similar to: Heat Maps

Dear List, When I plot a histogram with 'freq=FALSE' and overlay the histogram with a normal pdf curve, everything looks as expected, as follows: x <- rnorm(1000) hist(x, freq=FALSE) curve(dnorm(x), add=TRUE, col="blue") What do I need to do if I want to show the frequencies (freq=TRUE) with the same normal pdf overlay, so that the plot would still look the same? Regards,

Dear Lest, I have a two-variable data frame as follows (the time peirod of the actual data set is 10 years): Date Amount 1 6/1/2007 1 2 6/1/2007 1 3 6/4/2007 2 4 6/5/2007 2 5 6/11/2007 3 6 6/12/2007 3 7 6/12/2007 3 8 6/13/2007 3 9 6/13/2007 3 10 6/18/2007 4 11 6/18/2007 4 12 6/25/2007 5 13 6/28/2007 5

Dear List: Below is how I specify an axis: axis(2, at=c(0.00005, 0.0005)) R displays the numbers in scientific notation. What argument/parameter should I use to tell R to display the numbers as specified rather than in scientific notation? > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major

I have a model as follows: x <- replicate(100, sum(rlnorm(rpois(1,5), 0,1))) y <- quantile(x, 0.99)) How would one go about estimating the boundaries of a 95% confidence interval for y? Any pointers would be greatly appreciated. > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 2 minor 5.1 year 2007 month 06 day 27 svn rev 42083 language R

Given the following, one of the things I am trying to see is what % of draws are below a certain number: lambda <- 3 rate <- 5 n <- 5 set.seed(123) v <- replicate(n, rexp(rpois(1,lambda), rate)) vv <- unlist(v) cat("% of draws below 0.1:", round(length(subset(vv, vv < 0.1))/length(vv)*100,0), "%\n") In actuality, my lambda, rate, and n are 26, 10, 1000000,

Dear List, I have read that a lognormal mixture model having a pdf of the form f(x)=w1*f1(x)+(1-w1)*f2(x) fits most data sets quite well, where f1 and f2 are lognormal distributions. Any pointers on how to create a function that would produce the 5 parameters of f(x) would be greatly appreciated. > version _ platform i386-pc-mingw32 arch i386 os

Dear List, I have a simple model as follows: x <- rnorm(500) library(logspline) fit <- logspline(x) n <- 1000000 y <- replicate(n, sum(rlogspline(rpois(1,10), fit))) # last line The problem I keep getting is R crashes when doing the last line. It seems to be fine if n is small, but not if n is 1000000. The message I keep getting is: "R for Windows GUI front-end has

Dear List, Is there any way to password-protect script files (either within R or otherwise)? platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 2 minor 5.1 year 2007 month 06 day 27 svn rev 42083 language R version.string R version 2.5.1 (2007-06-27)

CRAN (and crantastic) updates this week New packages ------------ * disclapmix (0.1) Maintainer: Mikkel Meyer Andersen Author(s): Mikkel Meyer Andersen and Poul Svante Eriksen License: GPL-2 http://crantastic.org/packages/disclapmix disclapmix makes inference in a mixture of Discrete Laplace distributions using the EM algorithm. * EstSimPDMP (1.1) Maintainer: Unknown Author(s):

Dear List, This inquiry probably does not directly pertain to R. I am using library(evir) to learn EVT. Based on my reading of things, it is my understanding that if one wants to calculate quantiles of GPD, one could use either riskmeasures() or qgpd(). However, using data(danish) as an example, the quantile estimates produced by riskmeasures() are considerably different from those produced by

Dear List: I have a simple two-column data set in .csv format, with the first column being the date and second column being some value. I use read.csv() to import the data as follows: x <- read.csv("myfile.csv",header=T, dec=".", colClasses=c(Date="POSIXct")) The structure of x is: > str(x) `data.frame': 2959 obs. of 2 variables: $