similar to: poly(x) workaround when x has missing values

Hi all, Hi, When I want to compute the eigenvalues & eigenvectors of a specific matrix, R crashes (i.e. it stops responding to any input). I've tried it with different versions of R (2.1.1, 2.2.0, 2.2.1) - all with crashing as result. What I did before the crash was: M <- as.matrix(read.table("thematrix",header=T)) eigen(M) If, instead of eigen(M), I use eigen(M,

How can I get the result of, e.g., poly(1:3. degree=2) to give me the unnormalized integer coefficients usually used to explain orthogonal polynomial contrasts, e.g, -1 1 0 -2 1 1 As I understand things, the columns of x^{1:degree} are first centered and then are normalized by 1/sqrt(col sum of squares), but I can't see how to relate this to what is returned by poly(). >

I append below a suggested update for p.adjust(). 1. A new method "yh" for control of FDR is included which is valid for any dependency structure. Reference is Benjamini, Y., and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics 29, 1165-1188. 2. I've re-named the "fdr" method to "bh" but

Can anyone tell me how to delete a row in a matrix? I have searched around and couldn't find a straightforward way to do this. Thanks, any help will be greatly appreciated. Jennifer [[alternative HTML version deleted]]

I've written a program that involves a loop that creates a matrix. I'd like to be able to manipulate that matrix on my R desktop, but after I run the function, that matrix does not appear when I type ls(). How can I make that matrix become an object that I can manipulate? Thanks!

Hi I am having a particular problem with some glm models I am running. I have been adapting code from Bill Venables 'Programmers niche' in RNews Vol 2/2 to fit ca. 1000 glm models to a combination of species 0/1 data (as Y) and related physicochemical data (X), to automate the process of fitting this many models. I have successfully managed to fit all the models and have stored the

Full_Name: Russell Lenth Version: 2.6.2 OS: Windows XP Pro Submission from: (NULL) (128.255.132.36) The poly() function allows a higher-degree polynomial than it should, when raw=FALSE. For example, consider 5 distinct 'x' values, each repeated twice. we can fit a polynomial of degree 8: ===== R> x = rep(1:5, 2) R> y = rnorm(10) R> lm(y ~ poly(x, 8)) Call: lm(formula = y ~

How can I define environments within a function so that they are visible to calls to a sub-function? I have defined an objective function, ProfileErr = function(params,...) which I would like to optimize using standard routines (optim, nlminb,....) but which contains auxiliary variables which need to be updated along with params. No optimization routine in R that I have found has facilities

Hi all I want to assign a constant to a different column for each row eg: m[1,2] <- 0; m[2,3] <- 0; m[3,1] <- 0; m[4,2] <- 0; m[5,1] <- 0; ... etc... i've tried apply/tapply with no luck and also the following coefs <- rtt.abs[,5:8]; coefs.i <- coefs[] == 1; coefs[coefs.i] <- 0; wich results in "matrix subscripts not allowed in replacement"

Dear all, I executed svm calculation using e1071 library with a microarray data (http://www.iu.a.u-tokyo.ac.jp/~kadota/R/data_Singh_RMA_3274.txt). Then, I shuffled the data samples and executed svm calculation again. The results of 2 calculation were different (in SV, coefs and weights). I attached the script below. Could please tell me why this happens? If possible please tell me how to make

Hi, I have fit a series of ols() models, by group, in this manner: l <- ols(y ~ rcs(x, 4)) ... where the series of 'x' values in each group is the same, however knots are not always identical between groups. The result is a table of 'coefs' derived from the ols objects, by group: group Intercept top top' top'' 1 6.864 0.01 2.241 -2.65

Hi ! I would like to understand where do affinity.spline.coefs used in function compute.affinities come from ? library(gcrma) data(affinity.spline.coefs) affinity.spline.coefs X1 X2 X3 X4 X5 X1 -0.55004171 -0.58579091 -0.08870557 -0.47774242 0.23205570 0.58002746 X2 X3 X4 X5 X1 X2

Hi I'm running the latest R on a presumably up to date Linux server. 'Doing something silly I'm sure, but can't see why my saved PLMset objects come out all wrong. To use an example: Setting up an example PLMset (I have the same problem no matter what example I use) > library(affyPLM) > data(Dilution) # affybatch object > Dilution = updateObject(Dilution)

Hi there, I am trying to use linear regression to solve the following equation - y <- c(0.2525, 0.3448, 0.2358, 0.3696, 0.2708, 0.1667, 0.2941, 0.2333, 0.1500, 0.3077, 0.3462, 0.1667, 0.2500, 0.3214, 0.1364) x2 <- c(0.368, 0.537, 0.379, 0.472, 0.401, 0.361, 0.644, 0.444, 0.440, 0.676, 0.679, 0.622, 0.450, 0.379, 0.620) x1 <- 1-x2 # equation lmFit <- lm(y ~ x1 + x2) lmFit Call:

Dear List, A posting to R-Help exposed this problem with the print method for objects of class Arima: > set.seed(1) > x <- arima.sim(n = 100, list(ar = 0.8897, ma = -0.2279)) > mod <- arima(x, order = c(1,0,1)) > coefs <- coef(mod) > mod2 <- arima(x, order = c(1,0,1), fixed = coefs) > mod2 Call: arima(x = x, order = c(1, 0, 1), fixed = coefs) Coefficients: Error

Hi: I would like to write a function that takes as its input a formula and outputs normalized coefficients ( coef(x)*sdv(x)/sdv(y) ). now, a formula is an object, and I cannot see how to extract the variables for obtaining sdv's. the intent is to write something like my.print.lm( formula ) { model <- lm(formula); coefs <- (t(summary.lm(model)))[1,]; tvals <-

Marc: 1. I am aware of the need to explicitly name arguments after ... -- see the R Language definition where this can be inferred from the argument matching rules. 2. I am aware of the stated exception for poly(). However: > x1 <- runif(20) > x2 <- runif(20) > mx <- cbind(x1,x2) > poly(mx,2) Error in poly(dots[[i]], degree, raw = raw, simple = raw) : 'degree'

Full_Name: Renaud Lancelot Version: Version 2.3.0 (2006-04-24) OS: MS Windows XP Pro SP2 Submission from: (NULL) (82.239.219.108) I think there is a bug in predict.lme, when a polynomial generated by poly() is used as an explanatory variable, and a new data.frame is used for predictions. I guess this is related to * not * using, for predictions, the coefs used in constructing the orthogonal

Hello, list why not add the smart proposal by Greg Snow as a built-in function in {stats}, just changing the "x234" and "newc" lines to allow for more distributions to be generated ? Or do I miss an already existing function to do that ? Regards. Olivier # slight modification of the original code by Greg Snow [mailto:Greg.Snow at intermountainmail.org] # on April 04, 2007

Hi R-helpers. #I start with the reproducible example: firm<-c(rep(1,10),rep(2,10),rep(3,10),rep(4,10),rep(5,10)) year<-c(rep(1998:2007,5)) industry<-c(rep(1,20),rep(5,10),rep(7,10),rep(9,10)) X1<-rnorm(50) X2<-rnorm(50,mean=0.5,sd=0.1) Y<-rnorm(50,mean=0,sd=0.5) data<-data.frame(firm, industry,year,X1,X2,Y) data #I need to calculate for all the industries the following