similar to: how to correlate nominal variables?

I have a quick statistical question and hoped somebody has a tip for me without me having to go to the local statistician on Monday. I assess 4 statements from 90 subjects. Each of the 4 statements receives one of three responses (say -1, 0, or 1). I can use Cramer's V or Spearman correlations to assess the correlation between each pair of statements, but I am looking for a measure of

I am trying to fit a rank-frequency distribution with 3 unknowns (a, b and k) to a set of data. This is my data set: y <- c(37047647,27083970,23944887,22536157,20133224, 20088720,18774883,18415648,17103717,13580739,12350767, 8682289,7496355,7248810,7022120,6396495,6262477,6005496, 5065887,4594147,2853307,2745322,454572,448397,275136,268771) and this is the fit I'm trying to do: nlsfit

Hi R community, These days, I am writing some functions to work with 2-ways frequency tables ; you know all this tuff about measures of association: Chisq and derived (phi, cramer's v), tau b, tau c, somer's d and so on. So I consider all those functions could be gathered in a single R file, as they are coherent and dealing with the same problem / objective (analysis of crosstables).

Does anyone know of any X^2 tests to compare the fit of logistic models which factor out the sample size? I'm dealing with a very large sample and I fear the significant X^2 test I get when adding a variable to the model is simply a result of the sample size (>200,000 cases). I'd rather use the whole dataset instead of taking (small) random samples as it is highly skewed. I've

Hi all: I am trying to do PCA on the following matrix. N1 N2 A1 A2 B1 B2 gene_a 90 110 190 210 290 310 gene_b 190 210 390 410 590 610 gene_c 90 110 110 90 120 80 gene_d 200 100 400 90 600 200 >dataf<-read.table("matrix") >

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Hi, I have a few methodological and implementation questions for ya'll. Thank you in advance for your help. I have a dataset that reflects people's preference choices. I want to see if there's any kind of clustering effect among certain preference choices (e.g. do people who pick choice A also pick choice D). I have a data set that has one record per user ID, per preference choice.

Hi useRs, I'm trying to fit a basic AR(2) model with the 'ar' function. And when I try to check the value of the coefficients, I could not find the same value as the 'ar' function. Here is my example: myserie <- c(212, 205, 210, 213, 217, 222, 216, 218, 220, 212, 215, 236) #plot(myserie, type="l") myserieminus0 <- tail(myserie, -2) myserieminus1 <-

Hi useRs, I'm trying to fit a basic AR(2) model with the 'ar' function. And when I try to check the value of the coefficients, I could not find the same value as the 'ar' function. Here is my example: myserie <- c(212, 205, 210, 213, 217, 222, 216, 218, 220, 212, 215, 236) #plot(myserie, type="l") myserieminus0 <- tail(myserie, -2) myserieminus1 <-

I have problem with integer overflow that I cannot understand. I have a character vector curr.lemmas with the following properties: length(curr.lemmas) # 61224 length(unique(curr.lemmas)) # 2652 That vector is the input to the following function: yules.k1 <- function(input) { m1 <- length(input); temp <- table(table(input)) m2 <- sum("*"(temp,

Dear list, according to the documentation of acf{ts} "the partial correlation coefficient is estimated by fitting autoregressive models of successively higher orders up to lag.max. " However, R seems to return the Yule-Walker estimates of the PACF by default. You can check this using c(1:10) as the series: the YW estimates are 0.7000000 and -0.1527035 for lags 1 and 2 . If the PACF

I have a consulting client with data containing a mixture of continuous, nominal, and ordinal variables, who wishes to do some correlational analyses. Ignoring for the moment whether this is wise or useful, is there some way to calculate a matrix using, for example, polychoric correlations for pairs of nominal variables, or correlations based on normal scores for ordinal variables? [The PRELIS

Hi All, I am wondering if there is any literature or any prior implementations of cluster analysis for only nominal (categorical) variables for a large dataset, apprx 20,000 rows with 15 variables. I came across one or two such implementations, but they seem to assume certain data distributions. Thank you, Nagu

Hi, are there readily available R packages that are able to perform FA on ordinal and/or nominal data? If not, what other approaches and helpful packages would you suggest? BR, Jay

Hello, i just want to share a quick n dirty patch to support nominal power in APC ups's. I was not sure about the name of the nut key and put it to ups.power.nominal Tested on APC SMARTUPS RS 550. basos -------------- next part -------------- A non-text attachment was scrubbed... Name: nut-2.4.3-apcnompower.patch Type: text/x-patch Size: 892 bytes Desc: not available URL:

Hi All, I have a question regarding the nominal jitter buffer configuration: The call was setup as G.729A (annexb=no), ptime=20ms and nominal jitter buffer size = 50ms, and round trip delay is 200ms, the TDM side will experience intermittent one way voice during the call, but IP side can always heard the voice from TDM side. My question is, should this possible caused by the nominal jitter

I'm tentatively creating a Windows .dll file using the Vorbis tools demos as a base for it. I've taken oggenc and simply converted it into a .dll file, with some minor changes purely to pass parameters to it (the original uses the command line). I have it working as far as it goes, but there seems to be something going wrong deep inside that I don't understand. The line at fault

Hi, I am a new user of R. I have 2 series of nominal data (2 series of answers for the same question) and I want to calculate the correlation between these 2 series. I've tried to use the correlation function (corr, ...) but all are for numeric data... Does anyone know what function should I use for the nominal data? Thanks a lot, Bianca

Hi! I'm looking for a solution to analyse data, which consists of dichotomous responses (yes/no) for 2 multinomial ordinal variables. I was trying glm() and got hierarhical models treating all variables as nominal, but I can't figure out how to tell glm() to use a model for ordinal data like this: log(Mij) = intercept + X + Y + Z + beta*(x-x')*(y-y') where beta is a

Hi: I have been working in Item Response Theory, exactly, with Nominal Response Model (NRM). Exist in R a function for estimate parameter and ability from database for this Model?. Thank you, Xavier G. Ordóñez [[alternative HTML version deleted]]