similar to: Viewing specific data from a dataframe

Hi, If any one has time I need some help understanding the P-values given in the lmer output. Using AIC for model selection I find my minimal model is FOLLOW~MOVERSTATUS+DISTANCE however it appears DISTANCE is not significant at 95% confidence, see output quoted below. However, removing DISTANCE gives a higher AIC=433.5, therefore I will keep it in, but am confused as to what is adds to the

Dear R-users I have a dataset of 243 lines with replicate information for 20 different individuals (ID). I would like to randomly sample this dataset 100 times with a selection of unique IDs in each sample. First to create a random sample I have; cc<-read.table(blah.blah.blah) names(cc) [1] "CALL" "CONTEXT" "ORDER" "ID"

Hi, I was trying to evaluate the pearson correlation and the p-values for an nxm matrix, where each row represents a vector. One way to do it would be to iterate through each row, and find its correlation value( and the p-value) with respect to the other rows. Is there some function by which I can use the matrix as input? Ideally, the output would be an nxn matrix, containing the p-values

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 All. I'd like to generate a sample of n observations from a k dimensional multivariate normal distribution with a random correlation matrix. My solution: The lower (or upper) triangle of the correlation matrix has n.tri=(d/2)(d+1)-d entries. Take a uniform sample of n.tri possible correlations (runi(n.tr,-.99,.99) Populate a triangle of the matrix with the sampled correlations Mirror the

Hi, I'm trying to obtain confidence intervals for auto and cross correlation estimates. I've adapted code made available by Stock and Watson that uses the Bartlett Kernel and the delta method. In R it runs really, really slow because of the loops it uses and I have 9 series that I'd like to examine (81 total combinations). It was easy enough to replace one of the while loops with a

Hi I'm trying to write a generic script for processing some data which finishes off with some plots. Given Im never sure how many columns will be in my dataframe I wanted to using the following plot(spectra.wavelength, cormat, type = "l", ylim=c(-1,1), xlab="Wavelength (nm)", ylab="Correlation") however even if I specify as type="l" it appears plot

Hello, I'm attempting to create a data frame with correlations between every pair of variables in a data frame, so that I can then sort by the value of the correlation coefficient and see which pairs of variables are most strongly correlated. The sm2vec function in the corpcor library works very nicely as shown here: library(Hmisc) library(corpcor) # Create example data x1 = runif(50) x2 =

Dear R community: I want to simulate a regression matrix which is generated from an orthonormal matrix X of dimension 30*10 with different between-column pairwise correlation coefficients generated from uniform distribution U(-1,1). Thanks in advance! Rui [[alternative HTML version deleted]]

Dear R users, I am trying to print heatmaps in a loop (with a pause). Idea is to visualize changing correlations over time and for testing I wrote this simple (reproducible) code below. My problem is that levelplot() does not produce any output when I run the code (though heatmap does). Ideally I would like to use levelplot() as it produces a neat index on the side indicating the color and the

OK, now i've to start to move the big part of my users from my old NT-like domains to my new AD domain. I've setup roaming profile in the new domain following the wiki (https://wiki.samba.org/index.php/Roaming_Windows_User_Profiles, 'using windows ACL') and for new profiles works like a charm. But i've tried to move/copy old profile to the new domain, and seems work, with

Hi, Sorry for my newbies questions... but here it is... My goal is to have a "shading language", it is very similar to the "C" language but with special tokens. I have a parser and a lexer done with Lex/bison, once I have lexed/parsed my "shading language" I must create 3 new "source code" in OpenCL. It mean that by example for the following : shader

R-Experts, Here below my R code working without error message but I don't get the results I am expecting. Here is the result I get: [1] "All values of t are equal to 0.28611928397257 \n Cannot calculate confidence intervals" NULL If someone knows how to solve my problem, really appreciate. Best, S ######################################################### # Difference in Spearman

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]]

Hi. I am currently trying to run some Spearman correlations, and have encountered two issues. 1) When using cor.test() with a variable that includes ties, I get the "Cannot compute exact p-values with ties" error. I have read that this function now uses an asymptotic formula that allows for ties, so do not understand why I am getting this error. (I am running version 2.4.0.) I

Dear all, Suppose my data frame is as follows: id price distance 1 2 4 1 3 5 ... 2 4 8 2 5 9 ... n 3 7 n 8 9 I would like to calculate the rank-order correlation between price and distance for each id. cor(price,distance,method = "spearman") calculate a correlation for all. Then I tried to use apply(data,list='id',cor(price , distance , method =

I believe the problem is here: cor1 <- cor(x1, y1, method="spearman") cor2 <- cor(x2, y2, method="spearman") The x's and y's are not looked for in data (i.e. NSE) but in the environment where the function was defined, which is standard evaluation. Change the above to: cor1 <- with(d, cor(x1, y1, method="spearman")) cor2 <- with(d, cor(x2, y2,

Full_Name: Ian Wilson Version: R-1.8.0 OS: Windows (but own compilation) Submission from: (NULL) (139.133.7.38) the p-value is incorrect for cor.test using method "spearman" in R-1.8.0. This was not the case in R-1.7.1. Version R-1.8.0 on Windows > cor.test(rnorm(50),rnorm(50),method="spearman") Spearman's rank correlation rho data: rnorm(50) and rnorm(50) S

I have a data set where I want the correlations between 2 variables conditional on a students grade level. This code works just fine. by(tmp[,c('mtsc07', 'DCBASmathscoreSPRING')], tmp$Grade, cor, use='complete', method='pearson') However, this generates an error by(tmp[,c('mtsc07', 'DCBASmathscoreSPRING')], tmp$Grade, cor, use='complete',

Hi, How can I make the cor(x, y, method="spearman") call to produce an error when the input to it (x, y) produces an error? Here is a simple example: > a <- c(0, 1, 2) > b <- c(100, 2, 4) ## error: > log(a) [1] -Inf 0.0000000 0.6931472 ## error, as expected: > cor(log(a), log(b), method="pearson") [1] NaN ## not an error any more (not expected): >