similar to: Help w/ density() usage

AF_E PS_E 90.838 2.206 83.139 1.751 134.272 3.710 84.043 2.076 105.184 2.788 157.249 3.783 50.280 1.027 96.973 2.355 123.582 3.398 60.417 1.236 123.501 3.315 90.128 1.566 193.783 5.167 116.036 2.994 100.289 2.216 56.943 1.106 102.272 2.692 145.579 3.810 53.105 1.202 127.212 3.061 102.838 2.383 126.352 2.723 13.661 0.190 164.352 4.870 159.945 4.160 54.382 0.884 128.253 3.598 181.208 4.767 145.118

I am using the following commands for plotting kernel density for three kinds of crops density(s22$Net_income_Total.1, bw="nrd0",adjust=1, kernel=c("gaussian"))->t plot(t, xlim=c(-30000,40000), main="Net Income Distribution", axes=F, ylim=c(0,0.00035). xlab="Value in Rupees") par(new=T) density(s33$Net_income_Total.1, bw="nrd0",adjust=1,

Dear all, Sorry if I am posting to the wrong place, but I could not find the link for registration on the bug tracker, that?s why I am writing here. I think there is inconsistency between two R functions from the stats package, bw.nrd0 and bw.nrd. Consider the following vector: D <- c(0, 1, 1, 1, 1) bw.nrd(D) returns zero bandwidth for this object even without a warning. Considering the

Hi, I split a data set into two partitions (80 and 42), use the first as the training set in glm and the second as testing set in glm predict. But when I call glm.predict, I get the warning message:  Warning message: 'newdata' had 42 rows but variable(s) found have 80 rows  ---------------------  s = sample(1:122)

Dear R helpers I am having recovery rates as given below and I am trying to estimate the Loss Given Default (LGD) and for this I am using Kernel Density estimation method. recovery_rates = c(0.61,0.12,0.10,0.68,0.87,0.19,0.84,0.81,0.87,0.54,0.08,0.65,0.91, 0.56,0.52,0.30,0.41,0.24,0.66,0.35,0.36,0.64,0.55,0.43,0.36,0.28,0.89,0.11,0.23,0.07,

Dear All, I have a variable q which is a vector of 1000 simulated positive values; that is I generated 1000 samples from the pareto distribution, from each sample I calculated the value of q ( a certain fn in the sample observations), and thus I was left with 1000 values of q and I don't know the distribution of q. Hence, I used the given code for kernel density estimation to estimate the

For the hist estimate >par(mex=1.3) >dens<-density(q) >options(scipen=4) > ylim<-range(dens$y) > h<-hist(q,breaks="scott",freq=FALSE,probability=TRUE, +? right=FALSE,xlim=c(9000,16000),ylim=ylim,main="Histogram of q(scott)") > lines(dens) >box() ? For the kernel estimate>options(scipen=4) > d <- density(q, bw =

Hi all, I have a question regarding the density function which gives the kernel density estimator. I want to decide the bandwidth when using gaussian kernel, given a set of observations. I am not familiar with different methods for bandwidth determination. Below are the different ways in R on deciding the bandwidth. Can anyone give an idea on which ones are preferred. Also, how can I take

Hello, JIT compiler interferes with internal R completions: compiler::enableJIT(2) utils:::functionArgs("density", '') gives: utils:::functionArgs("density", '') Note: no visible global function definition for 'bw.nrd0' Note: no visible global function definition for 'bw.nrd' Note: no visible global function definition for 'bw.ucv'