similar to: Overlapping areas under normal distributions

Dear R helpers, I have a graph as following; I would like to highlight the overlapping area between the two curves. Do you know how to do this? Thank you in advance for your help. Nguyen ###START x1 <- rnorm(10000, 0.70,0.12) x2 <- rnorm(10000, 0.90,0.12) d1 <- density(x1) d2 <- density(x2) plot(range(d1$x,d2$x), range(d1$y, d2$y), type = "n", xlab = "X

Fixes CID 1227580 --- silk/process_NLSFs.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/silk/process_NLSFs.c b/silk/process_NLSFs.c index c27cf03..d9123ef 100644 --- a/silk/process_NLSFs.c +++ b/silk/process_NLSFs.c @@ -100,6 +100,6 @@ void silk_process_NLSFs( } else { /* Copy LPC coefficients for first half from second half */ - silk_memcpy(

I try to use ColorRamp as ColorRampPalette (i.e. with the same gradient), but it seems there is a nuance that I've missed. pal.crp<-colorRampPalette( c("blue", "white", "red"), space = "rgb") plot(rep(0,40),pch=16,col=pal.crp(40)) # is great But, using the same gradient with colorRamp is giving erratic colors. pal.cr<-colorRamp(

Dear UseRs, I'm making box plots from a data set that looks like this: Chr Start End GeneDensity ReadCount_Explant ReadCount_Callus ReadCount_Regen 1 1 1 10000 107.82 1.243 1.047 1.496 2 1 10001 20000 202.50 0.835 0.869 0.456 3 1 20001 30000 158.80 1.813 1.529 1.131

Dear R community, I am having trouble changing the tick marks on y-axis to every 5 units? I have the following: x <- c(12, 16, 6, 23, 27, 8, 5, 19, 23, 13, 16, 8) y <- c(29, 29, 23, 34, 38, 24, 22, 34, 36, 27, 33, 27) plot(x, y, pch=19) Should I change ylim=c(0,40), and then use axis()? I kept on thinking I can do everything inside plot(), but there is actually axis(), par(), ..., and

Hello, I asked a similar question before but in an existing thread. I am not sure if it is proper etiquette to repost a similar question as a new tread, I think in this case, it might be because this way more people can see it and perhaps learn from it. (Also, part of the existing thread became private) I want to know how to plot multiple ggplot area plots on top of one another so that the same

All I want to overlay two variables on the same plot following their appropriate grouping. I have attempted to use subscripting in panel with panel.xyplot, but I can't get the grouping to follow into the panel...here is an example... dat<-data.frame( y= log(1:10), y2=10:19, x=1:10, grp = as.factor(1) ) dat2<-data.frame( y= log(10:19), y2= 20:29, x=1:10, grp = as.factor(c(2)) )

Using R-2.12.1 and latticeExtra-0.6-14, I would like to understand why a rootogram displaying samples from the Poisson distribution looks like I expected it, whereas a rootogram using the normal distribution does not: library(latticeExtra) rootogram(~rpois(1000, lambda = 50), dfun = function(x) dpois(x, lambda = 50)) rootogram(~rnorm(1000), dfun = function(x) dnorm(x,mean(x),sd(x))) I

Dear All I am totally new to R and I would like to know whether R is able and appropriate to illustrate to my students the Central Limit Theorem, using for instance 100 independent variables with uniform distribution and showing that their sum is a variable with an approximated normal distribution. Thanks in advance, Paul

I define priors in jags within r using a gamma distribution. I would like to control the shape but I have problems. Any help will be usefull. From help of dgamma ___________________ The Gamma distribution with parameters shape = a and scale = s has density f(x)= 1/(s^a Gamma(a)) x^(a-1) e^-(x/s) and rate=1/scale From jags user manual ____________________ dgamma(r, mu) has a density of

Hi all, I found this useful package for generalized beta, yet the function that calculates its density leaves me puzzled, especially when I plotted it: plot(function(y) dbetagen(y,4,1.2,min=0,max=40),xlim=c(0,40)) The area between the density curve and the x axis does not seem to measure 1!!! -- View this message in context:

Hi Im working on my master thesis, I need to get a plot currents distributed over a surface. I have the data listed in a dataframe with the x coordinates, y coordinates and the value. Im using the persp function but I’ve not found out how to put the data into the z1 variable es <-read.table("RDatalog_pass_dev_1_wafers_1_8V.txt",header=T,sep="\t") x1 <-

http://en.wikipedia.org/wiki/Binomial_distribution Bino Hi there, How can I get the legend in the probability density plot in that http://en.wikipedia.org/wiki/File:Binomial_distribution_pmf.svg wiki page ? and the cumalative plot as well? ==================================== # Binomial Distribution x=0:40

Dear R users, The parameters xaxs and yaxs (function par, package graphics) seem not to work with the function hist (package graphics), even when the parameters xlim and ylim are defined. Is there any way to make yaxs="i" and xaxs="i" work properly with the function hist, mainly to produce histograms that "touch" the horizontal axis? The R documentation and the R

Hi All, I'm trying to do a Scatterplot (package: car), and add a line (just for reference). There is my code: #------------------------------------Code--------------------------------------------------- library("car") library("calibrate") G_T<-c("car","bike","boat") ave<-c(80,10,45) perf<-c(100,80,75) df2<-data.frame(G_T,ave,perf)

I have wrote this: EventDate.find(:all, :include => [:container,{:event => [:photo,:event_type_names]},{:place=>:location}], :joins =>" RIGHT JOIN (SELECT event_dates.id,date_add(event_dates.date, INTERVAL i DAY ) AS date FROM (select 0 as i union all select 0 union all select 1 union all select 2) as integers CROSS JOIN event_dates

I am attempting to plot frequency of occurrence against (calendar) time. As the output is to be produced as a Powerpoint picture on a dark background I wish to make the plot output yellow > par(col="yellow", col.axis="yellow", col.lab="yellow", col.main="yellow") > plot(t2, t1, ylim=c(0,40), main="Episynlestes run 2",

Dear friends, Please find below the code that I have employed for a rejection sampler to draw from asymmetric laplace distributions. I was wondering if this code can be written more efficiently? Are there more efficient ways of drawing random numbers from asymmetric laplace distributions?? Thanks in advance for your help and have a great weekend. Regards Anup

hi Suppose I have a mixture of 2 distributions generated by rtwonormals <- function(npnt,m1,s1,m2,s2,p2){ rv<-vector(npnt,mode="numeric") for( i in seq(1:npnt)){ if(runif(1,0,1)<=p2){ rv[i]<-rnorm(1,m2,s2) } else{ rv[i]<-rnorm(1,m1,s1) } } return(rv) } x <- rtwonormals(50000,0,100,500,500,0.05) #and I try to fit these with (based on thread: [R]

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