similar to: Accessing matrix elements within a list

Dear list members. I have problems with the usage of cv.glm from the boot package. Here are some parts of the script I wanted to use: data <- read.table("selected_2D.csv", header=TRUE, sep=",") ? glm.fitted <- glm("ydata$ y ~ 1 + density + vsurf_ID6 + vsurf_S ", data=data) error <- cv.glm(data=data, glm.fitted, K=6) ydata$y is a separate data set, where

Hi, how are you? I am trying to replicate the binary data f(2) function in the attached document by starting with the simple example found below: observed <- matrix(c(0, 1, 0, 0, 1, 1, 1, 0, 0),3,3,byrow=TRUE) data <- matrix(c(1, 1, 0, 0, 1, 0, 0, 0, 1),3,3,byrow=TRUE) f2 = sum(probability of the matrix element where the matrix element is present in both the observed and the

Hi, I am a new user of R using R 2.8.1 in windows 2003. I have a csv file with single column which contain the 30,000 students names. There were typo errors while entering this student names. The actual list of names is < 1000. However we dont have that list for keyword search. I am interested in grouping/cluster these names as those which are similar letter to letter. Are there any

I am having a problem understanding the lda package. I have a dataset here: [,1] [,2] [,3] [1,] 2.95 6.63 0 [2,] 2.53 7.79 0 [3,] 3.57 5.65 0 [4,] 3.16 5.47 0 [5,] 2.58 4.46 1 [6,] 2.16 6.22 1 [7,] 3.27 3.52 1 If I do the following; "names(d)<-c("y","x1","x2") d$x1 = d$x1 * 100 d$x2 = d$x2 * 100 g<-lda( y ~ x1 + x2, data=d) v2

Hi folks, I have a question about how to efficiently produce random numbers from Beta and Binomial distributions. For Beta distribution, suppose we have two shape vectors shape1 and shape2. I hope to generate a 10000 x 2 matrix X whose i th rwo is a sample from reta(2,shape1[i]mshape2[i]). Of course this can be done via loops: for(i in 1:10000) { X[i,]=rbeta(2,shape1[i],shape2[i]) } However,

Thanks David, Yes, I am talking about the MASS package.Thank you for pointing out that these scale the same. My question is, how do I get from the V1 data: V1 1 164.4283 2 166.2492 3 170.5232 4 156.5622 5 127.7540 6 136.7704 7 136.3436 to the other set of data: + 1 -2.3769280 + 2 -2.7049437 + 3 -3.4748309 + 4 -0.9599825 + 5 4.2293774 + 6 2.6052193 + 7 2.6820884 On Mon, Sep 28, 2009

Hey, I am confused about how to generate the sample data from a mixture of Multivariate Gaussian ditribution. For example, there are 2 component Gaussian with prior probability of 0.4 and 0.6, the means and variances are u1=[1 1]', Cov1=[1 0;0 1] and u2=[-1 -1]', Cov2=[1 0;0 1] repectively. So how can I generate a sample of 500 data from the above mixture distribution? Thanks. Fred

Hi: How can I extract the dimension-names of a pre-defined element in a multidimensional array in R ? A toy example is provided below: I have a 4-dimensional array with each dimension having certain length. In the below example, "mydatastructure" explains the structure of my data. mydatastructure = array(0, dim=c(length(b),length(z),length(x),length(d)), dimnames=list(b,z,x,d)) where,

Hi Now a more theoretical question. I have two correlation matrices - one of a set of variables under a particular condition, the other of the same set of variables under a different condition. Is there a statistical test I can use to see if these correlation matrices are "different"? Thanks Mick

Hi,   I'm a student in China, and I never used R before.   I'm now wondering how to simulate a sample of Markov chain of ,say 500 entries with a certain transition matrix.    Thanks a lot. ___________________________________________________________ 好玩贺卡等你发，邮箱贺卡全新上线！ http://card.mail.cn.yahoo.com/ [[alternative HTML version deleted]]

