similar to: using so-library involving Taucs

I recently uploaded a developmental version of the Matrix package, Matrix_0.6-1.tar.gz, to CRAN where it is in the src/contrib/1.9.0/Other directory. It requires some of the packages that will appear in R-1.9.0. This version marks a major redesign of the Matrix package to use S4 classes and methods and to incorporate sparse matrix manipulations using routines from TAUCS

Dear all, For a project I need to calculate the conditional AIC of a mixed effects model. Luckily, I found a reference in the R help forum for a function to be used: CAIC <- function(model) { sigma <- attr(VarCorr(model), 'sc') observed <- attr(model, 'y') predicted <- fitted(model) cond.loglik <- sum(dnorm(observed,

Full_Name: W.B.Kloke Version: 1.9.1 OS: FreeBSD-5.2.1 Submission from: (NULL) (195.253.16.182) Subject line says it. I had problems installing lme4. 1. The dependency on package Matrix was not resolved (I am not sure that this is really a bug; but it is annoying, anyway). 2. Installing Matrix failed with a message saying something like "no rule for %_D.o" after compiling a lot of

Dear all, I am trying to read in big amounts of data with scan. It's only one variable, numeric values, separated by tabs,.. and it's many of them. So I was thinking that I could use the skip option and read in 100000 values at a time - but skip doesn't work, probably because I don't have line breaks in the txt file. So any value specified for skip makes the scan function jump to

Dear all, I am attempting to model some one-dimensional data using a mixture model of non-central Student's t distributions. However, I haven't been able to find any R package that provides this functionality. Could there be a way to "manipulate" the EM algorithms from the mixdist or mixtools package to fit the model, or do you have any other suggestions? If anyone could help

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Hello all, I'm using R2.7.0 (on Windows 2000) and I'm trying do run a robust regression on following model structure: model = "Y ~ x1*x2 / (x3 + x4 + x5 +x6)" where x1 and x2 are both factors (either 1 or 0) and x3.....x6 are numeric. The error code I get when running rlm(as.formula(model), data=daymean) is: error in rlm.default(x, y, weights, method = method, wt.method =

Dear list members, I need to translate 3 lines of matlab code to R (a loop, to be specific), and I don't know what would be the results in matlab or how to do it in R-- I don't realise if they are doing to the col, vector or what. if the results are a vector or a value or a matrix :-( Anyone with matlab, can run it and give me the result? Any ideias what am I doing wrong? The code is

Dear All, Can anyone please tell me where I can obtain uncompiled binary instalation files for R version 1.8. (i.e. rw1080.exe)? I can only find the uncompiled source code on CRAN today. Thank you, Mary Wisz msw@dmu.dk [[alternative HTML version deleted]]

Hello, I have a problem with the checking and building of R packages that contain C or FORTRAN code. I have implemented R 2.3.1 (2006-06-01) and the corresponding toolset under Windows XP and Windows 2000. The MinGW compiler components are (exactly as recommended for R 2.3.1) gcc-core-3.4.5-20051220-1.tar.gz gcc-g++-3.4.5-20051220-1.tar.gz gcc-g77-3.4.5-20051220-1.tar.gz

Hi all, I have been searching all sorts of documentation, reference cards, cheat sheets but can't find why R's crossprod(A, B) which is identical to A%*%B does not produce the same as Matlabs cross(A, B) Supposedly both calculate the cross product, and say so, or where do I go wrong? R is only doing sums in the crossprod however, as indicated by (z <- crossprod(1:4)) # = sum(1 +

Dear R-users, I would like to know if there is any way to minimize || y - Xb||^2 under the constraint b'b=1. Thank you Giancarlo

Hello, all, I had problems with an extension to a classic optimization problem. The target is to minimize a quadratic form a'Ma with respect to vector b, where vector a=(b',-1)', i.e., a is the expand of b, and M is a symmetric matrix (positive definite if needed). One more constrain on b is b'b=1. I want to solve b given M. I tried but it seems impossible to find an analytic

I am looking to fit one or more latent categorical variables to data that is a mixture of categorical and continuous variables. Factor analysis would work for continuous data, latent class analysis for categorical data. I understand that in a package such as MPlus I could perform a single analysis of both data types. Are there similar routines available in R? Stuart -----Original Message-----

Hi all, This was posted originally on r-sig-mixed-models, but I thought I would post here as well as it might be of more general interest. With a colleague, I have been trying to implement the Conditional AIC described by Vaida and Blanchard 2005 Biometrika, "Conditional Akaike information for mixed-effects models". This quantity is derived in a way analogous to the AIC, but is

I wonder whether any of you know of an efficient way to calculate the approximate degrees of freedom of a gamm() fit. Calculating the smoother/projection matrix S: y -> \hat y and then its trace by sum(eigen(S))$values is what I've been doing so far- but I was hoping there might be a more efficient way than doing the spectral decomposition of an NxN-matrix. The degrees of freedom

(renaming subject as I am partially getting off-topic) On Sunday 10 August 2014 16:26:07 Richard W.M. Jones wrote: > > The next issue I see now is about the value_value function. This is > > briefly documented as: "return data length, data type and data of a > > value". > > > > For Perl, Python and OCaml, this is not true. A tuple is returned > >

Hi, I would like to solve a double integral of the form \int_0^1 \int_0^1 x*y dx dy using Gauss Quadrature. I know that I can use R's integrate function to calculate it: integrate(function(y) { sapply(y, function(y) { integrate(function(x) x*y, 0, 1)$value }) }, 0, 1) but I would like to use Gauss Quadrature to do it. I have written the following code (using R's statmod package)

I want to compute B=A^{1/2} such that B*B=A. For example a=matrix(c(1,.2,.2,.2,1,.2,.2,.2,1),ncol=3) so > a [,1] [,2] [,3] [1,] 1.0 0.2 0.2 [2,] 0.2 1.0 0.2 [3,] 0.2 0.2 1.0 > a%*%a [,1] [,2] [,3] [1,] 1.08 0.44 0.44 [2,] 0.44 1.08 0.44 [3,] 0.44 0.44 1.08 > b=a%*%a i have tried to use singular value decomposion > c=svd(b) > c$u%*%diag(sqrt(c$d))

Dear All, My question is simple but I need someone to help me out. Suppose I have a positive definite matrix A. The funtion chol() gives matrix L, such that A = L'L. The inverse of A, say A.inv, is also positive definite and can be factorized as A.inv = M'M. Then A = inverse of (A.inv) = inverse of (M'M) = (inverse of M) %*% (inverse of M)' = ((inverse of