similar to: two densities with same stepsize

Dear R-list, I have made a simple kernel density estimation by x <- c(2,1,3,2,3,0,4,5,10,11,12,11,10) kde <- density(x,n=100) Now I would like to know the estimated probability that a new observation falls into the interval 0<x<3. How can I integrate over the corresponding interval? In several R-packages for kernel density estimation I did not found a corresponding function. I

> On 26 Aug 2017, at 16:39, niharika singhal <niharikasinghal1990 at gmail.com> wrote: > > Hi, > > Thanks for your mail, and time > > It is not working for some arguments, when mean value is like >6. > > > case > > mc0 <- c(0.08844446,0.1744455,0.1379778,0.1209769,0.1573065,0. > 1134463,0.2074027) > > rv

Hello, I was wondering if I can plot two curves I get from "density(data)" into one plot. I want to compare both. With the following commad, I just get one curve plotted: plot( density(mydata) ) Sorry for this stupid question but I could not find a solution until now... Antje

Hi there, I have a very simple question. If I execute the following code: test <- function(x) { name <- paste(x,"info_within_function") c(1,2,3,4,5) } ret <- lapply(1:10, test) , I end up with a list and each entry is just numbered [[1]], [[2]], ... [[10]] How can I force the result entries gettings names which are determined within the function. For this example,

Hi, Thanks for your mail, and time It is not working for some arguments, when mean value is like >6. case mc0 <- c(0.08844446,0.1744455,0.1379778,0.1209769,0.1573065,0. 1134463,0.2074027) rv <-UnivarMixingDistribution(Norm(486.4255, 53.24133), Norm(664.0713, 3.674773), Norm(669.0484, 4.101381),

Hello, I have two questions. I'd like to visualize data with a heatmap and I have the following testcase: x <- rnorm(256) nx <- x + abs(min(x)) nnx <- 255/max(nx) * nx x <- matrix(nnx, 16, 16) rownames(x) <-

Hi there, I have a list which consists of data frames (all data frames have the same amount and type of columns but different length). Now, I'd like to calculate for each data frame in the list the density function of the values of the fist column ($V1). This list could be an example: l <- list( data.frame(rnorm(100)), data.frame(rnorm(20)), data.frame(rnorm(200)) ) Can anybody help?

Hi there, I hope this question is not as stupid as the one before ... I tried to shorten my histogram (because the distribution is quite skewed and I simply don't want to see the long tail but still use the histogram plot). How can I do something like this? (The example does not work but I don't know why...) data <- rnorm(100) # as example, of course this is not skewed... h <-

Hi, I like to generate two normal densities such that the overlapping area between them is 0.35. Is there any code/package available in R to do that?? Regards, Lavan -- View this message in context: http://www.nabble.com/densities-with-overlapping-area-of-0.35-tp19384741p19384741.html Sent from the R help mailing list archive at Nabble.com.

Hi, I'm using the densCols function for a scatterplot and cannot figure out 1) how to extract the computed densities, and 2) how to create a legend based that represents the upper and lower ranges of the densities. For example: movers.den <- densCols(move$x, move$y) table(movers.den) #08306B #083775 #083B7C #083D7E #3989C1 #3F8FC4 28 22 101 25

Hello, I have another, more statistic related question. I have several different measurements as an output of an image analysis process (e.g. size, intensity, ... of objects of interests). Now, I would like to quantify the difference for each parameter measured in a special condition comparing to the measurement of a reference condition. If I plot the density distribution (simply calling

Hello, I am trying to calculate and plot mean and confidence intervall for a set of data. This is the code that I am currently using: means <- sapply(data, mean, na.rm=TRUE) n <- sapply(data,length) stdev <- sqrt(sapply(data, var, na.rm=TRUE)) ciw <- qt(0.98, n) * stdev / sqrt(n) par(mgp=c(2,0.6,0), las=2, fin=c(7,3), mai=c(1,0.5,0.2,0.2), cex=0.8) plotCI(x=means, uiw=ciw,

Hi all. I would like to highlight overlapping regions of two densities and I could not find a way to do it. Here is the sample code: myd <- c(2,4,5, 4,3,2,2,3,3,3,2,3,3,4,2,4,3,3,3,2,2.5, 2, 3,3, 2.3, 3, 3, 2, 3) myd1 <- myd-2 plot(range(density(myd)$x, density(myd1)$x), range(density(myd)$y, density(myd1)$y), type = "n") lines(density(myd), col=1, lwd=4)

Hi group, I am currently plotting two densities using the following code: x1 <- c(1,2,1,3,5,6,6,7,7,8) x2 <- c(1,2,1,3,5,6,5,7) plot(density(x1, na.rm = TRUE)) polygon(density(x2, na.rm = TRUE), border="blue") However, I would like to avoid bordering the second density as it adds a nasty bottom line which I would like to avoid. I would also rather have a dashed or dotted line

Hi, I am doing a project on the simulation of glucose metabolism based on a pharmacokinetic modeling in which we have 4 differential equations. I did this in R by using the odesolve package. It works very well, but I have two questions: Here is the odemodel function _________________________________________________ Ogtt.Odemodel <- function(t, y, p) { absx <- c(-60, -45, -30,

Merry Xmas to all, I am writing a function and curiously this runs sometimes on one data set and fails on another and i cannot figure out why. Any help much appreciated. If i run the code below with data <- iris[ ,1:4] The code runs fine, but if i run on a large dataset i get the following error (showing data structures as matrix is large) > str(cluster.data) num [1:9985, 1:811] 0 0 0 0

Greetings: I have multiple hexbin plots with varying greatest densities. Right now, the data on each plot varies from 1-256 levels of density. The problem with that is that in Plot A the data are more scattered across a 2 x 2 grid whereas in Plot B the data are more concentrated in fewer cells. Hence, Plot A has a few very dense grid cells and plot B has nothing as dense as plot A. (In Plot A

Hi there, I am working on a project which needs the value of the interaction area under two distributions( eatimated by kernel density estimators). For example: x<-rnorm(100,0,1) y<-rnorm(100,0.2,1) density(x) # This produces the summary of dependent variable and independent variable. How can I get the individual values of variables and reform a curve to calculate the area under

Hello, I used the following codes to generate bivariate normal dependence structure with unit Frechet margins. Sigma <- matrix(c(1,.5*sqrt(1),.5*sqrt(1),1),2,2) # generate y <- mvrnorm(Nsam, c(0,0), Sigma) # random v <- cbind(pnorm(y[,1],mean = 0, sd = 1), pnorm(y[,2],mean = 0, sd = 1)) z <- cbind(-1/log(v[,1]),-1/log(v[,2])) z1 <- z[,1] z2 <- z[,2] And to

Dear R users, I would like to plot the bivariate density surface with its marginal densities on the sides of the 3D box, just like in the picture I attach. I tried to found information about how to do it but did not find anything. Does anyone know how to do it? Thanks in advance, Eduardo.