similar to: package ‘contingency.tables’ is not available (for R version 2.15.2)

I installed R on my Windows laptop in C:\Program Files\R\R-2.15.2 and am able to open RGUI (640bit), see packages and run commands. However, when I test the installation and run the basic tests and all the tests on the standard and recommended packages i.e.: library("tools") testInstalledBasic("both") testInstalledPackages("base", errorsAreFatal

I am getting an error when trying to import tab delimited .txt file saved from Excel. I have read what is posted on the forums but still am confused. I saved my Excel file (DataTestforR.xlsx) as a tab delimited txt file (DataTestR.txt) on my Desktop. In the RGUI, I tried to import the txt file and got an error > myfile<-"C:\\Users\\jpapa\\Desktop\\DataTestR.txt" >

Hello all, Let say I have 2-way contingency table: Tab <- matrix(c(8, 10, 12, 6), nr = 2) and the Chi-squared test could not reject the independence: > chisq.test(Tab) Pearson's Chi-squared test with Yates' continuity correction data: Tab X-squared = 1.0125, df = 1, p-value = 0.3143 However I want to get all possible contingency tables under this independence

Dear all Seems that puzzles always come in packs. I was asked to help with some statistics in blood analysis. (You can not refuse your wife's asks :-). She has contingency table for values IgVH mutation and ZAP expression. I can do chi-square test (in R) and get a results, and with some literature I can try explain them. However she found an article in which they use SPSS and use

Hi, I have a table in R with data I needed and need to create a contingency table out of it. The table I have so far looks like this: Binger r DietType No Yes Dangerous 15 12 Healthy 52 9 None 134 24 Unhealthy 72 23 These are the error messages that I keep getting whenever I try to get a contingency table. I'm not sure why it won't work

I'd like to carry out a Monte Carlo simulation test where given data is a contingency table. I think this is something to do with using rmultinonom(), but I'm not sure how to code this, to simulate contingency tables. Could anyone please help with how to use R to simulate contingency tables like this? -- View this message in context:

Is Pearson's Chi-Square test for contingency tables asymptotically unbiased for large tables (large degrees of freedom) regardless of the expected values in each cell? The rule of thumb is that Pearson's Chi-square should not be used when large numbers of cells have expected values < 5. However, I compared the results on 4x4 contingency tables for R's chisq.test using chi-square

Hi All, I have a question regarding contingency tables. I would like to calculate the mean and standard deviation of a continuous variable from my own dataset based on the percentages of a contingency table I obtained from a scientific article. dataset<- data.frame(cbind(case=rep(0:1,5), x1=c(1:10), x2=c(0:9))) contingency_table<- matrix(c(100, 75, 65, 85, 90, 87), nrow=3, ncol=3) In

Dear all, I have a dataset with qualitative variables (factors) and I want to test the null hypothesis of independance between two variables for each pair by using appropriate tests on contingency tables. I first applied chisq.test and obtained dependance in almost all cases with extremely small p-values and warning messages. > chisq.test(table(data$ins.f, data$ins.st))$p.val [1]

Dear R-philes: I am having an issue with exporting contingency tables with xtable(). I set up a contingency and convert it to a matrix for passing to xtable() as shown below. v.cont.table <- table(v_lda$class, grps, dnn=c("predicted", "observed")) v.cont.mat <- as.matrix(v.cont.table) Both produce output as follows: observed predicted uh uh~ uh 201

Hello! May you help me? I'm trying to do a contingency table using this > data(iris) > library(rpart) > modelo <- rpart(Species ~., iris) > prev <- predict(modelo, iris) Finally the contingency table > table(iris$Species, prev) But an error occurs: Error in table(iris$Species, prev) : all arguments must have the same length And I do not understand why, may you

Hi all, I have been struggling with this problem for a few days. I have a data table like this: gene rpkm1 diff1 rpkm2 diff2 gene1 23 50 13 120 gene2 111 220 827 1200 gene3 75 998 71 910 And I want to re-format it so that, for each gene, I have a 2x2 contingency table, such as: gene rpkm diff gene1 23 50 gene1 13 120 gene2 111 220 gene2 827

I have had the same problem and I wrote this function rmulti <- function(n, size, p) { NrDim <- length(p) if(NrDim<2) stop("The simulated variabel has to be at least 2-dimensional") res <- matrix(data=NA, nrow=n, ncol=NrDim) p <- p/sum(p) TempSize <- size for(i in 1:NrDim) { TempP <- p[i]/sum(p[i:NrDim]) TempBin <- rbinom(n=n, size=TempSize,

hello everyone, i often work on contingency table that I create from data.frame (with table() function) but a friend sent me an excel sheet wich *already is* a contingency table (just a simple 2 way table !...) any clue on how to import it in R (keeping row names and col names) ? any tuto I come accross only mention the table transformation, but never the import of such data I only found

Dear Friends. I am just starting to use R. And in this occasion I want to construct a high-dimensional contingency table, because I want to crate a mosaic plot with the vcd package. My table is in this format: año ac.rep cat.gru conteos 1 2005 R parejas 253 2 2005 N parejas 23 3 2006 R parejas 347 4 2006 N parejas 39 5 2007 R

Dear List: Most of the analysis I do involves contingency tables. I am migrating to R from Stata and I have a number of questions about using contingency tables in R. I suspect that most of the things I want to do are very short R scripts that people on this list probably have. I wonder if you would be willing to share them. First, the presentation of tables by table() is not

Dear List, I would appreciate help on the following matter: I am aware that higher dimensional contingency tables can be analysed using either log-linear models or as a poisson regression using a generalized linear model: log-linear: loglm(~Age+Site, data=xtabs(~Age+Site, data=SSites.Rev, drop.unused.levels=T)) GLM: glm.table <- as.data.frame(xtabs(~Age+Site, data=SSites.Rev,

Hello, How can I create a symmetric contingency table from two categorical vectors having different length of levels? For example one vector has 98 levels TotalData1$Taxa.1 [1] "Aconoidasida" "Actinobacteria (class)" "Actinopterygii" "Alphaproteobacteria" [5] "Amoebozoa"

I am having difficulty getting the dynamic tree cut package to work. Given the data table "myddtable" LengthPlaceColorAge5HRed224ABlue205WGreen243GRed222GBlue236WGreen255ARed194H Blue23 I created a similarity matrix using DAISY and Gower metric and specified Place and Color columns as characters (since they are categorical variables) > dd.daisy<-daisy(myddtable, metric =

Hi all, I want to make a contingency table in R. I want to tabulate two variables, one as the independent and second as the dependent variable. The IV has two categories, namely, birth complications, and no birth complications. The frequency of birth complication category is fifty, and the frequency of no birth complication category is 34. The categories and frequencies of DV follows.