similar to: plot(table..) using pairs

Ok, here is the class, I want to Unit Test, its part of a large app and is based on EventMachine library. I want to mock the class TickServer ( i.e not stub it) . Since in actual scenario, you can''t do this on this class: @server = TickServer.new # will toss an exception at your face you must initialize the server like this: EventMachine.run {

Hello, I am working on my thesis and can't really figure out how to produce a reasonable graph from the output from my glm., I could just give the R-output in my results and then discuss them, but it would be more interesting if I could visualise what is going on. My research is how bees react to different fieldmargins, for this I have 4 different types of field margin (A,B,C & D) and

## About DMA APIs Now, virtio may can not work with DMA APIs when virtio features do not have VIRTIO_F_ACCESS_PLATFORM. 1. I tried to let DMA APIs return phy address by virtio-device. But DMA APIs just work with the "real" devices. 2. I tried to let xsk support callballs to get phy address from virtio-net driver as the dma address. But the maintainers of xsk may want to use

Using one numeric and 2 factor variables densityplot( ~ Numeric | Factor1, ...) densityplot( ~ Numeric | Factor2, ...) .. both work, but densityplot( ~ Numeric | Factor1*Factor2, ...) produces the error Error in if (!(lo <- min(hi, IQR(x)/1.34))) (lo <- hi) || (lo <- abs(x[1])) || : missing value where logical needed None of the variables contain missing values. Any ideas

Dear List, I'm having problem with an exercise from The R book (M.J. Crawley) on page 567. Here is the entire code upto the point where I get an error. data(UCBAdmissions) x <- aperm(UCBAdmissions, c(2, 1, 3)) names(dimnames(x)) <- c("Sex", "Admit?", "Department") ftable(x) fourfoldplot(x, margin = 2) dept<-gl(6,4) sex<-gl(2,1,24)

I have a data frame with about 60 columns. I would like to run a chisq.test on all possible pairs of columns and on all triples. Is there an easy way to do it ? Andreas -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in

Dear R Help Community, I have a question and an answer (based on reading this forum and online research), but I though I should share both since probably there's a much better way to go about my solution. My question is specifically about how to best visualise multiple response contingency tables. What I mean by 'multiple response' is that the total number of responses per row of a

Hi all, I'm currently working on implementing ACLE extensions for ARM. There are some memory barrier intrinsics, i.e.__dsb and __isb that require the compiler not to reorder instructions around their corresponding built-in intrinsics(__builtin_arm_dsb, __builtin_arm_isb), including non-memory-access instructions.[1] This is currently not possible. It is sometimes useful to prevent the

Dear list, I wish to visualise some 4D data as a kind of colour / translucent cloud in 3D. I haven't seen such plots in R (but perhaps I missed a feature of rgl). The easiest option I found would be to export the data in povray's df3 (density file) format and visualise it with povray. The format specification baffles me a little, http://www.povray.org/documentation/view/3.6.1/374/ ;

Dear list users How is it possible to visualise both a linear trend line and a quadratic trend line on a plot of two variables? Here my almost working exsample. data(Duncan) attach(Duncan) plot(prestige ~ income) abline(lm(prestige ~ income), col=2, lwd=2) Now I would like to add yet another trend line, but this time a quadratic one. So I have two trend lines. One linear trend line

Hi, I have recently begun using the lattice package, and have been using the wireframe command to visualise matrices which are model outputs. I have been trying to plot two surfaces (from two matrices) simultaneously in one panel, to visualise intersections etc., but neither my attempts or trawling the net are helping me find how to do this. Can someone help? Thanks Cheers, Umesh Srinivasan

Hi, I have the following data about courses (504) in a university, two attributes about the proportion of resources used (#resources_used / #resources_available), namely the average and the standard deviation. Thus I have: [1] n=504 rows [2] 1 id column and 2 attributes Here's a sample of the data: courseid,average,std 12741,1,0 17161,1,0 12514,1,0

Dear list! I would like to calculate "chisq.test" on simple data set with 70 observations, but the output is ''Warning message:'' Warning message: In chisq.test(tabele) : Chi-squared approximation may be incorrect Here is an example:         tabele <- matrix(c(11, 3, 3, 18, 3, 6, 5, 21), ncol = 4, byrow = TRUE)         dimnames(tabela) <- list(        

Hi, This report deals with p-values coming from chisq.test using the simulate.p=TRUE option. The issue is numerical accuracy and was brought up in previous bug reports 3486 and 3896. The bug was considered fixed but apparently was only mostly fixed. Just the typical problem of two values that are mathematically equal not ending up numerically equivalent. Consider this series of three 2x2

Hi, In the chisq.test(), if the expected frequency for some categories is <5, there will be a warning message which says Warning message: Chi-squared approximation may be incorrect in: chisq.test(x, p = probs) I am wondering whether there are some methods to get rid of this mistake... Seems the ?chisq.test() doesn''t provide more options to solve this problem. Or, the only choice is

Full_Name: Reginaldo Constantino Version: 2.8.0 OS: Ubuntu Hardy (32 bit, kernel 2.6.24) Submission from: (NULL) (189.61.88.2) For many tables, chisq.test with simulate.p.value=TRUE gives a p value that is obviously incorrect and inversely proportional to the number of replicates: > data(HairEyeColor) > x <- margin.table(HairEyeColor, c(1, 2)) >

Hi, I was wondering why anova() and drop1() give different tail probabilities for F tests. I guess overdispersion is calculated differently in the following example, but why? Thanks for any advice, Tom For example: > x<-c(2,3,4,5,6) > y<-c(0,1,0,0,1) > b1<-glm(y~x,binomial) > b2<-glm(y~1,binomial) > drop1(b1,test="F") Single term deletions Model: y ~

I am getting "Chi-squared approximation may be incorrect in: chisq.test(x)" with the data bleow. Frequency distribution of number of male offspring in families of size 5. Number of Male Offspring N 0 518 1 2245 2 4621 3 4753 4 2476 5

> On 28 Dec 2017, at 13:08 , Kurt Hornik <Kurt.Hornik at wu.ac.at> wrote: > >>>>>> Jan Motl writes: > >> The chisq.test on line 57 contains following code: >> STATISTIC <- sum(sort((x - E)^2/E, decreasing = TRUE)) > > The preceding 2 lines seem relevant: > > ## Sorting before summing may look strange, but seems to be >

The cells are interpreted as counts, so by scaling you're analyzing a different experiment (one with fewer observations). So the chi-squared value will change (the terms (O-E)^2/E in the statistic scale linearly ignoring rounding and "Yates' continuity correction"). The chisq.test on the original data is a test of association. Conventionally you decide ahead of time on a