similar to: geoRglm question with covariates

Dear R users. I'm trying to fit a generalised linear spatial mode using the geoRglm package. To do so, I'm preparing my data (geodata) as follow: geoData9093 = as.geodata(data9093, coords.col= 17:18, data.col=15,* covar.col=16*) where covar.col is a factor variable (years in this case 90-91-92-93)). Then I run the model as follow: / model.5 = list(cov.pars=c(1,1),

Dear R users: I want to model mosquito count data based on landcover attributes and meteorological variables using a Poisson GLSM in the geoRglm package. I have monthly mosquito counts over more than 20 years with repeated observations from individual trap sites over time. I have used as.geodata() to successfully read my dataset into the geodata format utilized by geoR and geoRglm,

Hi R-people: Working in geoRglm, it shows me, according to AIC criterion, that the non-spacial model describes the process in a better way. It's the first time that I'm facing up to. These are my results: OP2003Seppos.AICnonsp-OP2003Seppos.AICsp #[1] -4 (OP2003Seppos.lf0.p<-exp(OP2003Seppos.lf0$beta)/(1+exp(OP2003Seppos.lf0$beta))) #P non spatial #[1] 0.9717596

> -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch [SMTP:r-help-bounces at stat.math.ethz.ch] On Behalf Of ernesto > Sent: Friday, January 13, 2006 9:25 AM > To: Mailing List R > Subject: [R] help with gepRglm::likfit.glsm > > Hi, > > I'm exploring likfit.glsm and I need some help. I have to say that I'm > not an MCMC expert ... >

Hello, I used glsm.mcmc and likfit.glsm to create model. Now I want to predict at different locations, but I can't get glsm.krige to work. I keep getting the error that trend.d and trend.l must have similar specifications. I have tried numerous ways to include the covariates in the glsm.krige model, and I keep getting the same error message. The bolded part is the part that doesn't work.

Hi. I'm trying to perform what should be a reasonably basic analysis of some spatial presence/absence data but am somewhat overwhelmed by the options available and could do with a helpful pointer. My researches so far indicate that if my data were normal, I would simply use gls() (in nlme) and one of the various corSpatial functions (eg. corSpher() to be analagous to similar analysis in SAS)

Hi, I'm writing some R code that requires a massive amount of looping and would ideally like to write it so that it avoid the use of "for" loop ... however I'm having some trouble. Very briefly, the basic idea is to implement a binary partitioning algorithm to determine the optimal cutpoint based on deviance measures obtained from likelihood estiamtes. This is in the

I don't know if this is the best place to post this question but I will try anyway. I have two experiements for which I use one-way matched-randomized ANOVA for the analysis and I would like to compare different treatments in the two experiments. The only common group in the two experiments are the controls. Is there any ANOVA design that allows me to make this comparison taking into

I have data set up like the following: control1 <- sample(1:75, 3947398, replace=TRUE) control2 <- sample(1:75, 28793, replace=TRUE) control3 <- sample(1:100, 392733, replace=TRUE) control4 <- sample(1:75, 858383, replace=TRUE) patient1 <- sample(1:100, 28048, replace=TRUE) patient2 <- sample(1:50, 80400, replace=TRUE) patient3 <- sample(1:100, 48239, replace=TRUE) control

Can someone show me some code to do normalization by the median of some control genes for the example below? Many Many Thanks in advance This strategy selects a subset of genes (called ?control genes?) and makes the median of their data distribution similar across arrays. ??? ??? id1??? id2??? id3 control1??? 0.8??? 0.7??? 0.6 control2??? 0.6??? 0.2??? 0.4 probe1??? ??? 0.3??? 0.2??? 0.5

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Here is my model with interaction terms and control variables (I changed variables names for easy read): reg1 <- lm(y ~ x1*x2*x3 +control1 + control2 + control3) x1 ranges from 0 to 6; x2 from 0 to 5; and x3 from 0 to 4. All three are discrete ordinal variables; but I will treat them as continuous variables. (a) How can I see the predicted values of y for each of these scenarios (210

Hi Michael- Since you bring up libquantum performance can you let me know what the IR will look like for this small code snippet (libquantum-like) with –enable-cond-stores-vec ? I ask because I don’t see vectorization kicking in unless -force-vector-width=<> is specified. Let me know if I am missing something. -Thx struct nodeTy { unsigned int c1; unsigned int c2; unsigned

Dear list, Hello to everybody, I´m interested in finding a package for statistical analysis of binary data, I have a matrix with the following structure: Case1 Case2 Case 3 ....... CaseX Control1 Control2 Control3 ....... ControlY Pep1 1 0 1 1 0 0 0 1 Pep2 1 1 1 1 1 0 0 1 Pep3 0 1 1 1 1 0 0 1 ... Pepz 1 1 1 1 1 0 0 1 I would like to know some R package to calculte similarity matrix or

Hi Dibyendu, Are you using a recent compiler? What architecture are you targeting? The target will determine whether the vectorizer thinks vectorization is profitable without having to manually force the vector width. For example, top-of-trunk vectorizes your snippet with "clang -O2 -mllvm -enable-cond-stores-vec" and "--target=aarch64-unknown-linux-gnu". However, with

I'm trying to (re)learn Sweave and run into some problems. I use now ubuntu (8.10), emacs + ess. Slowly getting upto speed on ess. I have a complete (hopefully) .Rnw file, but the resulting .tex will not compile. The file does not contain anything exotic, but it produces pdf figures, and that is where the problems come: library(tools) > Sweave("varioCoo.Rnw") Writing to file

Hi Matt- Yeah I used a pretty recent llvm (post 3.9) on an x86-64 ( both AMD and Intel ). -dibyendu From: Matthew Simpson [mailto:mssimpso at codeaurora.org] Sent: Wednesday, December 14, 2016 10:03 PM To: Das, Dibyendu <Dibyendu.Das at amd.com> Cc: Michael Kuperstein <mkuper at google.com>; llvm-dev at lists.llvm.org Subject: Re: [llvm-dev] Enabling scalarized conditional stores in

If there are no objections, I'll submit a patch for review that sets the default value of "-enable-cond-stores-vec" to "true". Thanks! -- Matt On Wed, Dec 14, 2016 at 12:55 PM, Michael Kuperstein via llvm-dev < llvm-dev at lists.llvm.org> wrote: > I haven't verified what Matt described is what actually happens, but > assuming it is - that is a known

Hello, I install R under Mandrake Linux 8.2. R itself work fine, but I had an error to install packages, i.e. fields, geoR and geoRglm : $: R CMD INSTALL geoRglm_0.4-3.tar.gz Installing *source* package `geoRglm' ... libs gcc-3.0.1 -I/usr/lib/R/include -I/usr/local/include -mieee-fp -D__NO_MATH_INLINES -fPIC -O3 -fomit-frame-pointer -pipe -mcpu=pentiumpro -march=i586 -fno-fast-math

Hello, I install R under Mandrake Linux 8.2. R itself work fine, but I had an error to install packages, i.e. fields, geoR and geoRglm : $: R CMD INSTALL geoRglm_0.4-3.tar.gz Installing *source* package `geoRglm' ... libs gcc-3.0.1 -I/usr/lib/R/include -I/usr/local/include -mieee-fp -D__NO_MATH_INLINES -fPIC -O3 -fomit-frame-pointer -pipe -mcpu=pentiumpro -march=i586 -fno-fast-math