similar to: a pickle with ranks and reals?

OK, I am try to produce an array of ranks. I have a set of data (s) that looks like this rt subj 312 dave 467 dave 411 dave 383 kim 398 kim ... Now I want to make a column that is an array of the ranks of rt by subject. The closest of gotten is using the following. r <- by (s, s$subj, function(d) rank(d[1])) This gets the data out with ranks but I cannot figure out how to easily

I"ve got a data frame with a selection of columns I want to compute a rank-order correlation matrix from without disturbing the original data frame. foo[,c("a","b","d","f","g")] What I wanted to do, intuitively, was: > cor(rank(foo[,c("a","b","d","f","g")])) but rank in that context

Dear r-helpers, Spencer Graves and Manual Morales proposed the following methods to simulate p-values in lme4: ************preliminary************ require(lme4) require(MASS) summary(glm(y ~ lbase*trt + lage + V4, family = poisson, data = epil), cor = FALSE) epil2 <- epil[epil$period == 1, ] epil2["period"] <- rep(0, 59); epil2["y"] <- epil2["base"]

Dear all I'm unable to find an example of extracting the rotated scores of a principal components analysis. I can do this easily for the un-rotated version. data(mtcars) .PC <- princomp(~am+carb+cyl+disp+drat+gear+hp+mpg, cor=TRUE, data=mtcars) unclass(loadings(.PC)) # component loadings summary(.PC) # proportions of variance mtcars$PC1 <- .PC$scores[,1] # extract un-rotated scores of

Dear R-Users, I am currently looking for a way to test the equality of two correlations that are related in a very special way. Let me describe the situation with an example. - There are 100 respondents, and there are 2 points in time, t=1 and t=2. - For each of the respondents and at each of the time points, I have information on 10 X-variables and on 10 Y-variables. - Based on this

Dear R-helpers, I would like to do a Spearman rank order test, and used the cor() function with the method "spearman". It gives me a number (correlation coefficient?) , but how can I get the p-value? Thank you for the help in advance! Regards, Anne-Katrin -- [[alternative HTML version deleted]]

Hello R-User! I appologise in advance if this should also go into statistics but I am presently puzzled. I have a data.frame (about 300 rows and about 80 variables) and my variables are dichotomous factors, continuous (numerical) and ordered factors. I would like to calculate the linear correlation between every pair of my variables, because I would like to perform a logistic regression (glm())

Hi there, I got a piece of code for the Iris data which allows to display correlation coefficients for each Iris species in the lower panel (color coded). I would now like to add e.g. a "*" to show the significance of each correlation next to the correlation coefficient. Furthermore I would like to make a t.test between the species "setosa" and "versicolor" for

Arrrrr, I think I've found a bug in the behavior of the stats::cor function when NAs are present, but in case I'm missing something, could you look over this example and let me know what you think: > a = c(1,3,NA,1,2) > b = c(1,2,1,1,4) > cor(a,b,method="spearman", use="complete.obs") [1] 0.8164966 > cor(a,b,method="spearman",

Hallo! Is there an error in cor to calculate Spearman correlation with cor if there are NA's? cor.test gives the correct result. At least there is a difference. Or am I doing something wrong??? Does anybody know something about this? a<-c(2,4,3,NA) b<-c(4,1,2,3) cor(a, b, method="spearman", use="complete.obs") # -0.9819805 cor.test(a, b,

Hallo, does anybody know if there is an implementation of the Spearman rank correlation in R that gives a correct (or at least 'safe') p-value in the case of ties?? I have browsed the R-help archives but I found nothing. Thanks a lot in advance for any help, Antonino Casile

Hello R gurus, I want to calculate the Spearman rho between two ranked lists. I am getting results with cor.test that differ in comparison to my own spearman function: > my.spearman function(l1, l2) { if(length(l1) != length(l2)) stop("lists must have same length") r1 <- rank(l1) r2 <- rank(l2) dsq <- sapply(r1-r2,function(x) x^2) 1 - ((6 * sum(dsq))

Dear HelpeRs, I have some data: "ice" <- structure(c(0.386, 0.374, 0.393, 0.425, 0.406, 0.344, 0.327, 0.288, 0.269, 0.256, 0.286, 0.298, 0.329, 0.318, 0.381, 0.381, 0.47, 0.443, 0.386, 0.342, 0.319, 0.307, 0.284, 0.326, 0.309, 0.359, 0.376, 0.416, 0.437, 0.548, 41, 56, 63, 68, 69, 65, 61, 47, 32, 24, 28, 26, 32, 40, 55, 63, 72, 72, 67, 60, 44, 40, 32, 27, 28, 33,

Hello, Does anyone know if the cor function, when method = "spearman", returns a correlation coefficient corrected for any ties in the ranks of the data? I have data with quite a few ties and am thinking that I should use a calculation of the coefficient corrected for ties, but before I try and code this calculation myself, I thought I should check whether or not cor() automatically

Hi there! In my case, cor(d[1:20]) makes me a good correlation matrix. Now I'd like to have it one sided, means only the left bottom side to be printed (the others are the same) and I'd like to have * where the p-value is lower than 0.05 and ** lower than 0.01. How can I do this? And another thing: Is there a way to output that table as a latex table? Thanks, Martin -- Ihr

Hi there, I am just starting in R and this might be a very basic question. I applied one on the examples of pairs() to my own data. The examples shows scatter plots on one side of the matrix and the correlation coefficients on the other which works well. I then modified it slightly because I want different colors in the plots. Now i am getting the error of unused arguments, because the color

I'm trying to obtain within-group correlations on a subset of variables. I first selected my variables using the following command: mydata$x<-mydata[c("iq","education","achievement")] I'd like to look at correlations among those variables separately for men and women. My gender variable in mydata is coded 1 (women) and 0 (men). I have successfully used

Dear list, This is more of a stats question than an R question per se. First, I realize there has been a lot of discussion about the problems with estimating P-values from F-ratios for mixed-effects models in lme4. Using mcmcsamp() seems like a great alternative for evaluating the significance of individual coefficients, but not for groups of coefficients as might occur in an experimental design

Dear all, I'm working on a data.frame named en.data, which has n cases and m columns. I generate the correlation matrix of en.data by > cor(en.data) I find that there is no p-value on each correlation in the correlation matrix. I searched in the R-help mail list and found some related posts, but I didn't find direct way to solve the problem. Someone said to use cor.test() or

I have a problem where I need to calculate the corresponding cohort percentile ranks for each of several variables. Essentially, what I need is a function that will calculate the distribution-free percentiles from each variable's data vector, returning a corresponding vector of percentiles: e.g.: percentile.my.data<-/function/(my.data) I tried to make ecdf() perform this task but