similar to: eof and svd calculus with NA's

I am trying to know exactly the formulas used to calculate aic for glm models. In glm.fit, the calculus of aic is: aic.model <- aic(y, n,mu, weights, dev) + 2 * fit$rank where 2 * fit$rank is (may be am i wrong?) twice the numbers of parameters p and aic(y, n, mu, weights, dev) refers to the function defined in the family function (which is for Gamma family, for instance) aic

Hey, All In principal component analysis (PCA), we want to know how many percentage the first principal component explain the total variances among the data. Assume the data matrix X is zero-meaned, and I used the following procedures: C = covriance(X) %% calculate the covariance matrix; [EVector,EValues]=eig(C) %% L = diag(EValues) %%L is a column vector with eigenvalues as the elements percent

Hi, How the eigenvectors output by the eigen() function are ordered. The first column corresponds to the largest eigenvalue? or is the last column as in Octave? I'm performing a spatial-temporal analysis of some climatic variables so my matrices are MxN (locations*time)and I'm looking for the leading EOF's. As I have understand the eigenvectors columns represent those EOF's

Hi, I have a bright, diligent second-year graduate student who wants to learn statistics and R and will, in effect, be taking a tutorial from me on these subjects. (If you've seen some of my questions on this list, please don't laugh.) As an undergrad he majored in philosophy, so this will be his first foray into computer programming and statistics. I'm thinking of having him use

Dear All, using R is noticed that I would benefit by going back to the books and "refresh" my linear algebra and calculus. Has anybody suggestions on which books are better as reference on the two these two topics? I was thinking to get Lang's "A First Course in Calculus", but I I have no idea what could be a good one for linear algebra. Something detailed and clear

An amusing afterthought : What is a rival software (ahem!) was planting this, hoping for a divide between S and R communities.or at the very minimum hoping for some amusement. an assumption or even a pretense of stealing credit is one of the easiest ways of sparking intellectual discord Most users of softwares don't really care about who gets credit ( Who wrote Windows Vista ,or Mac OS or

Dear R-list members, Just wondering if there is any way to compute the duplication matrix in R. I tried to search for it but only found functions "xpnd" and "vech". Basically for a symmetric n by n matrix A, the duplication matrix D_n is a matrix of dimension n^2 by n(n+1)/2 such that D_n vech(A)= c(A), where c(A) just vectorizes A. The duplication matrix is defined on page

Dear R'Helpers and Colleagues, I have looked in the documentation, asked to some colleagues in the lab, but was unable to solve the following difficulty. I am running R on a iMac G5 with OS 10.4. The file below (73 rows x 144 col) shows the values of a climatic index on the globe with a grid of 2,5 ? x 2,5 ? (NA = no value): ? With image() and map() and running the following

Christian Sayer wrote: > Hi, > my target architecture has a kind of "16bit addressing mode", i.e. one address does not address 8 bit but a 16bit chunk. Consequently, every constant used to calculate effective addresses must be divided by two. > So far this is not such a problem for stack objects since FrameIndexes, function arguments etc. have a lot of custom lowering code where

> > At first I thought this could be handled when lowering > loads and stores, but I realize that I can only catch the > targeted addresses of loads/stores here - however address > calculation nodes may occur anywhere in a DAG. > > > > So my first impulse would be to adjust the constants when > the GEP instructions are transformed to ADDs. Afaics his > would mean

Hello, I use extensively m[i0:i1,j0:j1] = ... to update matrices parts. This writing is very convenient, but, since I'm doing this calculus many many times, I would like to know if there is a way (a hope ?) to do the same operation faster ? Thanks for any advice or pointer. Vincent

Hello, I am using SVM under e1071 package for nu-regression with 18 parameters. The variables are ordered factors, factors, date or numeric datatypes. I use the linear kernel. It gives the following error that I cannot solve. I tryed debug, browser and all that stuff, but no way. The error is: Error in get(ctr, mode = "function", envir = parent.frame())(levels(x), :

On a real data set, running the lme function, I get parameters estimation and a log-likelihood value. Nevertheless, the variance-covariance matrix in this case had a determinant close to zero. So, I could not calculate the log-likelihood myself with the classical expression. What is the calculus made in lme? Thanks for all suggestions. Tessa P-J (student)

Hi guys again, it seems I haven't been doing the maximum likelihood estimation correctly. I quote below, can someone explain to me please what does it mean that the 2nd and 3rd derivatives of the function equals zero and how to compute that in R. "We have our initial estimated, subjective parameters for the gamma mixture and we have our likelihood that is the mixture of negative

Hi, my target architecture has a kind of "16bit addressing mode", i.e. one address does not address 8 bit but a 16bit chunk. Consequently, every constant used to calculate effective addresses must be divided by two. So far this is not such a problem for stack objects since FrameIndexes, function arguments etc. have a lot of custom lowering code where this can be done. But when it comes

Hi: I'm working with multinomial models with library nnet, and I'm trying to get the explained deviance (pseudo R^2) of my models. I am assuming that: pseudo R^2= 1 - dev(model) / dev (null) where dev(model) is the deviance for the fitted model and dev(null) is the deviance for the null model (with the intercept only). library(nnet) full.model<- multinom(cbind(factor1,

Hey, I am Swapnil Raj I am student in Trinity College Dublin and I am interested in working on LLVM. I am really interested in two projects listed, the first one is the extending the clang AST with template information and the second is finding smart null pointer dereferences. I am passionate about compilers and interpreters, I have written a few small language based on lambda calculus. I am

Per the discussion about the problems with prcomp() when n << p, which boils down to a problem with svd() when n << p, here is a patch to prcomp() which substitutes La.svd() instead of svd(). -Greg (This is really a feature enhancement, but submitted to R-bugs to make sure it doesn't get lost. ) *** R-1.6.0/src/library/mva/R/prcomp.R Mon Aug 13 17:41:50 2001 ---

(I tried to send this earlier, but it doesnt seem to have come through, due to problems on my system) Hola: Both cannot be correct: > m <- matrix(1:4, 2) > svd(m) $d [1] 5.4649857 0.3659662 $u [,1] [,2] [1,] -0.5760484 -0.8174156 [2,] -0.8174156 0.5760484 $v [,1] [,2] [1,] -0.4045536 0.9145143 [2,] -0.9145143 -0.4045536 > La.svd(m) $d [1]

Hello I just brought my machine back up from a very nasty crash. Not sure what caused it yet but whatever happeed cause the journal to be foobared. fsck dropped the journal and then proceeded to check the fs. I have a directory called websites which fsck unlinked. however when i look in debugfs i see this.. 622669 40755 0 0 4096 13-Jan-2002 22:39 home3 2133571369 --- error ---