similar to: geographically weighted glm

Greetings R users, I am an R graphics newbie trying to produce a custom trellis plot using pairs() with R 2.7.2. I have spatial data on which I run a geographically weighted regression (gwr, using the -spgwr- package). I want to check the gwr coefficients for multicollinearity and spatial association, following Wheeler and Tiefelsdorf (2005), and I would like to summarize the results of this

Hi everyone, I'm working with a modest sized spatial database consisting of 1513 records and 50 variables. Fourteen of these are dummy variables delineating regional planning councils. I'm trying to understand how to integrate the dummy variables in the geographically weight regression model. I'm reading Fotheringham et.al. and see reference to using dummy variables, but I don't

Dear all, I would like to use a GWR model in order to spatially predict food insecurity in Africa. I have a georeferenced village data-bases and I've run a "classic" regression model (taking into account the spatial dependence of the errors) that works fairly well for Niger that is a quite homogenous country. Now, I would like to make the same thing for countries where it should be

Supervisors: Dr Alex Singleton; Professor Chris Brunsdon Industrial Partner: Office for National Statistics Applicants are invited for a PhD studentship at the University of Liverpool within the ESRC North West DTC. The studentship will be supervised by Dr Alex Singleton and Professor Chris Brunsdon in the Department of Geography and Planning; and is being conducted in collaboration with the

Dear all, is it possible to perform logstic type of geographical weighted regression in R software? thanks in advance. robert. [[alternative HTML version deleted]]

I have a question about how to compare a GLM with a GAM model using anova function. A GLM is performed for example: model1 <-glm(formula = exitus ~ age+gender+diabetes, family = "binomial", na.action = na.exclude) A second nested model could be: model2 <-glm(formula = exitus ~ age+gender, family = "binomial", na.action = na.exclude) To compare these two GLM

How do I go about generating a WEIGHTED mean (and standard error) of a variable (e.g., expenditures) for each level of a categorical variable (e.g., geographic region)? I'm looking for something comparable to PROC MEANS in SAS with both a class and weight statement. Thanks. Marc [[alternative HTML version deleted]]

On Mar 20, 2024, at 13:16, Wayne Barron <wayne at cffcs.com> wrote: > In Windows and Linux web servers, we can create a forest for our web servers. > Send traffic to different servers to even the workload. > > Can we do something like this with the Icecast servers? > (or) > Will we have to install new VMs, add the heavy stations on that one, > and send the new traffic

Dear All, I am trying to produce log-log plots in R and I was wondering if any of you have a 'template' for generating these with 'nice' labels and log-log grids? I know I can set up axes individually and use the intervals I want, however, I will be producing a large number of these plots and would not like to do this manually for each of them + I am very new to R and at the

Hi All, I have a problem with weighted logistic regression. I have a number of SNPs and a case/control scenario, but not all genotypes are as "guaranteed" as others, so I am using weights to downsample the importance of individuals whose genotype has been heavily "inferred". My data is quite big, but with a dummy example: > status <- c(1,1,1,0,0) > SNPs <-

Dear R-users, I intend to do a spatial analysis on the genetic structuring within a population. For this I had thought to prepare a kernel density estimate map showing the spatial distribution of individuals, while incorporating the genetic distances among individuals. I have a dataset of locations of N unique individuals (XY-coordinates) and an NxN matrix with the genetic distances given as a

Dear all My 5 cents (or Rappen in CH) if it comes to serving many clients. We are running a 4 node cluster since several years ? rock solid and w/o any issues. This cluster serves many thousands of listeners from all over the world. Our source transcoder sending the audio streams to each Node. Hence, transcoding power is not an issue here. The four Nodes a geographically dispersed in 3

Hi all I'm doing some things with a colleague comparing different sorts of models. My colleague has fitted a number of glms in Genstat (which I have never used), while the glm I have been using is only available for R. He has a spreadsheet of fitted means from each of his models obtained from using the Genstat "predict" function. For example, suppose we fit the model of the type

Good day all, I was wondering if I could pick some admin heads here as I have a HUGE project I have been tasked with..... I am asking here since I will be basing everything on Centos, and want it to all play nice together. If anyone feels this is straying off topic, please just reply off list. I do not want to be the cause of one of those threads. I have 3 offices, 1 in Canada, 2 in

Hi Wayne Yep, HLS is something we would like to use with Icecast. As you mentioned, Liquidsoap made a great step in the right direction if it comes to HLS. It seem that icecast might not get this support soon, as you might read in the discussion list. I don?t share these arguments, because we have real world use cases (we have many of them) where we would like use icecast with HLS support.

Dear R-users: Im trying (using gam package) to develop a stepwise analysis. My gam object contains five pedictor variables (a,b,c,d,e,f). I define the step.gam: step.gam(gamobject, scope=list("a"= ~s(a,4), "b"= ~s(b,4), "c"= ~s(c,4), "d"= ~s(d,4), "e"= ~s(e,4), "f"= ~s(f,4))) However, the result shows a formula containing the whole

I fitted a GAM model with Poisson distribution to a data with about 200 observations. I noticed that the plot of the residuals versus fitted values show a trend. Residuals tend to be lower for higher fitted values. Because, I'm dealing with count data, I'm thinking that this might be due to overdispersion. Is there a way to account for overdispersion in any of the packages MGCV or GAM?

How can I obtain the values of confidence intervals from gam anf glm objects? Thanks in advance -- David Nogu?s Bravo Functional Ecology and Biodiversity Department Pyrenean Institute of Ecology Spanish Research Council Av. Monta?ana 1005 Zaragoza - CP 50059 976716030 - 976716019 (fax)

Dear All, I am economist and working on poverty / income inequality. I need descriptive statitics like the ratio of education expentitures between different income quintiles where each household has a different weight. After a bit of google search I found 'Hmisc' and 'quantreg' libraries for weighted quantiles. The problem is that these packages give me only weighted quintiles;

Dear R Help, I have noticed some inconsistent behaviour of add1 and drop1 with a weighted linear model, which affects the interpretation of the results. I have these data to fit with a linear model, I want to weight them by the relative size of the geographical areas they represent. _________________________________________________________________________________________ > example