search for: nrd0

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, kernel=c("gaussian"))->u p...

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 fact that in most cases, one divides something by the bandwidth, it is highly undesirable that these function return zero bandwidth without...

...to Bhattacharya, 1967): analysis of length-frequency distribution of fish larvae to id cohorts and later estimate growh rates! I've trouble with the 1st step: using kernel density estimation (KDE) function to examine size-freq distributions (variable sl): > density(sl[dofyr==139],bw="nrd0") (dofyr means day of year and refers to a particular sampling date) > kd139 Call: density.default(x = sl[dofyr == 139], bw = "nrd0") Data: sl[dofyr == 139] (55 obs.); Bandwidth 'bw' = 0.4234 x y Min. : 4.870 Min. :...

...ulated 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 density of q  >options(scipen=4) > d <- density(q, bw = "nrd0",kernel="gaussian") > d > plot(d)   But what I'm really intersed in is to estimate the probability that q is greater than a certain value , for ex.,P(q>11000), using the kernel density estimate I obtained.  Could u help me with a fn or some document to do this? Thank u...

...pen=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 = "nrd0",kernel="gaussian") > d > plot(d) ? In fact the variable q is a vector of 1000 simulated 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...

...mination. 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 a look at the source code for the density function? Thank you very much. Hannah x <- rnorm(1000) > bw.nrd(x) [1] 0.2688588 > bw.nrd0(x) [1] 0.2282763 > bw.ucv(x) [1] 0.2112366 > bw.bcv(x) [1] 0.2890085 Warning message: In bw.bcv(x) : minimum occurred at one end of the range > bw.SJ(x) [1] 0.2716242 > density(x, give.Rkern=T, kernel="gaussian") [1] 0.2820948 > density(x, kernel="gaussian&...

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' Note: no visible global function definition for 'bw.bcv' Note: no visible global function definition for 'bw.SJ' Note: no visible global functi...

The following is from version 1.6.2 of R under Windows, or 1.6.1 under Mac OSX/X11 > density(1) Error in if (!(lo <- min(hi, IQR(x)/1.34))) (lo <- hi) || (lo <- abs(x[1])) || : missing value where logical needed I am not sure how this should be handled. I encountered it in connection with densityplot(). In that connection, it might be enough to modify density() so that it

...41,0.97, 0.82,0.11,0.53,0.80,0.71,0.31,0.70,0.00,0.90,0.88,0.15,0.49,0.54,0.89,0.07,0.09,0.34, 0.20,0.73,0.37,0.94,0.49,0.40,0.55,0.68,0.89,0.00,0.42,0.94,0.06,0.53,0.46,0.11,0.96, 0.41,0.69) I need to find out the bandwidth and I have two ways of doing it as # as per the stats package bw1 = bw.nrd0(recovery_rates)             #    which gives me bw1 = 0.1050780 # as per the KernSmooth package bw2 = dpik(recovery_rates)                    #    which gives bw2 = 0.1027932 I am bit confused which bandwidth is to be used. Is it that "statistics package" deal with univariate data whil...

I issue the following: > d <- density(rnorm(1000)) > d and get: Call: density.default(x = rnorm(1000)) Data: rnorm(1000) (1000 obs.); Bandwidth 'bw' = 0.2235 x y Min. :-3.5157 Min. :2.416e-05 1st Qu.:-1.6892 1st Qu.:1.129e-02 Median : 0.1373 Median :7.267e-02 Mean : 0.1373 Mean :1.367e-01 3rd Qu.: 1.9639