search for: crabbing

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 folks, We would like to add SeaHorn <http://seahorn.github.io/> and Crab-llvm <https://github.com/seahorn/crab-llvm> to the list of LLVM's users on http://llvm.org/Users.html under Open Source Projects. The descriptions would be: 1. SeaHorn | An Algorithmic Logic-Based Reasoning Framework. 2. Crab-llvm | A static analyzer based on abstract interpretation for LLVM. I attach a

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

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 get the coefficient of partial determination for each of three independent variables. I've tried mvr in package pls.pcr. I'm a little confused by the output. I'm curious how I can order the LV's according to their names rather than their relative contribution to the regression. For instance, using the crabs data from MASS I made a regression of FL~RW+noise

"she is a great fool for going away, if she liked him."<BR>and the liberty of a manor, it was doubtful to many of those who best knew the easiness of his temper,<BR>and aunt so well, that i am not afraid of requesting it, though i have still something more to ask of the<BR>commerce; but with respect to any other leading characteristic, i do not imagine that much has

"she is a great fool for going away, if she liked him."<BR>and the liberty of a manor, it was doubtful to many of those who best knew the easiness of his temper,<BR>and aunt so well, that i am not afraid of requesting it, though i have still something more to ask of the<BR>commerce; but with respect to any other leading characteristic, i do not imagine that much has

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

r-package-devel is the right mailing list for your query, not here. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Thu, Mar 29, 2018 at 6:56 AM, Lucas Ferreira Mation <lucasmation at gmail.com> wrote: > I created

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

Hola buenas tardes: Vuelvo a solicitar su ayuda porque no puedo saber que pasa. Estoy usando el documento 'Regresión Logística Categor/Tema 4 Cate.pdf como guía y el ejemplo que ustedes conocen de los cangrejos (crab.txt). Cargo los datos, los guardo en un data frame llamado tabla; al pedir tabla[1:5,] obtengo: color spine width satell weight 1 3 3 28.3 8 3050 2 4

I created this package, https://github.com/lucasmation/microdadosBrasil which we are preparing for Cran submission. The package facilitates downloading and reading most of the microdatasets (household surveys, Census, etc). For each dataset it: (a) downloads the data from the data providers (b) "unzip" the necessary files (c) imports the data into R. For (b), some of the datasets

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

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

Hello, Me and four other students at Royal Institute of Technology (KTH) in Sweden are in the startup phase for an interesting project utilizing Xen. We have been asked to modernise and improve the performance of a router simulation package called Crab[1]. Currently Crab runs a few instances of UML and inside each guest OS Quagga[2] is run. Quagga is a routing daemon with interface similar

Hello everyone, Assuming this is the right place to post potential bug reports, try this : (Apologies if it's been posted/covered somewhere else, but your lists don't seem to have a search facility...) nmbd had a good go at fork bombing my "server" just now, with a setup as follows : Samba 2.2.0a on Slackware Linux v8.0, linux kernel 2.4.16. i586 with 500ish mb of ram.

Hi, I am wondering if anyone can explain to me if cumulative incidence (CI) is just "1 minus kaplan-Meier survival"? Under what circumstance, you should use cumulative incidence vs KM survival? If the relationship is just CI = 1-survival, then what difference it makes to use one vs. the other? And in R how I can draw a cumulative incidence plot. I know I can make a Kaplan-Meier

Hello, I have a dataset which contains multiple trap pulls over the course of a fishing season. Each trap was baited and then returned to the water to be pulled again. I am trying to remove that bait from the catch observed on the next pull (the bait is the same as the catch, lobster). The traps have been baited differently throughout the season. I would like to randomly remove two lobsters

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