similar to: question regarding hypothesis testing in contingency tables

Hi, I want to set a make a contingency table which will look like this.. The problem is that I can't  set the table like the following.                      col1     col2       Total     row1              a         b         n10                      rp1       rp2        100                      cp1       cp2       rtp1   ---------------------------------------------     row2            

Dear list. New to R, I'm looking for a way of using crosstab to output low-dimensional (higher than 2) contingency tables (frequencies, per-cents by rows, % by columns, mean, quantiles....) I'm looking for something of the following sort dataframe: singers, categorical variates: voice category (soprano,mezzo-soprano, ...) , voice type( drammatic, spinto, lirico-spinto, lirico,

I hesitate to post this question in the light of recent threads, indeed I have hesitated for several weeks, however I have come to a full stop and really need some help if I am going to progress. I am a new user of R for medical statistics. I have attempted to read all the relevant documents, but would welcome any suggestions as to what I have missed. I am trying to contruct "table 1"

Hello there! I am still struggling with a binomial response over all categorical variables (some of them with 3 levels, most with 2 levels). After initial struggles with glm's (struggle coming from the data, not the actual analysis) I have decided to prefer contingency tables. I have my data such as: response:

Hi R-users, I have the following problem with CrossTable function within ?gmodels? package: the output of the function (format ?spss? and asresid=T) can not be stored within another object. For example: >library(gmodels) >data(infert, package = "datasets") > CrossTable(infert$education, infert$induced)->aa # the function prints everything ok on the screen > aa # works

Good evening R-users! I have the following problem: i want to get a weighted crosstable along with the adjusted standardized residuals test Example: a<-c(1,1,1,1,1,2,2,2,3,3) b<-c(10,10,23,24,33,45,46,70,21,66) c<-c(3,3,2,3,4,1,1,1,3,3) d<-c("a", "b","b","c","a","a","a", "b",

Hey All, I''m currently working on a Guitar Reviews website and I think i''ve been coding for way to long and forgot the basics..I''m having trouble doing a simple SQL method. I have 2 Tables, 1 for Guitar Manufacturers (gmanufacturers) and 1 for Guitar Models (gmodels), every model has a manufacturer and every manufacturer has many models (gmodel belongs_to

Full_Name: Thomas R Famula Version: 2.2.1 OS: Linux - fedora core 4 Submission from: (NULL) (169.237.28.28) Sorry to bother - I hope this is a simple fix. Here is the set of error messages I recieved in trying to install the "gmodels" package. I typed in "install.packages(c("gmodels"))" as the root and recieved the following series of replies:

This doesn't seem right; I'm using R version 2.4.1 (2006-12-18) on Mac OS and Win XP and find the same issue: > mydataframe <- data.frame(ppi=c(.5,.5),mmu=c(5,10)) > dput(mydataframe,"mydataframe.txt") > dget("mydataframe.txt") Error in attributes(.Data) <- c(attributes(.Data), attrib) : row names must be 'character' or 'integer', not

Hi I just installed the gmodels package and the installation was successful but when I was trying to load the library I got an error (see below). Interesting, yesterday I wrote to the maintainer of RSQLite apckage because I got the same error. Does somebody knows what is going on ?? thanks, Mayte library(gmodels) Error in dyn.load(x, as.logical(local), as.logical(now)) : unable to load

Hi I just installed the gmodels package and the installation was successful but when I was trying to load the library I got an error (see below). Interesting, yesterday I wrote to the maintainer of RSQLite apckage because I got the same error. Does somebody knows what is going on ?? thanks, Mayte library(gmodels) Error in dyn.load(x, as.logical(local), as.logical(now)) : unable to load

Dear Listers: I have a categorical feature selection problem for random forest. Suppose I have a multiple-leveled category variable A, which has m=3 levels: red, green, and blue and the final target is binary classification. I want to evaluate its power in discrimination between 2 classes. We know rf splits multiple-leveled category variable by considering all combinations of its levels. So

Hi, I asked this question before, which was hidden in a bunch of questions. I repharse it here and hope I can get some help this time: I have 2 contingency tables which have the same group variable Y. I want to compare the strength of association between X1/Y and X2/Y. I am not sure if comparing p-values IS the way even though the probability of seeing such "weird" observation under H0

Hi I've just installed the R version 2.10.1 on my new PC and unfortunately I have problem to use the function Estimables. I have downloaded gmodels succesfully but have problems when I activate the package. I get following meassage; > utils:::menuInstallLocal() package 'gmodels' successfully unpacked and MD5 sums checked > local({pkg <-

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:

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 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

I am trying to create contingency tables (to evaluate prior to performing Pearson's Chi-Squared test for independence). I would like to see column and row totals as well as expected and observed values and cell counts. I tried to use the package "contingency. tables" but get the following warning: package ‘contingency.tables’ is not available (for R versions 2.15.2) Is there

Dear R-users, I want to fit a linear model with Y as response variable and X a categorical variable (with 4 categories), with the aim of comparing the basal category of X (category=1) with category 4. Unfortunately, there is another categorical variable with 2 categories which interact with x and I have to include it, so my model is s "reg3: Y=x*x3". Using fit.contrast to make the

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