similar to: backward prediction in multivariate multiple regression

Dear r-helpers, I have a trouble with a package downloaded from sourceforge.net (namely the package 'kopls'). I installed it from the local zip file with the expected result > utils:::menuInstallLocal() package ?kopls? successfully unpacked and MD5 sums checked but when I tried to load it I obtained the following error message > require(kopls) Loading required package: kopls Failed

Dear r-helpers, I have a query regarding use of contrasts in MANOVA. summary(manova(model)) gives me only result of test for overall difference. Would you be so kind and give me a hint how to get the same test statistics (e.g.Pillai's) and P values for the predefined contrasts? Best regards Ondrej Mikula -- Institute of Animal Physiology and Genetics Academy of Sciences of the Czech

Dear R-helpers, I have a number of point configurations representing skull shapes, but some of them contain superfluous points. I want to write a loop in which each configuration is plotted and I am asked to write the numbers of points that are superfluous. However, I don't know how to introduce this interactive element. Would you give me an advice? Best regards Ond?ej Mikula -- Ond?ej

Dear R-helpers, could anybody explain me briefly what is the difference between eigenvectors returned by 'eigen' and 'svd' functions and how they are related? Thanks in advance Ondrej Mikula

Dear r-helpers, I have a graph created using the library 'spatgraphs'. library(spatstat) library(spatgraphs) xy <- rbind(c(28.39, -16.27), c(30.62, -20.13), c(32.25, -28.7), c(22.43, -27.22), c(27.5, -21.17), c(31.22, -24.52), c(17.93, -26.92), c(18.72, -17.95), c(24.15, -17.82), c(29.23, -22.85)) ow <- owin(xrange=range(xy[,1]), yrange=range(xy[,2])) pp <-

Dear R-helpers, I need to fit natural cubic spline with specified number of knots. I expected 'splines' package will be helpful, but I am confused by its help. Is more detailed documentation available for it or could you recommend another R function? Best regards Ondrej Mikula

Dear list, following the vignette of the package 'spacemmakeR' I have built a gabriel graph of ten randomly placed points: library(spdep) set.seed(3) xyir<-matrix(runif(20),10,2) nbgab<-graph2nb(gabrielneigh(xyir), sym=TRUE) plot(nbgab,xyir,points=FALSE) points(xyir,pch=20,cex=2,col=c(2,4,4,4,2,2,4,2,4,4)) title(main="Gabriel Graph") Now, I need to determine for any

Dear list, is there a way how to add information to internal nodes (branching points) in dendrogram created via plot.agnes function (package cluster)? I wish to place colored circles on the nodes, but I don't know how to proceed... I'll be grateful for any suggestion Ond?ej -- Ond?ej Mikula Institute of Animal Physiology and Genetics Academy of Sciences of the Czech Republic Veveri 97,

Dear R-helpers, I need to quantify dissimilarity of two minimum spanning trees, specifically dissimilarity of their topologies. (They connect the same objects but they are calculated from different sets of variables.) Are you aware of any R-function doing this? Best regards Ondrej Mikula

Hello, I look for a simple command computing multivariate group means and returning an object of class "matrix" rather than "list". Does any such function exist in standard packages? I'm beginning with R, so I'm sorry if the solution is trivial. Ondra Mikula

Dear R-devel, It seems that lsfit incorrectly reports coefficients when the input matrix 'x' is rank-deficient, see the example below: ## here values of 'b' and 'c' are incorrectly swapped > x <- cbind(a=rnorm(100), b=0, c=rnorm(100)); y <- rnorm(100); lsfit(x, y)$coef Intercept a b c -0.0227787 0.1042860 -0.1729261 0.0000000 Warning

Hello experts, as newcomer in pca, i have a question, concerning the princomp algorithm. With a dataset "r" containing 18 "input" parameters and 1 "output" parameter r[19], i got with the following fit ls <- lsfit(r[1:18],r[19]); lsdiag <- ls.diag(ls); lsdiag$std.dev a prediction error of: [1] 8.879561 what is quite reasonable. If i take only two

Dear R-listers, I want to reperfrom a poisson distribution test that presented in a recent-published biological research paper (Plant Physiology 2008, vol 148, pp. 1189-1200). That test is about the occurrence number of a kind of gene in separate chromosomes. For instance: The observed gene number in chromosome A is 36. The expected gene number in chromosome A is 30. Then, the authors got a

This is not a solution but your lsfit attempt #Error in lsfit(A, b) : only 3 cases, but 4 variables lsfit(A,b) gave that error because lsfit adds a column of 1 to its first argument unless you use intercept=FALSE. Then it will give you an answer (but I think it converts your sparse matrix into a dense one before doing any linear algebra). Bill Dunlap TIBCO Software wdunlap tibco.com On

I'm trying to extract the points above and below a particular lsfit. I can only get the residuals from the original fit though. x = runif(100, 0, 10) plot(x) abline(lsfit(1:100, test)) abline(lsfit(1:100, test + sd(test))) #I want the points above THIS line. Is there a way to use the coefficients from the fit to do this? Thanks for any help.

Hi, in R-3.0 I get the following error when calling lsfit with more observations than variables, which seems to come from an error in the formatting of the error message (note that this was not happening in 2.15.3): > nobs <- 5; nvar <- 6; lsfit(matrix(runif(nobs*nvar), ncol=nvar), runif(nobs), intercept=FALSE) Error in sprintf(ngettext(nry, "%d response", "%d

To the purpose of fitting a 2nd order polynomial (a + b*x + c*x^2) to the chunk of signal falling in a 17 consecutive samples window I wrote the following very crude script. Since I have no previous experience of using Least Square Fit with R I would appreciate your supervision and suggestion. I guess the returned coefficients of the oolynomial are: a = -1.3191398 b = 0.1233055 c = 0.9297401

I'm using R 2.2.0 on Windows. Doing some conversions of help files. Internal comments indicate that the Sd2Rd conversion is "Converted by Sd2Rd version 1.21." I'm converting .d -> .Rd .sgml -> .Rd using Sd2Rd, then checking by using Rdconv to convert .Rd back to .d or .sgml. Here are errors in some of the conversions. The most significant errors are in .Rd to .sgml.

ls.print produces error that I don't seem to be able to trace. Output of the commands as follows: (hyeung is a 24x2 matrix of data) ------------------------------------------------- > summary(hyeung) x.1 x.2 Min. : 28.0 Min. : 10.0 1st Qu.: 72.0 1st Qu.: 87.5 Median : 86.5 Median : 92.5 Mean : 81.0 Mean : 82.5 3rd Qu.: 97.0 3rd Qu.:100.0 Max.

ls.print produces error that I don't seem to be able to trace. Output of the commands as follows: (hyeung is a 24x2 matrix of data) ------------------------------------------------- > summary(hyeung) x.1 x.2 Min. : 28.0 Min. : 10.0 1st Qu.: 72.0 1st Qu.: 87.5 Median : 86.5 Median : 92.5 Mean : 81.0 Mean : 82.5 3rd Qu.: 97.0 3rd Qu.:100.0 Max.