similar to: kernel density plot

Hi, I am trying to read the "credit.lisp" file of the Japanese credit database in UCI repository, but it is in lisp format which I do not know how to read. I have not found how to do that in the foreign library http://archive.ics.uci.edu/ml/datasets/Japanese+Credit+Screening <http://archive.ics.uci.edu/ml/datasets/Japanese+Credit+Screening> Could anyone help me? Best

I have some data 'myData' in wide form (attached at the end), and would like to convert it to long form. I wish to have five variables in the result: 1) Subj: factor 2) Group: between-subjects factor (2 levels: s / w) 3) Reference: within-subject factor (2 levels: Me / She) 4) F: within-subject factor (2 levels: F1 / F2) 5) J: within-subject factor (2 levels: J1 / J2) As this is the

I have a dataset that is based on crop output for a single crop *sugarcane*...but each observation is further subdivided into 3 kinds of sugarcane i have read the file and have also used melt it to sort it on the basis of *crop group* using melt(sugarcane, m=c("Crop_group")) -> canefile this is how it has cast the data variable value 1 Crop_group Sugarcane

I have data on area under cultivation of a crops for 17 states..I need a scatter plot to be made for the following assignment Read the data file /Data/ep602/areas.csv, and make a scatter plot that displays area under rice in 1960s on the x axis, and percentage change in area under rice between 1960 and 1990 on the y axis. Let origin of the plot be at (50 per cent of area of rice in India as a

I'm havinf a problem with a simple file i have the following data State 1960 1970 1980 1990 1 All India 35988.70 37346.00 39707.30 42321.00 2 Andhra Pradesh 3431.03 3163.27 3687.23 3695.63 3 Assam 1902.93 2001.60 2278.47 2525.33 4 Bihar 5277.07 5133.80 5138.70 4662.57 5 Gujarat 538.13 456.10 484.23 590.47 6

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'

I have this assignment to do and after ten hours of constant trying my eyes ache and i give up.. all i'm able to get is this plot please help me these are the commands i have used till now read.table(file.choose(), sep=";", header=T) read.table(file.choose(), sep=";", header=T)->areas melt(areas,id=c("Year","State"),m=c("Rice"))->

Hello R helpers, I am producing a figure with dual strips, i.e., x~y | S1 + S2, where S1 and S2 are two strips. For example, in figure 2.1 at http://lmdvr.r-forge.r-project.org/figures/figures.html. In this case, I would like to combine the the top strip, since all three pictures in the same row have the same level based on the first strip. In other words, instead of | -- S11 -- | -- S11 -- |

Good Evening- I have a dataframe that has 10 columns that has a header and 7306 rows in each column, I want to combine these columns into one. I utilized the stack function but it only returned 3/4 of the data...my code is: where nfcuy_bw is the dataframe with 7305 obs. and 10 variables Once I apply this code I only receive a data frame with 58440 obs. of 2 variables, of which there should be

Hi Folks, Given k RVs with MVN distribution N(mu,S) (S a kxk covariance matrix), let (w.l.o.g.) X1 denote the first r of them, and X2 the last (k-r). Likewise, let mu1 and mu2 denote their respective expectations. Then, of course, the expectation of X2 given X1=x1 is mu2 + S21*inv(S22)*(x1 - mu1) and the covariance matrix of X2 given X1=x2 is S22 - S21*inv(X11)*S12 where Sij is the

If I'm understanding what's going on in this test correctly, what's happening is: * ARMTargetLowering::LowerCall prefers indirect calls when a function is called at least 3 times in minsize * In thumb 1 (without -fno-omit-frame-pointer) we have effectively only 3 callee-saved registers (r4-r6) * The function has three arguments, so those three plus the register we need to hold the

If your phones support PoE, I have had huge success with Zyxel: http://www.amazon.com/ZyXEL-ES1100-16P-16-Port-Ethernet-Unmanaged/dp/B00 5GRETMM/ref=sr_1_3?ie=UTF8&qid=1426296572&sr=8-3&keywords=zyxel+poe If you want to go even cheaper, I have successfully used these as well: http://www.amazon.com/TRENDnet-8-Port-100Mbps-Switch-TPE-S44/dp/B000QYEN

Ok, So this is really more of an observation than anything else.? I had a FreeBSD 10.1 server that was running great. Some SSL issue came up, or I upgrade Dovecot in ports - something occurred and the machine started rebooting randomly.? It would run for 2 weeks, then reboot.? It might run for 5 days and then reboot. So I started doing more FreeBSD upgrades, thinking it was a kernel issue. The

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

Dear All, (this msg is a statistics/computing question to the list) I'm trying to implement a modern-version of a (classic) "several-step protocol" in Fishery Biology (due 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

If my extension is 22, and voice mail access number is 909, then with exten => 909,1,voicemailmain(s22) I can access voice mail 22, without number and password prompt. But, I want that every extension can access its voice mail without number and password. So, when I put exent => 909,1,voicemailmain(${calleridnum}) voicemail want only password. I want to eliminate password too, so when I

Dear Knowledgeable R Community Members, Please excuse my ignorance, I apologize in advance if this is an easy question, but I am a bit stumped and could use a little guidance. I have a finite mixture modeling problem -- for example, a 2-component Weibull mixture -- where the components have a large overlap, and I am trying to adapt the "mclust" package which concern to normal