similar to: Two factors -> nurical data dependency analyzing

Hello to all users and wizards. I am regulary using 'boxplot' function or its analogue - 'bwplot' from the 'lattice' library. But they are, as far as I understand, totally flawed in functionality: they miss ability to select what they would draw 'in the middle' - median, mean. What the box means - standard error, 90% or something else. What the whiskers mean -

I am using lattice to make figures as pdfs: trellis.device(device = "pdf",file = "Figure6.pdf",color = FALSE) I need to specify some blank space on the left-hand margins (the pages will be bound so we need about 0.5 inch)). I have tried a number of solutions but none seems to work (e.g. par.set). Can this be done when initiating the plotting device? Or is the some other way

Hello, dear R users. I've already sent a question here, but I'm not sure that it had been read. I need to visualize classification of my numerical data based on 2-3 factors. As I suppose, the best way is a tree. With an orbitrary function at the ends (leaves), or at least with means of my data at the ends. What is the way to do it? As I found, ctree offers binary classification, but

Hello, dear R-users. I have 2-d dotplot with two variables: x, y. Dots on this dotplot are grouped in human-recogniseable areas. These areas are not round-shaped nor oval-shaped. They are free-form, but still recogniseable by an operator. What is ability in R to graphically (per mouse) define some area and to select all the cases felt in it? 'identify' is OK for 5-10 cases, but what

Hi, Suppose I have the following data frame. __BEGIN__ > library(MASS) > data(crabs) > crab.pca <- prcomp(crabs[,4:8],retx=TRUE) > crab.pca$rotation PC1 PC2 PC3 PC4 PC5 FL 0.2889810 0.3232500 -0.5071698 0.7342907 0.1248816 RW 0.1972824 0.8647159 0.4141356 -0.1483092 -0.1408623 CL 0.5993986 -0.1982263 -0.1753299 -0.1435941 -0.7416656 CW

Hi, I can't believe I am unable to do this. I just installed R (never used it before, but want to learn it). At present, though. all I want to do is export a data file (specifically "crabs" in the MASS library) to a text file for use in SPSS. It allows me to peek into the dataset, using the command head(crabs) but I can neither view the entire file nor can I seem to export it to a

Hi, Is there some alternative to the 'groups' argument in lattice's bwplot function for boxplots? Say in the example below: bwplot(yield ~ site | year, data = barley) you want to have two side by side boxplots per site, corresponding to each year in the barley data frame. Ideally, the space between boxplots of the same site should be smaller than that between boxplots of different

I've been experimenting with the lattice graphics in chapter 3 of MASS. The code to produce figure 3.14 is: data(crabs) library(mva) lcrabs.pc <- predict(princomp(log(crabs[,4:8]))) crabs.grp <- c("B", "b", "O", "o")[rep(1:4, rep(50,4))] splom( ~lcrabs.pc[, 1:3], groups = crabs.grp, panel = panel.superpose, key = list(text =

I'm trying to learn how to create Trellis multi-panel plots, but I'm having some trouble reproducing the graphs shown in Venables & Ripley (2002) (e.g., Figs 4.14 & 4.15). Actually, everything looks fine except for the fact that I can't see any text above the individual panels. I'm using R 1.9.0 for OS-X running on Mac OSX 10.3.3, and I'm drawing the graphs into

Hi all, I have a question about the SOM routine. You can either supply the initial representatives for the lattice yourself or else they are chosen randomly from the dataset. Is it possible to pass the random-seed as an argument somehow, when choosing the random initialisation of the lattice? As it is now, each time I run a SOM on a dataset with the same settings the resulting SOM will still

Can anyone explain the root of my problem? When I type the following code into R, I receive 42 support vectors insted of the 21 stated in the book 'Modern Applied Statistics with S': library(MASS); library(e1071); library(class); lcrabs <- log(crabs[,4:8]); (svm(crabs$sp ~ ., data = lcrabs, cost = 100, gamma = 1)); By changing the value of gamma I can obtain only 21 support vectors,

Hi, When I tried an example of the xgobi function, I got the following error. Could someone explain to me what is wrong ? Thanks a lot. xgobi(crabs,colors=c("SkyBlue","SlateBlue","Orange","Red")[rep(1:4,each=50)]) c:/PROGRA~1/R/R-23~1.1/library/xgobi/scripts/xgobi.bat -vtitle 'crabs' -std mmx

Hello, I'm new to R and in the process of testing it My first question: I fail to read in my data (ANSI toto.txt file, tab separated) > test <-read.table("toto.txt") Error in file(file, "r") : unable to open connection In addition: Warning message: cannot open file `toto.txt' > test <-

Hello could you please tell me what is the function to get the absolute value of the real or complex number. most of other languages it is abs(x) , what is it in r? I did few searhces in the help docs for no avail. thanks F.J

I have created an xyplot of a time series with the following code... win.graph(width = 10, height = 7) panel1 = function(x, y) { panel.loess(x, y, lwd=2.5, span=0.5, col="gray") panel.xyplot(x, y, pch=19, col="blue", cex=1.25) } xyplot(oneplusdensity ~ year, data=figdata, aspect="fill", cex=1.5, xlab=NULL, ylab=expression("Crabs per

Dear R users, I want to do discriminant analysis on my data. I have successfully followed the discriminant analysis in V & R on the iris data: > ir <- rbind (iris3[,,1],iris3[,,2],iris3[,,3]) > ir.species <- c(rep("s",50),rep("c",50),rep("v",50)) > a <- lda(log(ir),ir.species) > a$svd^2/sum(a$svd^2) [1] 0.996498601 0.003501399 > a.x <-

Dear friends, I have two questions regarding the use of lattice. First some code: ## begin code z <- cbind(rep(c("BIC", "ICL", "s_v", "Q_v", "sig-q", "s_lsk", "s_lML", "s_mlsk", "s_mlML", "s_la8", "s_haar"), each = 250), rep(c(5, 10, 20, 30, 50), each = 50)) z

Hi everyone, Does anybody knows if there is an equivalent R function that gives the same outcome as in "Explore" function in SPSS ? (Analize->Descriptive Statistics->Explore) It does a categorical vs quantitative variables analysis. ( But not linear regression) I need to compare intragroup (categorical variable with 4 values) means and confidence intervals of a quantitative

> Or, the front-end of my programming language has to analize the source > code, and convert it to LLVM-IR? Yes

Hello everyone, I`m trying to normalize and analize an illumina SNP array. But when i`m trying to segmentate i`m getting an error: Error in sort(abs(diff(genomdat)))[1:n.keep] : only 0's may be mixed with negative subscripts. I`ve tried everything to fix this but the error still occours. Can anybody give me a tip? Thanks in advance! -- View this message in context: