search for: qpoi

Full_Name: Manikandan Narayanan Version: 2.8.1 OS: Linux Submission from: (NULL) (155.91.45.231) Here is an excerpt from qpois help page (?qpois): The quantile is left continuous: 'qgeom(q, prob)' is the largest integer x such that P(X <= x) < q. I think the "qgeom" here should be "qpois" instead. Please correct this typo in ?qpois, since it's misleading in its current...

Full_Name: Mikael Weigelt Version: 2.0 OS: windows Submission from: (NULL) (207.171.180.101) The calculation of the qpois attempts to use the Cornish-Fisher expansion as a starting approximation. The definition of the expansion is incorrect. However, since this approximation just gives an initial solution, the end result of the function is still correct. To fix the approximation, in the snippet below the line...

version .62: --------------------------------------------- > ?qpois The Poisson Distribution dpois(x, lambda) ppois(q, lambda) qpois(p, lambda) rpois(n, lambda) Arguments: x: vector of (positive) quantiles. p: vector of probabilities. n: number of random values to return. lambda: vecto...

Full_Name: Jerry W. Lewis Version: 2.10.1 OS: Windows XP Professional Submission from: (NULL) (198.180.131.21) In the line "The quantile is right continuous: qpois(q, lambda) is the smallest integer x such that P(X <= x) >= q." "q" is used as a probability when the Arguments section defines it to be a quantile. Also there are some representation problems where the escape character is printed instead of interpreted, such as "\ldots...

...th(x))) UpperCI<-mean(x)+(qnorm(1-alpha[n]/2, mean = 0, sd = 1)*sqrt(var(x)/length(x))) cat("For Alpha=",alpha[n],"LowerCI=",LowerCI,"<","Lambda=",mean(x),"<","UpperCI=",UpperCI,"\n") } When I do something like: qpois(.975, 12.73, lower.tail = TRUE, log.p = FALSE) [1] 20 > qpois(.025, 12.73, lower.tail = TRUE, log.p = FALSE) [1] 6 I get quite a different result. Is this the difference between the normal approx and an (almost) exact Poisson CI? Thx! [[alternative HTML version deleted]]

..., 3.0, 1, 0) << std::endl; Outputs 0.0282703, expect 5.03504 I should stress that within the R environment, I *do* get the values I expect: though sometimes the arguments need adjusting as the C functions have slightly different argument lists from the R versions. Also: std::cout << qpois(0.5, 5.0, 1, 0) << std::endl; Seems to go into an infinite loop inside qpois, while: qbeta(0.1, 1e-5, 1e-5, 1, 0); Raises an access violation inside R: although I should stress that all other beta quantiles I've tested come out OK. Anyway, hopefully you can just tell me what an idiot...

...s the observed distribution? One simple way is to use the uniform random output of randomLHS as input to the quantile function for your desired distribution(s). For example: q <- randomLHS(1000, 3) colnames(q) <- c("A", "B", "mort") q[, "mort"] <- qpois(q[,"mort"], 1.5) S Ellison ******************************************************************* This email and any attachments are confidential. Any use, copying or disclosure other than by the intended recipient is unauthorised. If you have received this message in error, please...

Hello, I am performing a sensitivity analysis using a Latin Hypercube sampling. However, I have difficulty to draw a Hypercube sample for one variable. I?ve generated this variable from a Poisson distribution as follows: set.seed(5) mortality_probability <- round(ppois(seq(0, 7, by = 1), lambda = 0.9), 2) barplot(mortality_probability, names.arg = seq(0, 7, by = 1), xlab = "Age

Thanks for your answer. However, my variable is simulated from the cumulative distribution function of the Poisson distribution. So, the pattern obtained from the function "qpois" is not the same as the observed pattern (i.e., obtained from the function "ppois") set.seed(5) mortality_probability <- round(ppois(seq(0, 7, by = 1), lambda = 0.9), 2) barplot(mortality_probability, names.arg = seq(0, 7, by = 1), xlab = "Age class", ylab = "Prob...

....o pgeom.o phyper.o plnorm.o plogis.o pnbeta.o pnbinom.o pnchisq.o pnf.o pnmath.o pnorm.o pnt.o polygamma.o ppois.o pt.o ptukey.o punif.o pweibull.o qbeta.o qbinom.o qcauchy.o qchisq.o qexp.o qf.o qgamma.o qgeom.o qhyper.o qlnorm.o qlogis.o qnbeta.o qnbinom.o qnchisq.o qnf.o qnorm.o qnt.o qpois.o qt.o qtukey.o qunif.o qweibull.o sign.o stirlerr.o toms708.o -lgomp -F/Library/Frameworks/R.framework/.. - framework R -Wl,-framework -Wl,CoreFoundation ld: warning, duplicate dylib /Developer/SDKs/MacOSX10.4u.sdk/usr/local/ lib/libgcc_s.1.dylib ** arch - ppc ... gcc -arch ppc -isysroot /De...

