similar to: sm.density.compare, date on x axis?

I've been using the sm.denstiy.compare function and would like to know some specifics: a) What kind of bootstrap sampling is used, with or without replacement? b) A p-value is returned after running the following script (for example): library(sm) y <- rnorm(100) g <- rep(1:2, rep(50,2)) sm.density.compare(y, g, model="equal") to what statistic does this p-value correspond?

Dear fellows, The two questions are on sm.density.compare(). I compare kernel density estimates of two arrays of data. I'd like to scale up y-axis so that I can show better the differences in y values. English is not my first language so I'll try to explain it. I would like to stretch y-axis a bit longer but not to change the range of y values. How can I do this? Second, I'm

I am running the sm.density.compare function amd I am getting the following error:   my code is  > sm.density.compare(LBSTRESN,COHORT,xlab="Units = umol/L"subset = LBTEST=="Creatinine")   Error in if (from == to) rep.int(from, length.out) else as.vector(c(from,  :   missing value where TRUE/FALSE needed   I do not understand the error and I have had no help when searching

Dear all, Let say I have a sets of numbers: rno1 = rnorm(1000) rno2 = rnorm(1000) If I write request as follow: sm.density.compare (rno, rno3, xfit=min(rno), max(rno2)) why I receive a lot of curves in my plot, while I have only two data sets? regards Przemek -- View this message in context: http://r.789695.n4.nabble.com/sm-density-compare-a-lot-of-curves-tp4253039p4253039.html Sent from the

Dear List, I have been trying to use library sm to do density estimation on a 3D dataset. I am using the current MacOS X binary of sm from CRAN. If I do this on a 2D dataset, sm.density returns a list including the component "estimate" which contains the density estimate over a uniform grid. When doing this with 3D data, although I get a nice plot (even when I don't ask for one),

Hi! I have two dimensional dataset which has and I need to decide if a point lies in some "confidence level". If a point has low confidence/density it can be anomaly which I need to find. For example: #load library library(sm) #get some data x.locs = c(74, 74.5, 75, 77,74.5) y.locs = c(64, 63.5, 63, 61,61.5) points = cbind(x.locs, y.locs) #plot it plot(points) #get points density

> > (Um. So what device were you using?) In terms of hardware, I'm using a Dell Latitude CPx (not quite sure on the RAM, but the hard disk is 2GB and the server its linked to is much larger...I'm running on the unit's hard drive though). The printer is an EPSON LP-9300. I'm printing directly from the window one gets in R by typing plot(...). Another quick note, its

Dear R users, I used sm.density function in the sm package and kde2d() in the MASS package to estimate the bivariate density. Then I calculated the Kullback leibler divergence meassure between a distribution and the each of the estimated densities, but the asnwers are different. Is there any difference between the kde2d and sm.density estimates? if there is a difference, then which is the best

Estimada comunidad, nuevamente con algo que no se como hacer en R, pero aprendiendo .... El problema es el siguiente: 1. tengo un conjunto de 10 mil datos (n) cuyo valor va entre 0 y 10.000 a los que aplico una funcion sm.density() para obtener una especie de histograma pero con una curva que parece continua. 2. Una vez que obtengo esa curva necesito calcular el area bajo la curva en ciertos

Hello to everyone I''ve downloaded the version sm2 for smoothing methods and when I try the simple code y <- rnorm(50) sm.density(y, model = "Normal") I get the error message Error in if (any(omit)) { : missing value where logical needed I''m running R 1.1.1. on windows 98 Anybody can help? -------------- next part -------------- An HTML attachment was

I would like to remove rows from the following data frame (df) if there are only two specific elements found in the df$ch character string (I want to remove rows with only "0" & "D" or "0" & "d"). Alternatively, I would like to remove rows if the first non-zero element is "D" or "d".

Hi, I'm using R 1001 for Windows NT and the sm library. I'm trying to create plots for my data set like Bowman and Azzalini have in Figure 1.8 (p. 9) of their book for my data (i.e. a contour plot for each group in my data set and its all plotted on 1 plot). The problem I'm having is that R is not drawing closed contour lines for each group. Sometimes it does; other times it

Hi, I am doing kernel density plots, and am trying to make the lines thicker. I comparing three groups, in sm.density.compare. I tried changing lwd to make the line sthicker right on the density compare call, but was not able to do it. There is not an option in sm.options to specify line thickness, as well as cex.ylab or cex.xlab- I tried it and it does not change the thickness of the lines.

I have the following data which I tried to draw the probability density plot. Here is the code I have: x <- read.table("mydat.txt"); d <- rep(x$V2,times=x$V3); hist(d,probability=T, xlab="FlowSignal"); But why the y-axis range from 0 to 6, instead of 0 to 1? What's the correct way to plot it? #id flowsignal frequency 1 0.67 1 1 0.70 1 1

I've got a few issues with the x-axes in the histogram and density plots. First, often the default x-axis doesn't even extend to the length of my data. R often draws histogram bars (or density lines) farther than the drawn x-axis extends. For example, I might have a histogram bar at -15,000. But I wouldn't know that, because the most negative number on the x-axis is -10,000. The

Hi all Just this morning I upgraded to R 2.12.0 (for Mac OS X 10.6.4). All went well until I needed to run a help() or help.search() in my session, which I'm running within Emacs (ESS 5.3.7). Say I need help with the command 'density'. When I type help(density) or ?density the ESS help buffer opens, it is titled *help[R](density)* but it contains only a couple of lines saying,

Hello, To indicate probability densities instead of counts on a histogram, I specify freq = FALSE. However, I expect that summing all top y coordinates over all the intervals of the histogram will provide 1. 1) v <- c(0.2885, 0.2988, 0.3139, 0.2615, 0.3179, 0.3163, 0.2583, 0.3052, 0.2527, 0.3147, 0.3235, 0.2408, 0.2480, 0.3108, 0.3577, 0.2829, 0.2694, 0.3275, 0.3314, 0.2639, 0.3076,

The sm package (by Adrian Bowman and Adelchi Azzalini) implements a variety of nonparametric smoothing techniques, centred on nonparametric regression for one or two covariates and density estimation for up to three variables. A new version of the package is now available on CRAN. In an earlier unannounced version (2.1), a variety of methods of bandwidth selection were added, with default

The sm package (by Adrian Bowman and Adelchi Azzalini) implements a variety of nonparametric smoothing techniques, centred on nonparametric regression for one or two covariates and density estimation for up to three variables. A new version of the package is now available on CRAN. In an earlier unannounced version (2.1), a variety of methods of bandwidth selection were added, with default

Dear R experts, density estimation on a 2 dimensional bounded domain --------------------------------------------------------------------- I am currently trying to estimate the probability density (PD) of cancers within the breast using the sm library with the routine sm.density Of course a practical PD must be limited by the curve of the breast outline. I don't have a clue after perusing