similar to: GAM weights

Dear all, I want to model presence/absence data of tree occurrence using a number of predictor variables. Absences of some sample points are probably wrongly specified (they should be presences) due to land use which can not be incorporated as a predictor because of some sort of arbitrariness. Some trees were logged while others were not but to both cases land use as a category would apply.

Can anyone point me in the right direction of figuring out what downweight() is doing? I am using vegan to perform CCA on diatom assemblage data. I have a lot of rare species, so I want to reduce the influence of rare species in my CCA. I have read that some authors reduce rare species by only including species with an abundance of at least 1% in at least one sample (other authors use 5% as a

Hi all, I have site data with plant species cover and am looking for trends. I'm kind of new to this, but have done lots of reading and can't find an answer. I tried decorana (I know it's been replaced by ca.) and see a trend, but I'm not sure what it means. Is there a way to get the loadings/eigenvectors of the axes (like in PCA)? Is there a way to do this with rda() too? How

Hi, I wonder if someone can help me. I have built a gam model to predict the presence of cold water corals and am now trying to evaluate my model by splitting my dataset into training/test datasets. In an ideal world I would use the sample() function to randomly select rows of data for me so for example with 936 rows of data in my HH dataset I might say ss <- sample(nrow(HH), size =

On a RHEL4u4 machine... [root@rincon mnt]# mount -t smbfs -o username=qamount,password=m0unt1\! //10.4.0.15/Builds /mnt/builds/ 18179: session setup failed: ERRDOS - ERRnoaccess (Access denied.) SMB connection failed 10.4.0.15 is a W2K3 R2 file server. Yes, I know this isn't a Samba question, but Google turns up absolutely nothing, and I figure Samba people are far more likely to have some

Dear Samba org. I have a problem when mounting a windows drive into my linux server, I have samba 2.2.3a and kernel 2.4.18 with SuSE 8.0 The problem I have is that I get a "segmentation fault" message when I try to access some windows directories, after that samba gets frozen and I cant kill the process (looks to me it keeps running an endless cycle). This wasn't a problem with

Hi R user, Could you please give me some hints on how I can convert text in table format in R? I was doing model assessment using dismo package for example: bg <- randomPoints(current, 500) pvtest <- data.frame(extract(current, occtest)) avtest <- data.frame(extract(current, bg)) e2 = evaluate(model, p=pvtest, a=avtest) > e2 class : ModelEvaluation n presences : 10 n

Dear all, I have built glm models based on presences/absences and a number of predictor maps and would like to compute habitat suitability based on the modelled coefficients. I thought this is pretty straight forward and wanted to use predict() and supply the new data in a data frame, with one column for each predictor. However, I do get an error msg warning me that the number of rows for

We are running samba 2.2.8 on redhat linux kernel 2.4.20, our linux server joined a PDC server runing win 2k SP2. We share a FS from our linux server and mapped it from the PDC, everything is OK so far, when we create a directory from the client that have the FS mapped and try to see the security properties it shows all check boxes empty below next to the "Advanced.." button it display a

Hi, I am doing a binomial GLMM with a random intercept using the formula below, but I always get the same warning message. > m01 <- lmer(pres~ HT + DN + dtree + DNm + cmnhi + cmxes + cplan + craan + lfphal0100 + lfov0100 + lfop0100 + (1|plot), family=binomial, data=vphal, verbose=TRUE) 0: 6309.9448: 0.459924 -5.20747 -0.378722 0.558779 -0.200922 -0.0488451 -0.397844 0.367916 -2.09820

Amigos de la erre. He creado mi primer bucle con for para entrenar unos modelos con GAM. La respuesta es quasipoisson porque estoy trabajando con densidades de peces. Sin embargo, tengo un problema, no se muy bien como añadir el offset a la formula siguiente cuando creo el bucle. GAM.A1 <-gam ((DYO)~s(DMA,k=4)+ s(WOD,k=4)+s(CIN,k=4)+s(DRA,k=4)+s(DBR,k=4)

I have a problem finding how to use prior.weights or weights options when performing a stepwise GAM analysis (gam and step.gam functions) for covariate inclusions. Thanks beforehand for references or help on how to handle these 2 options. -- Paul Baverel, MSc, PhD student Div. of Pharmacokinetics and Drug Therapy, Dept. of Pharmaceutical Biosciences, Faculty of Pharmacy Uppsala University Box

Hi, I'm adjusting a binomial gam and I use the inverse variance of each proportion as weights in the gam function (it's a model for the sex ratio of hake). My doubt is what are these weights doing ? I get a negative UBRE and if I use GCV the scale parameter is close to 0 ... Regards EJ

Amigos de la erre. He creado mi primer bucle con for para entrenar unos modelos con GAM. La respuesta es quasipoisson porque estoy trabajando con densidades de peces. Sin embargo, tengo un problema, no se muy bien como añadir el offset a la formula siguiente cuando creo el bucle. GAM.A1 <-gam ((DYO)~s(DMA,k=4)+ s(WOD,k=4)+s(CIN,k=4)+s(DRA,k=4)+s(DBR,k=4)

Hello all, I've got a problem with a function in a R package I've developed (MetaLandSim). One of the outputs of my function is the plotting of four images (combining a graph and a map). This plotting is made in a graphical device, with the graph in one side and the map in the other. My function works perfectly fine until it has to plot the map. Then it stops and I get an error.

Hello, Suppose one is interested in fitting a GLM with a log link to binomial data. How does R choose starting values for the estimation procedure? Assuming I don't supply them. Thanks, Tyler

Hi everyone: I am just wondering how can I use weights with MCMClogit function (in MCMCpack package). For example, in case of glm function as given below, there is weights option in the arguments. Aparently there is no option of using weights in MCMClogit. glm(formula, family = gaussian, data, weights, subset, na.action, start = NULL, etastart, mustart, offset, control =

We are currently analyzing data on children clustered in day care-centers (DCC). We have tried to use geepack and gee libraries to estimate an overall incidence rate for absences (=number of absences/risk time) by specifying geese(number.absences ~ offset(log(risktime)), id=day.care.id, family=poisson("log"), data=dcc, corstr="exch",

# Hello R Users, # # I would like to fit a glm model with quasi family and # logistical link function, but this does not seam to work # with binary data. # # Please don't suggest to use the quasibinomial family. This # works out, but when applied to the true data, the # variance function does not seams to be # appropriate. # # I couldn't see in the # theory why this does not work. # Is

Hi, Currently the `gaussian' family's initialization code signals an error if any response data are zero or negative and a log link is used. Given that zero or negative response data are perfectly legitimate under the GLM fitted using `gaussian("log")', this seems a bit unsatisfactory. Might it be worth changing it? The current offending code from `gaussian' is: