search for: rchisq

Hi all, I'm trying to superimpose a rchisq density line over a histogram with something like: hist(alnlength) lines(density(rchisq(length(alnlength), 4)),col="red") But the rchisq line won't appear anywhere, Anyone knows what I am missing here? Thanks in advance, Albert.

...5) The QQ-plot of two versions of simulating noncentral F-distributed random numbers has quite different scales: > qqplot(rf(1000,2,15,3),qf(runif(1000),2,15,3)) The rf() function reads: > rf function (n, df1, df2, ncp = 0) { if (ncp == 0) .Internal(rf(n, df1, df2)) else rchisq(n, df1, ncp = ncp)/rchisq(n, df2) } <environment: namespace:stats> where I believe both the numerator and the denominator should be divided by their corresponding degrees of freedom. My suggested (slighly augmented) version is: > rf= function (n, df1, df2, ncp1 = 0, ncp2=0) { if (...

Hi! This is going to be a real newbie question, but I can't figure it out. I'm trying to plot densities of various functions of chi-square. A simple chi-square plot I can do with dchisq(). But e.g. chi.sq/degrees of freedom I only know how to do using density(rchisq()/df). For example: plot(1, type="n", xlab="", ylab="", xlim=c(0,2), ylim=c(0,7)) for (i in c(10,50,100,200,500)){ lines(density(rchisq(100000,i)/i)) } But even with 100,000 samples the curves still aren't smooth. Surely there must be a more elegant way to do t...

...for (cm in 1:length(changeMagnitudes)) { for (cl in 1:length(changeLocations)) { print(sprintf("cpm:%s magnitude::%s location:%s", cpmType, changeMagnitudes[cm], changeLocations[cl])) temp <- numeric(sims) for (s in 1:sims) { x <-c(rchisq(changeLocations[cl], df=3), rchisq(2000, df=changeMagnitudes[cm])) temp[s] <-detectChangePoint(x, cpmType, ARL0=ARL0, startup=startup)$detectionTime } results[[cpmType]][cm,cl] <- mean(temp[temp > changeLocations[cl]]) - chan...

...s matter. The code is below. The function needed to be integrated (undint with respect to u from 0 to Inf). Sincerely, V. Code rm=list(ls()) #index matrix t1<-c(1:15) t2<-c(1:5)%x%matrix(1,3,1) t3<-matrix(1,3,1)%x%c(1:5) t4<-c(1:3)%x%matrix(1,5,1) comb<-cbind(t1,t2,t3,t4) nu<-rchisq(15,4) gam<-matrix(rchisq(75,df=5),15,5) undint<-function(u){ prob<-function(i) { i1<-comb[i,1] i2<-comb[i,2] i3<-comb[i,3] i4<-comb[i,4] val1<-gam[((i3-1)*2+1):(i3*2),i4] vc1<-matrix(12,length(val1),1)-matrix(u,length(val1),1)-val1 distr1<-prod(pchisq(vc1,df=2)) p1...

...t(n = 1000, df = 20) set.seed(12345) t.2 <- rt(n = 1000, df = 20, ncp = 0) all.equal(t.1, t.2) ## Not close to true This appears to be due to the fact that in 2.4.x rt is now rt function (n, df, ncp = 0) { if (missing(ncp)) .Internal(rt(n, df)) else rnorm(n, ncp)/sqrt(rchisq(n, df)/df) } <environment: namespace:stats> Whereas in 2.3.1 rt() is verified to work as expected when someone (redundantly) types ncp = 0 rt function (n, df, ncp = 0) { if (ncp == 0) .Internal(rt(n, df)) else rnorm(n, ncp)/(rchisq(n, df)/sqrt(df)) } <environment: namespace:st...

...<- function(n,mu=0,sigma=1){ mu + sigma*cos(2*pi*runif(n))*sqrt(-2*log(runif(n))) } which uses the ``(r,theta)'' method of generating random normals. When I did so, the resulting values were indeed all ``close to'' 0.05, as they should be. I also tried the experiment using rchisq(n,1) instead of rnorm(n) (and then of course taking m = max of x --- rather than max of x^2). Again all the resulting values were close to 0.05 as ought to be the case. (So rchisq() appears to be OK in this regard.) Enclosed below is a script to demonstrate the bug. cheers, Rolf Turn...

Dear R useRs, i wanted to ask if the folded normal destribution (Y = abs(X) with X normal distributed) with density and random number generator is implemented in R or in any R-related package so far? Maybe i can use the non-central chi-square distribution and rchisq(n, df=1, ncp>0) here? Thanks and best regards Andreas

Dear all, I have an error in the simple prediction function for lm(). Maybe someone experienced the same? xma <- matrix(data = 0, nrow = 100, ncol = 2) xma[, 1] <- rnorm(100) xma[, 2] <- rchisq(100, df = 3) m1 <- lm(xma[, 1] ~ xma[, 2]) predict(m1, as.data.frame(seq(-13, 13, 0.5))) Thanks a lot, Trafim [[alternative HTML version deleted]]

...#####case 4######### *> b<-1-0.8 > rnorm(20*b) [1] 0.9101742 0.3841854 1.6821761* I was expecting the 4 cases should do the same job--generate 4 random numbers. But in case 2 and 4 I only get 3. Has anyone else seen this problem before? Thanks. (I have tried with other functions i.e "rchisq","rexp" ...) ####################################################################### One of my colleague also have a problem that we think it might be related with the problem I addressed above: > test1 <- runif(10,0,1) > test1 [1] 0.3868379 0.1587814 0.8140483 0.7796691 0...

Given a data frame with a continuous variable and a factor. I would like to generate a histogram of the continuous variable, where each bar is filled with different colors according to the percentage of factor values falling into this region of the continuous variable. I looked into packages like 'lattice' and 'ggplot2'. Searching R-help revealed that 'histogram' is

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

...+00 4.00000e+00 4.00000e+00 4.00000e+00 4.00000e+00 4.00000e+00 4.00000e+00 #[21] 4.00000e+00 4.00000e+00 4.00000e+00 4.00000e+00 4.00000e+00 4.00000e+00 5.77316e-15 # ! Notice the last (27th) value very close to 0 nsim<-10 set.seed(10) #nsim x K array of ChiSq(1)-variates w.k.sq.mat<-matrix(rchisq(nsim*K,1),nrow=nsim) #nsim x 1 array of ChiSq(n-p-K)-variates w.sum2<-rchisq(nsim,n-p-K) ### vectorized computation of nsim=10 realizations ### of a part of the RLR-statistic under the Null: w.k.sq<- cbind(w.k.sq.mat,w.sum2) #nsim x (K+1) #vector-based results: nu...

Hi there: I'd thought these two versions of noncentral t-distribution are essentially the same: > qqplot(rt(1000,df=20,ncp=3),qt(runif(1000),df=20,ncp=3)) But, the scales of the x-axis and the y-axis are quite different according to the QQ-plot. Did I make any mistakes somewhere? Thanks, Long ---------------------------------

Hi there, I need some helpo with one simulation. I have a matrix of 4x21 I want ro generate 100 random Chi2 distribution but inly from the first row of my matrix. This is what I try: Option (a): >lambda=1:100 >f=function(x)chisq(4,0,x) >lapply(lambda,f) It doesn't work! option(b): >m=mean(matrix[1,]) >x<-rpois(100,lambda=m) Works, but only generates a row of 25 values.

Hello, I'm taking samples from certain distributions and drawing a density distribution over the histogram of the samples It works fine for the chi-square and for the normal, but not for the cauchy. Any idea what I'm doing wrong? Thanks x <- rchisq(10000, df = 4) hist(x, freq = FALSE, breaks=100) curve(dchisq(x, df = 4), col = 2, add = TRUE) x <- rnorm(10000) hist(x, freq = FALSE, breaks=100) curve(dnorm(x), col = 2, add = TRUE) x <- rcauchy(10000) hist(x, freq = FALSE, breaks=100) curve(dcauchy(x), col = 2, add = TRUE) [[a...

Hello, How can I make a monte carlo simulation on R? Regards Adel -- PhD candidate in Computer Science Address 3 avenue lamine, cité ezzahra, Sousse 4000 Tunisia tel: +216 97 246 706 (+33640302046 jusqu'au 15/6) fax: +216 71 391 166 [[alternative HTML version deleted]]

Dear list I am using the qq.plot command to create quantile-quantile plots. The plot should display a 45 degree reference line upon which the points of the graph should fall if the two distributions being examined are roughly equal. If I try: x<-rchisq(100, df=6) qq.plot(x, dist="chisq", df=6) Then I get a quantile plot which has an intercept of roughly 1 when the line should be going through (0,0)? Could someone explain why this is happening on this plot and how I can get what I need? Many thanks Laura -.-.-.-.-.-.-.-.-.-.-.-.-.-....

...e are too many hits for "Random" and they dive too deeply. My answer to this FAQ would be: In order to generate random numbers, there are "r" methods for every distribution. Distributions include Uniform (runif), Binomial (rbinom), Normal (rnorm), Cauchy (rcauchy), Chi Square (rchisq), Exponential (rexp), Gamma (rgamma), and many more. By the way, the R program itself is really badly in need of an "appropos()" to go alongside "help()". Perhaps "help()" can be renamed to "hunt()" or "huntAndPeck()".