similar to: matrix log

Hi guys, Got a few days left and I need to model a random effect of species on the body mass (logM) and temperature (K) slopes. This is what i've done so far that works: model1<-lme(logSSP~logM + K,random=~1|species,data=data1) model2<-lme(logSSP~logM + K,random=~K|species,data=data1) model3<-lme(logSSP~logM + K,random=~logM|species,data=data1) The one I now want is:

Hi, there: As you all know, correlation is not a very robust procedure. Sometimes correlation could be driven by a few outliers. There are a few ways to improve the robustness of correlation (pearson correlation), either by outlier removal procedure, or resampling technique. I am wondering if there is any R package or R code that have incorporated outlier removal or resampling procedure in

Hello, I use wavethresh packages to perform wavelet analysis. In particular, I would like to compare 2 signals (vectors) after a wavelet decomposition. I would like to use cor.test function, but this function acts on the entire vector values. I plan to perform a cor.test on each level of the wavelet decomposition, say N. So I will have at the end of a first step N results of cor.test. How can

Hi, If a matrix is not positive definite, make.positive.definite() function in corpcor library finds the nearest positive definite matrix by the method proposed by Higham (1988). However, when I deal with correlation matrices whose diagonals have to be 1 by definition, how do I do it? The above-mentioned function seem to mess up the diagonal entries. [I haven't seen this complication, but

Dear all, Does R offer a means by which a function can determine whether its return value is assigned? I am using R 2.4.1 for Windows. Suppose what I am looking for is called "return.value.assigned". Then one might use it like this myfunction <- function () { # Create bigobject here if (return.value.assigned()) { bigobject } else {

Hi R experts, I'm using the xyplot function in lattice to draw a multipanel plot consiting of 5x6 scatterplots. Now I need to link single points in each of those scatterplots (=panel),but the points, that need linking are different for each panel. I tried to use the panel.segments function for that, but I can't address each panel separately. Links right for panel 1, show up in all other

The marilith boxes use a conserver (http://www.conserver.com/) setup for serial access. Our installation exports the logs via http allowing us to grab them with wget. Sending debug keys with is handled separately via xenuse. xenuse ultimately speaks to the conserver too but it abstracts away the IP and port to use so this is preferred. With these changes the correct Serial hostprop for a

Magazine: Pharmaceutical Manufacturing Date: Nov/Dec 2009 Title: What Your ICH Q8 Design Space Needs: A Multivariate Predictive Distribution Author: Peterson, John J. Company: GlaxoSmithKline Pharmaceuticals Summary: Multivariate Predicitive distibution quantifies the level of QA in a design space. "Parametric Bootstrapping" can help simplify early analysis and compliment Bayesian

Dear R-gurus, I have an interpretation problem regarding lme models. I am currently working on dog locomotion, particularly on some variation factors. I try to figure out which limb out of 2 generated more dispersed data. I record a value called Peak, around 20 times for each limb with a record. I repeat the records during a single day, and on several days. I tried to build two models, one

Hi All, I would like to ask a question on fixed and random effecti in lme. I am fiddlying around Mick Crawley dataset "rats" : http://www.bio.ic.ac.uk/research/mjcraw/statcomp/data/ The advantage is that most work is already done in Crawley's book (page 361 onwards) so I can check what I am doing. I am tryg to reproduce the nested analysis on page 368:

TOP Institute Pharma (TI Pharma) has granted our proposal to set up a mechanism-based PKPD modelling platform. This platform focuses on the transfer of knowledge from academia to the pharmaceutical industry and is a collaborative effort of four excellent academic institutes and six leading global pharmaceutical industries. Unique to this platform is the availability of shared databases on

Dear members, I would like to switch from nlme to lme4 and try to translate some of my models that worked fine with lme. I have problems with the pdMat classes. Below a toy dataset with a fixed effect F and a random effect R. I gave also 2 similar lme models. The one containing pdLogChol (lme1) is easy to translate (as it is an explicit notation of the default model) The more parsimonious

I am trying to use the survfit() function with the newdata argument to produce predicted survivor curves for a particular covariate profile. The main purpose of the plot will be to visualise the effect of snp1, coded 0 and 1. In my Cox model I have stratified by one variable, edu, and so I know I will automatically get a separate curve for each strata. My problem is how to deal with the

Dear R users, I am curious to know if someone has connected R with the Insight Segmentation and Registration Toolkit (www.itk.org). From the website... ITK is an open-source, cross-platform system that provides developers with an extensive suite of software tools for image analysis. Developed through extreme programming methodologies, ITK employs leading-edge algorithms for registering and

R-help, I am trying to perform a basic anlaysis of the BreastCancer data from "mlbench" using the svm() function in "e1071". I use the following code library("e1071") library("mlbench") data(BreastCancer) BC <- subset(BreastCancer, select=-Id) pairs(BC) model <- svm(Class ~ ., data=BC, cross=10) ## plot(model, BC, ) tobj <- tune.svm(Class ~ .,

dcemri 0.10 has been released on CRAN "dcemri" is (to the best of my knowledge) the first public-domain software package for the quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI or DWI). Data import and export is availble for ANALYZE or NIfTI data formats (sorry, no DICOM). Images are stored in neurological format regardless of the

dcemri 0.10 has been released on CRAN "dcemri" is (to the best of my knowledge) the first public-domain software package for the quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI or DWI). Data import and export is availble for ANALYZE or NIfTI data formats (sorry, no DICOM). Images are stored in neurological format regardless of the

Dear all, Has someone implemented in R (or any other language) Knol DL, ten Berge JMF. Least-squares approximation of an improper correlation matrix by a proper one. Psychometrika, 1989, 54, 53-61. or any other similar algorithm? Best regards Jens Oehlschl?gel Background: I want to factanal() matrices of polychoric correlations which have negative eigenvalue. I coded Highham 2002

Hi Folks, I'm trying to understand the model specification formalities for 'lme', and the documentation is leaving me a bit confused. Specifically, using the example dataset 'Orthodont' in the 'nlme' package, first I use the invocation given in the example shown by "?lme": > fm1 <- lme(distance ~ age, data = Orthodont) # random is ~ age Despite the

Dear R user, I am trying to reproduce the results in Montgomery D.C (2001, chap 13, example 13-1). Briefly, there are three suppliers, four batches nested within suppliers and three determinations of purity (response variable) on each batch. It is a two stage nested design, where suppliers are fixed and batches are random. y_ijk=mu+tau_i+beta_j(nested in tau_i)+epsilon_ijk Here are the