similar to: Model averaging with (and without) interaction terms

I'm trying to use the inflated binomial distribution of zeros (since 75% of the values are zeros) in a randomized block experiment with four quantitative treatments (0, 0.5, 1, 1.5), but I'm finding it difficult, since the examples available in VGAM packages like for example, leave us unsure of how it should be the data.frame for such analysis. Unfortunately the function glm does not have

[cc'ing back to r-help: this is good etiquette so that the responses will be seen by others/ archived for future reference.] On 10-08-25 04:35 PM, Marino Taussig De Bodonia, Agnese wrote: > Yes, I meant "MuMIn" > > the global formula I introduced was: > > rc4.mod<-lm(central$hunting~ central$year + central$gender + central$hunter + central$k.score +

Dear R users, Recent changes to the MuMIn package now means that the model averaging command (model.avg) no longer returns confidence intervals, but instead returns zvalues and corresponding pvalues for fixed effects included in models. Previously I have used this package for model selection/averaging following Greuber et al (2011) where it suggests that one should use confidence intervals from

I am struggling to get GAM model predictions from the top models calculated using model.avg in the package "MuMIn". My model looks something like the following: gamp <- gam(log10(y)~s(x1,bs="tp",k=3)+s(x2,bs="tp",k=3)+ s(x3,bs="tp",k=3)+s(x4,bs="tp",k=3)+s(x5,bs="tp",k=3)+ s(x6,bs="tp",k=3)+x7,data=dat,

Dear All, I'm using AIC-weighted model averaging to calculate model averaged parameter estimates and partial r-squares of each variable in a 10-variable linear regression. I've been using the MuMIn package to calculate parameter estimates, but am struggling with partial r-squares. There doesn't seem to be any function in the MuMIn package dealing with partial r-square

Hola a todos, He realizado un dredge (para obtener todos los modelos GAM posibles a parir de un full model), luego he seleccionado un confidence set (los modelos que no se diferencian en 2 en AIC) y he hecho un model averaging con ese confidence set. Ahora me gustaría aplicar ese modelo "average" ajustado sobre otro set de datos pero no se como especificar en R que use el mismo modelo

Dear R users, I am using lmer combined with AIC model selection and averaging (in the MuMIn package) to try and assess how isotope values (which indicate diet) vary within a population of animals. I have multiple measures from individuals (variable 'Tattoo') and multiple individuals within social groups within 4 locations (A, B, C ,D) crucially I am interested if there are

Hello, I recently began using R and the lme4 package to carry out linear mixed effects analyses. I am interested in the effects of variables 'prime','time', and 'mood' on 'reaction_time' while taking into account the random effect 'subjects.' I've read through documentation on lme4 and came up with the following formula for use with lmer:

Hi Everyone,  Sorry to ask what I think is a basic question but I really haven't found my answer yet in the archives.  I am trying to run a mixed effects model in R using the lme package. My experiment is such that I am interested in the effects of Temperature (2 levels) and Species (3 levels) on Growth. I collected individuals from three populations within each species. Because individuals

Dear all, We want to test if the invasiveStatus is predicted by the amount (quant) of animals arriving to a country of a certain species (taxonid). We are using lmer to perform the model. The model is: lmer(invasiveStatus~I(log(quant+1))+I(log(inDegree+1))+(1|taxonid)+(1|country), family=binomial,data=td), where invasiveStatus is a binary variable, quant and inDegree are integer variables, and

Dear All, I have conducted an experiment in order to examine predation pressure in the surroundings of potential wildlife road-crossing structures. I have documented predation occurrence (binary?) in these structures and calculated several possible explanatory variables describing the spatial heterogeneity in several scales. At the landscape scale I have calculated the percentage of different

Hello,<?xml:namespace prefix = o ns = "urn:schemas-microsoft-com:office:office" /><o:p></o:p> while doing my thesis in habitat modelling I´ve come across a problem with interaction terms. My question concerns the usage of interaction terms for linear regression modelling with R. If an interaction-term (predictor) is chosen for a multiple model, then, according to

HI, List I want to know weather any body has used BMA Package for model averaging for prediction of future values.....What are the paprmeters we have to suplly to the model. Any script for the same. thanks in advance ANIL KUMAR( METEOROLOGIST) LRF SECTION NATIONAL CLIMATE CENTER ADGM(RESEARCH) INDIA METEOROLOGICAL DEPARTMENT SHIVIJI NAGAR PUNE-411005 INDIA MOBILE +919422023277

Hello, is it possible in R to calculate a Weighted averaging partial least squares regression? I'm not firm in statistics and didn't found anything about weighted averaging in combination with PLS in the help archives. Or is it possible to develop a workaround with the pls-package? thanks for help in advance Andreas Plank -- _____________________________________________ Dipl. Biol.

Dear all, 1) I have a very large matrix of  str(keep)  num [1:153899, 1:3415] -98.6 -95.8 -96.4 -95.8 -98 ... that  I would like to reduce its size to something like str(keep)  num [1:1000, 1:3415] -98.6 -95.8 -96.4 -95.8 -98 ...  or anything similar in size as this is a matrix that needs plotting (so is ok if it is 1000 row, 995, or 1123) I think what I need here is a way of selecting

Dear R-help and ROCR developers (Tobias Sing and Oliver Sander) - I think my first question is generic and could apply to many methods, which is why I'm directing this initially to R-help as well as Tobias and Oliver. Question 1. The plot function in ROCR will average your cross validation data if asked. I'd like to use that averaged data to find a "best" cutoff but I

Dear all, does anyone have r code to perform model-averaging of regression parameters by Akaike weights, and/or to do all-possible-subsets lm modelling that reports parameter estimates, AICc and number of parameters for each model? I have been looking for these in the archive but found none. (I am aware that many of you would warn me against these methods advocated by Burnham and Anderson

I have the following data: http://i.imagehost.org/0650/Untitled2.jpg How would i average, say the 5th and the 8th entries? Thanks in advance -- View this message in context: http://n4.nabble.com/Simple-averaging-question-tp1009086p1009086.html Sent from the R help mailing list archive at Nabble.com.

Hi Folks, Ran into something I'd really like to do in R simply/elegantly, but my R - coding skills seem surpassed. This is the thing. Imagine the following data: labs<-c("abcdef","abcgg","tgthefdk","tgtijuel","tgtnjmoi","gbnt","dlift") dat<-c(0.5,0.25,1,2,16,0.250,4) dframe<-data.frame(labs,dat) I would like to

Dear all, I have a dataframe with 132 columns and 100 rows. Every 2nd column is a repeat measurement so that the columns could be titled, a a b b c c d d etc. I would like to average the repeats such that I am left with a data frame of 66 columns (of means) and 100 rows. I have been trying to use rowMeans but have not been able to average the pairs of columns, only the whole dataframe. Any