Hi all, why doesn't something like this does not work? speedy <- (sdata$VaR < sdata$DdtdAbs) && sdata$DdtdDuration >= qpois(pct,lambda) && sdata$Ddtd > MinDD or sdata$Ddtd[sdata$Ddtd > 0 && sdata$VaR < sdata$DdtdAbs] sdata looks like this: dataId date value Ddtd VaR DdtdAbs DdtdDuration 18948 79637 2004-07-27 10085.10 NA NA 0.00 0 18949 79...

Full_Name: Wolfgang Resch Version: R 2.8.1 GUI 1.27 OS: OS X 10.4.11 Submission from: (NULL) (137.187.89.14) Strange behavior of qbinom: > qbinom(0.01, 5016279, 1e-07) [1] 0 > qbinom(0.01, 5016279, 2e-07) [1] 16 > qbinom(0.01, 5016279, 3e-07) [1] 16 > qbinom(0.01, 5016279, 4e-07) [1] 16 > qbinom(0.01, 5016279, 5e-07) [1] 0

There is a problem with `rpois'. It does seem to take care about the order of the arguments. This is an example: > rpois(n=1,lambda=2) [1] 3 > rpois(lambda=2,n=1) [1] 2 0 It obviously uses the first argument as the number of samples to be drawn, which is wrong. I used Version 0.49 Beta (April 23, 1997). Fredrik

There is a problem with `rpois'. It does seem to take care about the order of the arguments. This is an example: > rpois(n=1,lambda=2) [1] 3 > rpois(lambda=2,n=1) [1] 2 0 It obviously uses the first argument as the number of samples to be drawn, which is wrong. I used Version 0.49 Beta (April 23, 1997). Fredrik

Could somebody help me to understand the syntax of R's ppois function? I'm looking to calculate the cumulative probability density of an observed value (y) given the expected mean (mu) and the level of significance (alpha). I'm coming from using SAS to do this and don't recognize the descriptions of the arguments for ppois. The definitions of lambda and p as stated in the R manuals

...onvolution algorithm, we found the following "inexactness": We work with R Version 1.8.1 (2003-11-21) on Windows systems (NT, 2000, XP). Try the code: ## Kolmogorov distance between two methods to ## determine P(Poisson(lambda)<=x) Kolm.dist <- function(lam, eps){ x <- seq(0,qpois(1-eps, lambda=lam), by=1) max(abs(ppois(x, lambda=lam)-cumsum(dpois(x, lambda=lam)))) } erg<-optimize(Kolm.dist, lower=900, upper=1000, maximum=TRUE, eps=1e-15) erg Kolm1.dist <- function(lam, eps){ x <- seq(0,qpois(1-eps, lambda=lam), by=1) which.max(abs(ppois(x, lambda=lam)-cumsu...

...o rt.o dnt.o dchisq.o pchisq.o qchisq.o rchisq.o rnchisq.o dbinom.o pbinom.o qbinom.o rbinom.o rmultinom.o dcauchy.o pcauchy.o qcauchy.o rcauchy.o dexp.o pexp.o qexp.o rexp.o dgeom.o pgeom.o qgeom.o rgeom.o dhyper.o phyper.o qhyper.o rhyper.o dnbinom.o pnbinom.o qnbinom.o rnbinom.o dpois.o ppois.o qpois.o rpois.o dweibull.o pweibull.o qweibull.o rweibull.o dlogis.o plogis.o qlogis.o rlogis.o dnchisq.o pnchisq.o qnchisq.o dnbeta.o pnbeta.o qnbeta.o pnf.o pnt.o qnf.o qnt.o ptukey.o qtukey.o toms708.o wilcox.o signrank.o ranlib libnmath.a config.status: creating src/unix/Makefile make: /usr/local...

Full_Name: Murray H Smith Version: 0.63.3 OS: Windows NT Submission from: (NULL) (130.216.5.57) The ppois function is displaced by -0.5. Try: > ppois(-0.5,1) [1] 0.3678794 > ppois(-0.51,1) [1] 0 > ppois(0,1) [1] 0.3678794 and > par(mfrow=c(2,1)) > x<-seq(-1,5,0.01) > plot(x,ppois(x,1),type="s",ylab="F(x)",main="Poisson CDF?") >

Hi I want to write a C function for the R Code below and call it with .Call: SimPoisson <- function(lambda,tgrid,T2M) #Simulation eines Poissonprozesses { NT <- 0 Ni <- rep(0,length(tgrid)) tau <- 0 sign <- 0 if(lambda != 0) { i=1 j=1 while (1) { EVar <- rexp(1,lambda) sign <- sign + EVar if (sign > T2M) { break } tau[i] <- sign

Dear All I would like to know whether it is possible with R to define a discrete random variable different from the ones already defined inside R and generate random numbers from that user-defined distribution. Thanks in advance, Paul