similar to: [sm.density.compare] scale up y-axis and additional line type

Hi, I want to plot grouped bars to compare 95% confidence interval estimates from two models. Each bar represents a 95% confidence interval estimate of a coefficient from one of the two models. Each group represents confidence interval estimates of the same coefficient from the two models. I think such a bar plot will nicely present whether 95% confidence interval estimates of the same

Hi, I need to find the z-score of the data present in a speardsheet. The values needs to be calculated for each gene across the samples (refer the example). And, it should be a simple thing, but I am unable to do it right now ! The example re the structure of the spreadsheet is - # Example: MyFile <- read.csv( text= "Names,'Sample_1','Sample_2','Sample_3'

Hello, I have a data set that looks like this: name G-ID test_id g-id g 1 00077464 C_068131 C_068131 OC_068131 - 2 00051728 C_044461 C_044461 OC_044461 - 3 00058738 C_050343 C_050343 OC_050343 - 4 00059239 C_050649 C_050649 OC_050649 - 5 00001761 C_000909 C_000909 OC_000909 - 6 00005119 C_002752 C_002752 OC_002752 - locs

Dear useRs, I have a slightly complicated data structure and am stuck trying to extract what I need. I'm pasting an example of this data below. In some cases, there are duplicates in the "gene_id" column because there are two different "sample 1" values for a given "sample 2" value. Where these duplicates exist, I need to average the corresponding

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 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

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

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

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?

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]]

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 there, I've looked through the very helpful advice about adding fitted lines to plots in the r-help archive, and can't find a post where someone has offered a solution for my specific problem. I need to plot logistic regression fits from three differently-sized data subsets on a plot of the entire dataset. A description and code are below: I have an unbalanced dataset

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

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

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, 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:

> > (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 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,