similar to: Test of significance in estimation of correlation coefficients

I want to read data from a number of files into R. Reading individual files one by one requires writing enormous amount of code that will look something like the following. **************** maptools:::dbf.read("wb-01vc.dbf")->dist1 maptools:::dbf.read("wb-02vc.dbf")->dist2 maptools:::dbf.read("wb-03vc.dbf")->dist3

I want to add RMySQL and RODBC packages to my R installation on redhat linux box. The command install.packages gives following output. What could be wrong? ******************** install.packages(RMySQL) trying URL `http://cran.r-project.org/src/contrib/PACKAGES' Content type `text/plain; charset=iso-8859-1' length 202145 bytes opened URL .......... .......... .......... ..........

If you multiply a matrix by its inverse you should get an identity matrix. In R, you get an answer that is accurate up to about 16 decimal points? Why can't one get a perfect answer? See for example: c(5,3)->x1 c(3,2)->x2 cbind(x1,x2)->x solve(x)->y x%*%y Vikas Rawal ============================================== This Mail was Scanned for Virus and found Virus free

I want to use grid to modify some boxplots made using ggplot. I would really appreciate if somebody could guide me to a resource on how to use grid to modify such graphics. I guess the basic approach will be similar to using grid to modify lattice graphics. To that extent something that explains use of grid to modify lattice graphics may also be useful. I have gone through vignettes in the grid

Hi, I'm trying to match two data frames in order to replace the boundary values in a dataframe.1 with values in dataframe.2 ONLY where the pair id1 id2 matches between the two data frames. Eg. > dataframe.1 ... id1 id2 boundary 3307 1095 1108 438.691 3308 1095 1109 438.691 3309 1095 1121 438.691 3310 1096 1109 438.691 ... 3345 1108 1120 438.691 3346 1108 1121 438.691 3347 1108

I need to make weighted boxplots. I found that ggplot makes them. I would however like to label them with the boxplot statistics (the median, q1 and q3). In the boxplot function in r-base, I could output boxplot statistics and then write a text on the plot to place the labels. How would one do it with ggplot? Vikas

This issue has been discussed on this list before but the solutions offerred are not satisfactory. So I thought I shall raise it again. I want to merge two datasets which have three common variables. These variables DO NOT have the same names in both the files. In addition, there are two variables with same name which do not necessarily have exactly same data. That is, there could be some

I have been struggling to get the legends in ggplot2 right or do away with them altogether (on which I have already sent a post). In the following code, the labels argument in the scale_colour_gradient2 does not give me the desired labels in the legend. Could someone explain? qplot(Year,CDR,data=b3,colour=Phase,geom=c("point","line"),legend.position="bottom")->p

I am having a peculiar problem with installation of packages. I am trying to install the package "maptools". This is what I get. install.packages("maptools",depend=T) Warning in install.packages("maptools", depend = T) : argument 'lib' is missing: using /usr/local/lib/R/site-library Warning: unable to access index for repository

Hello all, I've run a Spearman's Rank test to discern relationships between landscape characteristics and a specific aspect of river behaviour. I've executed a correlation matrix between the one dependent variable and all of the predictors, which gives me a nice output of Spearman's Rho values. However, I also need to somehow find the "p" value, to assess the strength

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

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))

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 R users, Does the results below make any sense? Can the the interval of the correlation coefficient be between *-1.0185* and -0.8265 at 95% confidence level? Liviu > library(boot) > data(mtcars) > with(mtcars, cor.test(mpg, wt, met="spearman")) Spearman's rank correlation rho data: mpg and wt S = 10292, p-value = 1.488e-11 alternative hypothesis: true rho is not

For boxplot(), is it possible to pass in a parameter to change the default way that the 1st and 3rd quartiles are computed? (specifically, I'd like to use type 6 described in the quantile function). Also, what are the options for how outliers are computed, and how can one change them? Thank you [[alternative HTML version deleted]]

Hi, I am not really familiar with Correlation foundations, although I read a lot. So maybe if someone kindly help me to interpret the following results. I had the following R commands: correlation <-cor( vector_CitationProximity , vector_Impact, method = "spearman", use="na.or.complete") cor_test<-cor.test(vector_CitationProximity, vector_Impact,

I have this assignment to do and after ten hours of constant trying my eyes ache and i give up.. all i'm able to get is this plot please help me these are the commands i have used till now read.table(file.choose(), sep=";", header=T) read.table(file.choose(), sep=";", header=T)->areas melt(areas,id=c("Year","State"),m=c("Rice"))->

Dear all, I am trying to compute a matrix of Pearson's `r' or Spearman's `rho' rank correlation coefficients using rcorr (Hmisc) the following way: > mx<-rcorr(x, type="spearman")[1] but then ... > is.matrix(mx) [1] FALSE Even if I use as.matrix the result is not better. What can I do? Thank you all Luca

A search of the archives did not reveal an answer: For basic tests of association, where one has no a priori knowledge of the form of the relation or of the distributions of the variables, rank correlation seems like a good start. Why is cor.test() with Kendall and Spearman options relegated to the ctest package, rather than in the base package? Does this suggest that the developers consider

Hi, I have a dataset with four categories of data, the number of samples are not the same in each category. I want to find the Spearaman's Rho. Let me give an example. x=(14.22770439,26.49420624,46.7277932,19.02550707,23.37379361,16.97789862,19.77100085,23.11270162,13.72929843,33.54430621,14.4756979,70.15811106,11.22789833,NA,NA,NA)