similar to: Separating out data values

I have 3 variables relating to the successful introductions of species to 95 different areas: introduction frequency; number of successes pre 1906; number of successes post 1906 The data are not normal, nor homo-skedatic, so I am using non-parametric statistics. I have calculated Kendall's tau between both introduction & successes pre 1906 (tau=0.3903) and introduction & successes

I have a table which looks like this:   ACC        BAL 1 hal -171245.33 2 opn  -50487.63 3 pga  213440.38 4 prt       0.18 5 rbs    8292.54 How do I create a column which shows the running totals of the BAL columns? [[alternative HTML version deleted]]

Dear colleagues, I teach statistics using SPSS. I want to use R instead. I hit on one problem and I need some quick advice. When I want to work with ordinal variables, in SPSS I can compute the median or create a barchart or compute a spearman correlation with no problems. In R, if I "read" the ordinal variable as numeric, then I cannot do a barplot because I miss the category names. If

Dear all, The other day I was reading this post [1] that slightly surprised me: "To reject the null of no correlation, an hypothsis test based on the normal distribution. If normality is not the base assumption your working from then p-values, significance tests and conf. intervals dont mean much (the value of the coefficient is not reliable) " (BOB SAMOHYL). To me this implied that in

>I am interested in R as an alternative for a statistical tool >at our firm. Ditto... I have recently moved to this agency from a company where I had access to Splus. There is also a coworker here who had used Splus at a previous employer. We both would like some access to the S language. We are considering either begging loud and long to try to get the agency to purchase two copies of

Dear R-"helpers"! May I kindly ask the pure statistics-experts to help me for a purpose which first part is not directly concerned with R. Consider two distribution functions, say f and g. For both, the median is smaller than a half. Now, the multiplicative or additive linkage of both distribution leads to a new distribution function, say h, whereas the median of h is greater than a

Hi All, I need to calculate the median for even number of data points.However instead of calculating the arithmetic mean of the two middle values,I need to calculate their geometric mean. Though I can code this in R, possibly in a few lines, but wondering if there is already some built in function. Can somebody give a hint? Thanks in advance [[alternative HTML version deleted]]

I have a linear model y~x1+x2 of some data where the coefficient for x1 is higher than I would have expected from theory (0.7 vs 0.88) I wondered whether this would be an artifact due to x1 and x2 being correlated despite that the variance inflation factor is not too high (1.065): I used perturbation analysis to evaluate collinearity library(perturb)

Hi all, Thanks for the really great help I've received on this board in the past. I have a very particular graph that I'm trying to plot, and I'm not really sure how to do it. I think I should be able to use ggplot for this, but I'm not really sure how. I have a data.frame which contains fifty sub frames containing one hundred data points each. I can do a histogram of each of

Hello, I have conducted an SEM in which the resultant standardized path coefficients are much higher than would be expected from the raw correlation matrix. To explore further, I stripped the model down to a simple bivariate relationship between two variables (NDVI, and species richness), where it's my understanding that the SEM's standardized path coefficient should equal the correlation

Hi everyone, I am Evelina and i have just joined the forum and hope to be able to support soembody as well as finding some help for my stats problem. I am trying to plot Frequencies against Densities of an ecological community using a Olmstead-Tukey diagram (O-T diagram). it is based on the Corner test for Association. I seem to undestand that the test can be run in R using the cor.test()

Hello, I have a statistical problem that I am using R for, but I am not making sense of the results. I am trying to use multiple regression to explore which variables (weather conditions) have the greater effect on a local atmospheric variable. The data is taken from a database that has 20391 data points (Z1). A simplified version of the data I'm looking at is given below, but I have a

Hi, R people, I wonder if there is a statistics than can measure the correlation for more than two random variables, instead of computing the correlation coefficient matrix. If so, what R package should I use? Right now I can only think of the mean of all pair-wise correlation coefficients, e.g., (corr(x,y) + corr(x,z) + corr(y,z)) / 3 for three random variables (x, y, z). Thanks a

I have a bunch of benchmark measurements that look something like this: sample.1 0.0000066660 0.0000062500 0.0000058330 0.0000058330 0.0000058330 sample.2 0.0000058330 0.0000058330 0.0000058330 0.0000058330 0.0000058330 sample.3 0.0000062500 0.0000062500 0.0000070830 0.0000062500 0.0000066660 i.e each measurement take on one of a set of values. The set values isn't fixed, but

Dear all When using Sweave, I'm always hitting the same bump: I want to group repetitive calls in a function, but I want both the results and the function calls in the printed output. Let me explain myself. Consider the following computation in an Sweave document: summary(iris[,1:2]) cor(iris[,1:2]) When using these two calls directly, I obtain the following output: > summary(iris[,1:2])

Hi, Please consider the example below. How can I manage to overlay the points the way I want in the second case? Thanks, Joh library(ggplot2) # Modify data to match "real" case myDiamonds <- diamonds myDiamonds[["clarity"]] <- as.character(myDiamonds[["clarity"]]) myDiamonds[myDiamonds[["clarity"]]=="I1","clarity"] <- 1

This is a general statistics question so I'm sorry if its outside the field of r help. Anyway, I have a suite of female and male traits and I have made a matrix of correlation coefficients using rcorr(). This results in a 6 by 6 matrix like this.. [1] 0.11287990 0.20441361 0.23837442 0.04713234 0.04331637 0.01461611 [7] 0.22627981 0.11720108 0.14252307 0.19531625 0.29989953 0.09989502

I am scraping data from a web page using XML (excellent package BTW - that's scraping data the easy way!). So far, I've got the code: tables <- readHTMLTable(theurl) rhf <- tables$tabResHistFull div1 <- rhf[which(rhf$V1=="Div ps"),] div1 which is giving me the result:        V1 V2    V3    V4    V5    V6    V7          V8    V9   V10   V11   V12   V13   V14  V15 15

Hi, I'm running this for a simulation study, so many combinations of parameter produce many predictions that I need to correlate with data. The problem ---------------- I'm using rating data with 3 to 5 categories (e.g., too low, correct, too high). The underlying continuous scales should be normal, so I chose the polychoric correlation. I'm using library(polychor) in its

Dear R-List, I would like to recode categorial variables into binary data, so that all values above median are coded 1 and all values below 0, separating each var into two equally large groups (e.g. good performers = 0 vs. bad performers =1). I have not succeeded so far in finding a nice solution to do that in R. I thought there might be a better way than ordering each column and recoding the