Hi, I am very happy. My problems are solved without one little thing: (Iske <- matrix(c(1, 1, 1, 2, 2, 2, 1, 1, 1, 5, 1, 2, 2, 2, 1, 1, 1, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 5, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 2), ncol = 5)) #My Matrix Iske<- Iske+33 #I want see the letters (Iske.char<-apply(Iske, 1, function(x) rawToChar(as.raw(x)))) #Numbers to

Javier, Creo que aquí aplica la ley de Linus que dice: "Dado un número suficientemente elevado de ojos, todos los errores se convierten en obvios". La persona que revisa y encuentra un error no necesariamente tiene que ser la misma que la que lo escribe. Una motivación muy importante al compartir un código es la de recibir los beneficios del control de calidad por parte de tus pares.

(sorry if this is a duplicate-problems with posting at my end) ---- Hello all, I need some help with a nice R-idiomatic and efficient solution to a small problem. Essentially, I am trying to eliminate randomly half of the entries in a vector that contains index values into some other vectors. More details: I am working with two strings/vectors of 0s and 1s. These will contain about 200

Dear all, I am having trouble creating summary tables using aggregate function. given the following table: Var1 Var2 Var3 dummy S1 T1 I 1 S1 T1 I 1 S1 T1 D 1 S1 T1 D 1 S1 T2 I 1 S1 T2 I 1 S1 T2 D 1 S1 T2 D 1 S2

Dear R'ers, When I used S-plus i wrote a small program for a Mantel-Haenszel test for trend (I think it worked). Unfortunately I can't get it working in R. It appears as if my use of 'el' is the problem but I can't sort it out. Error in apply(array, c(, 2, 3), function(el) el * 1:s) : argument is missing, with no default Further down in the program I use 'el'

Hi, I'm trying to get maximum likelihood estimates of \alpha, \beta_0 and \beta_1, this can be achieved by solving the following three equations: n / \alpha + \sum\limits_{i=1}^{n} ln(\psihat(i)) - \sum\limits_{i=1}^{n} ( ln(x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} 1/(psihat(i)) - (\alpha+1) \sum\limits_{i=1}^{n} ( 1 / (x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} (

Hello everyone, I am trying to use the following code: dat<-read.table(file="C:\\Documents.....txt") class2<-read.table(file="C:\\Documents.....txt") class1<-read.table(file="C:\\Documents .....txt") dat<-as.data.frame(dat) dimnames(dat)[[1]]<-paste("g",c(1:nrow(dat)),sep="")

I have some data 'myData' in wide form (attached at the end), and would like to convert it to long form. I wish to have five variables in the result: 1) Subj: factor 2) Group: between-subjects factor (2 levels: s / w) 3) Reference: within-subject factor (2 levels: Me / She) 4) F: within-subject factor (2 levels: F1 / F2) 5) J: within-subject factor (2 levels: J1 / J2) As this is the

On Tue, 12 Oct 2004 14:01:21 -0500 (CDT) Chris Lattner <sabre at nondot.org> wrote: > On Tue, 12 Oct 2004, Morten Ofstad wrote: > > > This is my first post on this mailing list, so bear with me... My name > > is Morten Ofstad and I work for Hue AS (www.hue.no), a company that > > makes 3D Visualization software. We are looking into using LLVM for JIT > >

Hello there, I wish to solve the following nonlinear System of equations: + u1 - Vmax11*S1/(S1 + Km11 *(1 + S2/Km21)) - Vmax12*S1/( S1 + Km12 *(1+S2/Km22)) == 0 + u2 - Vmax22*S2/(S2 + Km22 *(1 + S1/Km12)) - Vmax21*S2/( S2 + Km21 *(1+S1/Km11)) == 0 + Vmax11*S1/(S1 + Km11 *(1 + S2/Km21)) + Vmax12*S1/( S1 + Km12 *(1+S2/Km22)) - d1*P1 == 0 + Vmax22*S2/(S2 + Km22 *(1 + S1/Km12)) + Vmax21*S2/( S2 +