search for: downweight

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 (ot...

...porated 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. However, at last I cannot decide if absences occur truly because of unsuitability of conditions at sampling location. Therefore, I want to downweight these "wrong" absences based on a certain algorithm producing decimal numbers so that they become less influential (i.d. I have a column "weights" (1>=weights>0) of the same lenght as predictors and response variable). I tried to figure this out checking the forum, but are...

...axes. Also after doing envfit() and plotting, is there a way to label sites based on certain environmental factors? For instance, I want to highlight plots that have a certain treatment (or have different colored points for the different treatments. I don't know if it matters but I used downweight() before applying decorana(). down <-downweight(spp2009bttl, fraction = 5) down.dca <- decorana(down) scores(down.dca) summary(down.dca) Thanks so much! -Akane [[alternative HTML version deleted]]

Can anyone comment or point me to a discussion of the pros and cons of robust regressions, vs. a more "manual" approach to trimming outliers and/or "normalizing" data used in regression analysis?

Dear List, I want to use transparency in R to represent downweighting of observations based on clusters (repeated observations in a dataset). Some clusters will have identical covariate values in a parameter space -- in the 2D x,y case, these represent a bunch of semi-tranparent dots in the same place. I'd like these overlapping dots to be completely op...

Hi, This is actually a theory question. I'm a bit confused by the s.window parameter in the stl() function (which is in the ts package). For example, in the stl documentation it uses the nottem data, and then: plot(stl(nottem, s.win = 4, t.win = 50, t.jump = 1)) What does it mean by s.win = 4? Is it because a year has 4 seasons (namely Spring, Summer, Autumn and Winter)? If so will it

Hello We would like to perform a logistic regression analysis weighting the independent variable in a temporal fashion, i.e. events occuring most recent get highest weight. Does anyone know how to do this in R?? Regards S. Merser and S. Lophaven

...rare plants so we always have less than 50 1x1 km presences, and the total area is about 550.000 square km. So we have a real problem, when we perform a GAM, if we consider only the same amount of absences than presences. We have thought to use a greater number of absences but in this case we shoud downweight them. Does anybody know how to use the ?wheight? term? thank you in advance daniel -------------------------------------------------------------------------- Mensaje enviado mediante una herramienta Webmail integrada en *El Rincon*: ------------->>>>>>>> https://rincon....

...uot;good for my eyes", however, when I did summary(CCA1), the first two axis accounted 0.04 CCA1 and similar in CCA2....then the variation explained by each axis was small. On the other hand, when I performed CCA in CANOCO, without selecting the option Log-transforming data matrix and without downweighting rare species, the results were the opposite from the CCA performed in R. The axes accounted high percentage of variation, the model was also significant, but the plot had little sense. Thank you very much! [[alternative HTML version deleted]]

Hello, I know that two possible approaches to dealing with clustered data would be GEE or a robust cluster covariance matrix from a standard regression. What are the differences between these two methods, or are they doing the same thing? Thanks. -- View this message in context: http://www.nabble.com/Difference-between-GEE-and-Robust-Cluster-Standard-Errors-tp22092897p22092897.html Sent from the

Dear friends. On the Bayesian averaging homepage http://www.research.att.com/~volinsky/bma.html I found some S code some of which perhaps may run in R. There was a call to an algorithm possibly within S but not supported by R 64.1: "leaps and bounds". I guess it is a minimization step. Can anyone clarify the algorithm and perhaps even give a pointer to some code ? I guess this may be

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 <-

...We've tried various approaches for zero-heavy data but I think this increasingly looks like a survival analysis to me. The question is, how can I adapt a survival analysis so that it takes into account the different probability of censoring (the different random probability of being a zero) and downweights the uninformative zeroes? (With the added fun of double censoring, non-integer values and a small number of negative values). Remember that what we have is the number of species fractions changing status in a single time period, not the more usual "time to event". Please also remember:...

Hi, I would appreciate of someone could explain how the residual standard error is computed for rlm models (MASS package). Usually, one would expect to get the residual standard error by > sqrt(sum((y-fitted(fm))^2)/(n-2)) where y is the response, fm a linear model with an intercept and slope for x and n the number of observations. This does not seem to work for rlm models and I am wondering

Dear list, first apologies for this is not strictly an R question but a theoretical one. I have read that use of k-means clustering assumes sphericity of data distribution. Can anyone explain me what this means? My statistical background is too poor. Is it another kind of distribution, like gaussian or binomial? What does it happen if the distribution is not spherical? Could you give me an

Dear R developers, I am a little disappointed that my bug report only made it to the wishlist, with the argument: Well, it does not say it has. Only relevant to prediction intervals. predict.lm does calculate prediction intervals for linear models from weighted regression, so they should be correct, right? As far as I can see they are bound to be wrong in almost all cases, if no weights