=1000), meanlog=1.2), replace=T) percentage.fast =0.8 walk.all = c(sample(walk.fast, percentage.fast*1000), sample(walk.slow, (1-percentage.fast)*1000 ) ) plot(density(walk.fast, from=...

...136.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, 231.67,110.62,173.11,202.85,689.27,23.82,234.15,625.89,192.88,29.53,109.99, 80.53, 46.85,82.69,231.53,75.38,217.52,85.29,182.18) ltlnorm <- function(x, meanlog, sdlog, log=FALSE) { dlnorm(x, meanlog, sdlog, log = FALSE) / plnorm(1, meanlog, sdlog, lower.tail = TRUE, log.p = FALSE) } meanlog = mean(log(x)) sdlog = sd(log(x)) ltlnormfit <- fitdistr(x,ltlnorm, start=list(meanlog,sdlog)) I get following error - Error in dlnorm(x, meanlog, sdlog, log = FALSE) : element 3 is empt...

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

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

...information to my own query, this function gets to the core of the problem, which I think lies in the behaviour of 'integrate'. ------------------------------------- function (x, meanlog = 0, sdlog = 1, ...) { require(stats) integrand <- function(t, x, meanlog, sdlog) dpois(x,t)*dlnorm(t, meanlog, sdlog) mapply(function(x, meanlog, sdlog, ...) # (1/gamma(x+1))* integrate(function(t, x, meanlog, sdlog) # gamma(x+1)* integrand(t, x, meanlog, sdlog), lower = 0, upper = Inf, x = x, meanlog = meanlog, sdlog = sd...

Dear R users, When I use two functions, 'optim' and 'integrate', simultaneously, I always get an error like this -------------------------------------------------------------------------- numint = function(z) { dlnorm(z,mu[1],sqrt(exp(g[1]))) * dnorm((z-mu[2])/sqrt(exp(g[2])))/sqrt(exp(g[2])) } integrate(numint,lower=0,upper=Inf)$value Error in integrate(numint, lower = 0, upper = Inf) : non-finite function value ----------------------------------------------------------------------------- Dr. Ripley said fo...

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

....70, 14.90, 6.12, 17.88, 13.72) breaks<-c(0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44) hist(app_depths,breaks,freq=FALSE,main="LARV Irrigation Application Depths (in) 2004-2007",xlab="Application Depth (in)",col="grey") plot(function(app_depths) dlnorm(app_depths,meanlog=mean(app_depths),sdlog=sd(app_depths)),0,44,add=TRUE,col="black",lwd=3) Problem is, I get a plot that doesn't look right, http://n4.nabble.com/file/n998495/Image3.png If there is an obvious error as to why the pdf line doesn't fit the data, I'm unable t...