similar to: Downweighting of cases in GLM

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

Dear all, we are trying to model some data from 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

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 I tried decorana (I know it's been replaced by ca.)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 =

If I have a column with 2 levels, but one level has no remaining observations. Can I remove the level? Had intended to do it as listed below, but soon realized that even though there are no observations, the level is still there. For instance summary(dbs3.train.sans.influential.obs$HAC) yields 0 ,1 4685,0 nlevels(dbs3.train.sans.influential.obs$HAC) yields [1] 2 drop.list <- NULL

I was wondering if someone might be able to tell me what formula R's influence.measures function uses for determining whether the hat value it computes is influential (i.e., the true/false value in the "hat" column of the returned is.inf data frame). The reason I'm asking is that its results disagree with what I've just learned in my statistics class, namely that a point

influence.measures gives several measures of influence for each observation (Cook's Distance, etc) and actually flags observations that it determines are influential by any of the measures. Looks good! But how does it discriminate between the influential and non- influential observations by each of the measures? Like does it do a Bonferroni-corrected t on the residuals identified by

I am having trouble understanding (i.e. getting) confidence intervals from the survey package. I am using R version 2.8.1 (2008-12-22) and survey package (3.11-2) on FC7 linux. To simplify my question I use an example from that package: R> data(api) R> dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) R> (tst <- svyby(~api99, ~stype,

On 7/27/2016 12:17 PM, Chris Bieneman wrote: > > This is a really bad argument for large influential changes like this. Quite the contrary---anybody can participate and anybody can express their concerns, explain their goals, their workflow, etc. For a large influential changes like this, "zoning out" is a poor choice of action. > I suspect this is why the idea of having a

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

Hi everyones, my function like; e <- rnorm(n=50, mean=0, sd=sqrt(0.5625)) x0 <- c(rep(1,50)) x1 <- rnorm(n=50,mean=2,sd=1) x2 <- rnorm(n=50,mean=2,sd=1) x3 <- rnorm(n=50,mean=2,sd=1) x4 <- rnorm(n=50,mean=2,sd=1) y <- 1+ 2*x1+4*x2+3*x3+2*x4+e x2[1] = 10 #influential observarion y[1] = 10 #influential observarion data.x <- matrix(c(x0,x1,x2,x3,x4),ncol=5) data.y

Webinar signup: Advances in Gradient Boosting: the Power of Post-Processing December 14, 10-11 a.m., PST Webinar Registration: http://2.salford-systems.com/gradientboosting-and-post-processing/ Course Outline: * Gradient Boosting and Post-Processing: o What is missing from Gradient Boosting? o Why post-processing techniques are used? * Applications Benefiting from

Hi, I am a new user of R. This is a conceptual doubt regarding screeing out outliers from the dataset in regression. I read up that Cook's distance can be used and if we want to remove influential observations, we can use the metric (>4/n) (n=no of observations) to remove any outliers. I also came across Grubb's test to identify outliers in univariate distns. (assumed normal) but i

Webinar: Advances in Gradient Boosting: the Power of Post-Processing TOMORROW: December 14, 10-11 a.m., PST Webinar Registration: http://2.salford-systems.com/gradientboosting-and-post-processing/ Course Outline: I. Gradient Boosting and Post-Processing: o What is missing from Gradient Boosting? o Why post-processing techniques are used? II. Applications Benefiting from Post-Processing:

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

Hi R users, I have two data matrix, one with community data and another with environmental data. Prior to preform the CCA, I have used PCA to select some environmental variables and to avoid redundance information. The result is that I have 4 environmental variables and my community data matrix where, following bibliography, I have eliminated rare species. All variables were log-transformed (x+1)

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 opaque. In other cases, the

Hi, I' am interested in differences between sample's result when samples consist of full elements and consist of only distinct elements. When sample consist of full elements it take about 120 sec., but when consist of only distinct elements it take about 4.5 or 5 times more sec. I expected that opposite of this result, because unique(sample) has less elements than full sample. Code as

Hi! I'm using a large panel data, and now I have faced some difficulties with my analysis. The predictors are not normally distributed and there are quite many outliers (some of them are influential though). I have tried to change the logarythm, but i'm not sure, how to do that. I want also draw a plot picture in which logarythms of predictors x and y are changed. How could I do that?

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