similar to: normal cdf over an interval

Hi, I am working on a package which requires separate documentation (tutorial documentation, really, with a lot of beautifully latexed equations), and does not use R-help. I am trying to make it so that my help function will automatically search R-help if the help topic isn't found in my documentation. The function looks something like this: help.pkg <- function(topic) { #

Hi, I am working on a package which requires separate documentation (tutorial documentation, really, with a lot of beautifully latexed equations), and does not use R-help. I am trying to make it so that my help function will automatically search R-help if the help topic isn't found in my documentation. The function looks something like this: help.pkg <- function(topic) { #

Hi all, I'm not sure if this is a feature or a bug (and I did read the FAQ and the posting guide, but am still not sure). Some of my students have been complaining and I thought I just might ask: Let K be a vector of length k. If one types dim(K), you get NULL rather than [1] k. Is this logical? Here's the way I explain it (and maybe someone can provide a more accurate

In the R source, nmath/pnorm.c contains the code for a rational function approximation for the normal cdf. These constants are listed: const double a[5] = { 2.2352520354606839287, 161.02823106855587881, 1067.6894854603709582, 18154.981253343561249, 0.065682337918207449113 }; The source file cites a paper by Cody (1969) and states that these

install.packages works for Linux because the directory structure is not version dependent, as it is for the Windows download. That should be fixed as soon as my sys admin consents to install R 1.9.0 so we can do the cross compile. Of course, you are right, Martin and Peter, that Zelig should be on CRAN proper. We just need to write a few .Rd files to be compliant with R check, and change our

Hi, Is there a way to make demo() print comments along with the code and output? For example, if I use something like readline("..."), demo prints both readline("...") and ...; as far as I can tell, this is also true of print() and cat(). I just want students to stop and think about every command in my demo scripts. Thanks, Olivia.

Hello all, I was wondering if there is an R function to do the following: [*] log(pnorm(x)-pnorm(y)), where x>y. I don't want all the area under the natural log of the normal pdf less than x, I only want the area between y and x. I am aware of the ability to specify log.p=TRUE, which gives me the log of the probability that X<=x. This does not help me, because the following code:

Dear R-users, I have been using the code below in order to verify how the CDF of a skew-normal distribution was calculated: library(sn) s=seq(-30,30,by=0.1) a<-matrix(nrow=length(s),ncol=5) lambda=1 for(i in 1:length(s)){ a[i,1]=pnorm(s[i],mean=0,sd=1); a[i,2]=T.Owen(s[i],lambda); a[i,3]=a[i,5]-2*a[i,6]; a[i,4]=pnorm(s[i])-2*T.Owen(s[i],lambda);

Hi, I'm working on a set of demo files for a package, and I'm having a problem because I have two demo files (one called "logit", the other called "blogit") and when I type demo(logit), it gives me demo(blogit) instead. (And calls it the the demo for logit.) I've figured out that this is because the demo() function searches through all the demo directories (in

Dear Experts, I need to calculate the cdf of the standard normal distribution, i.e. H(x) = 1/sqrt(2*pi) integral(exp(-z^2/2) dz), where z is b/w -infi to infi. I know there should be a way to do it in R, but did not know to do it. I'd appreciate any help you could offer. Charlie Liu Graduate student intern at EPA/ECO

Hi, I am trying to use boot() to refit an ordinal logit (polr in MASS) model. (A very basic bootstrap which samples from the data frame without replacement and updates the model.) I need to extract two statistics per run (the coefficients and zeta) and I tried concatenating them into a single vector after fitting, but I get the following error: Error in "[<-"(*tmp*, r, ,

Hello all, I would like to plot the emperical CDF, normal CDF and pareto CDF in the same graph and I amusing the following codes. "z" is a vector and I just need the part when z between 1.6 and 3. plot(ecdf(z), do.points=FALSE, verticals=TRUE, xlim=c(1.6,3),ylim=c(1-sum(z>1.6)/length(z), 1)) x <- seq(1.6, 3, 0.1) lines(x,pgpd(x, 1.544,0.4373,-0.2398), col="red") y

I am trying to plot two density lines on the same graph. Using the functions on the base package, I would go: plot(density(x), col = 1) lines(density(y), col = 2) And I get two distinct (one-bump) density lines. When I try to do it using lattice, I get two two-humped lines. (In other words, I think the smoothing function is taking the next set of data points and smoothing them in the same

Is anybody working on a way to standardize the creation of "newdata" objects for predict methods? When using predict, I find it difficult/tedious to create newdata data frames when there are many variables. It is necessary to set all variables at the mean/mode/median, and then for some variables of interest, one has to insert values for which predictions are desired. I was at a

Hi, I have a data set which is assumed to follow weibull distr'. How can I find of cdf for this data. For example, for normal data I used (package - lmomco) >cdfnor(15,parnor(lmom.ub(c(df$V1)))) Also, lmomco package does not have functions for finding cdf for some of the distributions like lognormal. Is there any other package, which can handle these distributions?

I am trying to make a loglikelihood function using copulas. I am trying to use mvdc to find the density function. When I run this I got the error that the pdf and cdf of my function tobit doesn't exist. Can somebody guide me where my mistake is? dtobit <- function(beta,sigma, x, y) {ifelse(y>0, dnorm(y,x%*%beta, sigma),(1-pnorm((x%*%beta)/sigma)))} ptobit <- function(beta,sigma, x,

Hi, I already found a conversation on the integration of a normal distribution and two suggested solutions (https://stat.ethz.ch/pipermail/r-help/2007-January/124008.html): 1) integrate(dnorm, 0,1, mean = 0, sd = 1.2) and 2) pnorm(1, mean = 0, sd = 1.2) - pnorm(0, mean = 0, sd = 1.2) where the pnorm-approach is supposed to be faster and with higher precision. I want to integrate a mixed

This practical problem in maximum likelihood estimation must be encountered quite a bit. What do you do when a data point has a probability that comes out in numerical evaluation to zero? In calculating the log likelihood you then have a log(0) problem. Here is a simple example (probit) which illustrates the problem: x<-c(1,2,3,4,100) ntrials<-100 yes<-round(ntrials*pnorm((x-3)/1))

Hi, Does anyone know what the distribution for the product of two correlated normal? Say I have X~N(a, \sigma1^2) and Y~N(b, \sigma2^2), and the \rou(X,Y) is not equal to 0, I want to know the pdf or cdf of XY. Thanks a lot in advance. yu [[alternative HTML version deleted]]

I believe that this may be more appropriate here in r-devel than in r-help. The normal hazard function, or reciprocal Mill's Ratio, may be obtained in R as dnorm(z)/(1 - pnorm(z)) or, better, as dnorm(z)/pnorm(-z) for small values of z. The latter formula breaks dowm numerically for me (running R 2.4.1 under Windows XP 5.1 SP 2) for values of z near 37.4 or greater. Looking at the pnorm