similar to: sm.density.compare

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

Is it possible to generate a density plot comparing several (6 or 7) groups of data and have the x-axis in date format (e.g.: "%d%b", 10Mar)? When I try to input the data in date format I get an error saying "this function only allows 1-d data". Thank you, Claudia [[alternative HTML version deleted]]

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 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

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),

> > (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

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

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".

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

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, there: It's my first time to post question in this forum, so thanks for your tolerance if my question is too naive. I am using a nonparametric smoothing procedure in sm package to generate smoothed survival curves for continuous covariate. I want to truncate the suvival curve and only display the part with covariate value between 0 and 7. The following is the code I wrote:

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

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

Hi R-Users, I am working with sm.ancova (in the package sm) and I have two problems with the graph, which is automatically generated when sm.ancova() is run. 1-Besides of the fitted lines, the observed data appeared automatically in the graph. I prefer that only fitted lines appear. I check the sm.options, but I could not find the way that the observed data do not appear in the graph. 2-I

I have discovered a problem with the compilation of the sm package. On debian 2.2, sm was not compiling. * Installing *source* package 'sm' ... ** libs g77 -fPIC -g -O2 -c routines.f -o routines.o gcc -shared -o sm.so routines.o -L/usr/lib/gcc-lib/i386-linux/2.95.4 -lreadline -ldl -lncurses -lg2c-pic -lm -L/usr/lib/R/bin -lR /usr/bin/ld: cannot find -lg2c-pic collect2: ld returned

I adjusted an exponential regression to the following data and wish to plot confidence bands as well. Is this possible? Any help greatly appreciated. Claudia x <- c(1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001, 2002,2003,2004,2005,2006,2007,2008,2009) y <- c(987,937,810,749,1087,807,1050,1294,1455,1022,927,403,698,1191,1078, 1176,1125,936,1263,647,868) dat <-

Has Bowman and Azzalini's sm library been ported to R yet (goes with Appied smoothing techniques for data analysis book)? I had a quick go but got tied up at a silly stage of ignorance caused I think (?hope) because I have never seriously used S+ at all. Rather than waste time, perhaps some kind soul has already done it. \John

Dear all, Does anybody know how to get more evaluation points in performing Nonparametric analysis of repeated measurements data with "sm" library. The following command gives the estimation for 50 points, by I would like to increase to 100 points But I do not know how to do that. library(sm) provide.data(citrate, options=list(describe=FALSE)) provide.data(dogs,