similar to: plot points using vis.gam

Hi all, I'm searching for a little clarification on partial mantel tests (ecodist package) I've a distance matrix (x,y), and several others containing environmental/chemical variables. Based on the help file, and the package instructions I've managed to implement the tests as; var1 ~ env1 + space to partial out the effect of space and test the relationship between the

Hi, Currently object.size() is not very useful on environments as it always returns 56 bytes, no matter how big the environment is: env1 <- new.env() object.size(env1) # 56 bytes env2 <- new.env(hash=TRUE, size=75000000L) object.size(env2) # 56 bytes env3 <- list2env(list(a=runif(25000000), L=LETTERS)) object.size(env3) # 56 bytes This makes it pretty useless on

Hi Gabe, On 09/29/2015 02:51 PM, Gabriel Becker wrote: > Herve, > > The problem then would be that for A a refClass whose fields take up N > bytes (in the sense that you mean), if we do > > B <- A > > A and B would look like the BOTH take up N bytes, for a total of 2N, > whereas AFAIK R would only be using ~ N + 2*56 bytes, right? Yes, but that's still a *much*

I am running into a problem with my LDAP puppet setup with this error message: PuppetClient: err: Could not retrieve catalog from remote server: Error 400 on SERVER: Could not parse for environment DR: undefined method `to_sym'' for ["ENV1", "ENV2"]:Array PuppetMaster: err: Could not parse for environment DR: undefined method `to_sym'' for ["ENV1",

Hi, (1.) How is it possible to get automatic a "lower triangle of correlation matrix" ? h.cor <- cor(dat,use="pairwise.complete.obs") zz <- lower.tri(h.cor,diag=T) ### that's not what i wish and "wrong" ? results <- matrix(unlist(h.cor[upper.tri(h.cor,diag=T)])) results <- matrix(unlist(h.cor[upper.tri(h.cor,diag=T)]),5) Must i take the lowest

Hello, I''m using the following expression to format a list: $valid_environments = [''env1'', ''env2'', ''env3''] $env_message = $valid_environments.reduce |$message, $env| { "${message}, ${env}" } It works at run-time (Puppet 3.2.4 standalone with "--parser=future"). However in Eclipse (v4.3.1),

Hi R Users, I created interaction plots in ggplot2 and was trying to add output of two way ANOVA models, especially only interaction ( example treatment*control F(XX, XX) = xxx, p = xxx) into figures, but i was not able to add. Would you mind to help on how I can add information into each figure? I have attached the example data and the code that I used for this. dat<-structure(list(Sites

Hi all, I am attempting to learn my way through the sem package by constructing a simple structural model for some of my data on bird diversity, abundance, and primary productivity. I have constructed a covariance matrix between these variables as per the following: >S_matrix = matrix(c( >+ 0.003083259, 0, 0, >+ 0.143870284, 89.7648490, 0, >+ 0.276950919,

Dear useRs, I am using mgcv version 1.7-16. When I create a model with a few non-linear terms and a random intercept for (in my case) country using s(Country,bs="re"), the representative line in my model (i.e. approximate significance of smooth terms) for the random intercept reads: edf Ref.df F p-value s(Country) 36.127 58.551 0.644

Hi All, I am using canonical correspondence analysis to compare a community composition matrix to a matrix of sample spatial relationships and environmental variables. In order to parse out how much variance is explained purely by space (S/E) or the environment (E/S) I am using a conditional (partial) CCA. I want to test significance via Monte Carlo but I can not find a way to do this with a

Herve, The problem then would be that for A a refClass whose fields take up N bytes (in the sense that you mean), if we do B <- A A and B would look like the BOTH take up N bytes, for a total of 2N, whereas AFAIK R would only be using ~ N + 2*56 bytes, right? ~G On Tue, Sep 29, 2015 at 2:42 PM, Herv? Pag?s <hpages at fredhutch.org> wrote: > Hi, > > Currently object.size()

Dear R developers, I've run into a weird behavior of the numericDeriv function (from the stats package) which I also posted on StackOverflow (question has same title as this email, except for the version of R). Running the code bellow we can see that the numericDeriv function gives an error as the derivative of x^a wrt a is x^a * log(x) and log is not defined for negative numbers. However,

Hi. I'm trying to perform what should be a reasonably basic analysis of some spatial presence/absence data but am somewhat overwhelmed by the options available and could do with a helpful pointer. My researches so far indicate that if my data were normal, I would simply use gls() (in nlme) and one of the various corSpatial functions (eg. corSpher() to be analagous to similar analysis in SAS)

Dear all As far as I could trace, looking at the function C function numeric_deriv, this unwanted behavior comes from the inner most loop in, at the very end of the function, for(i = 0, start = 0; i < LENGTH(theta); i++) { for(j = 0; j < LENGTH(VECTOR_ELT(pars, i)); j++, start += LENGTH(ans)) { SEXP ans_del; double origPar, xx, delta; origPar = REAL(VECTOR_ELT(pars, i))[j];

Dear list, I have a problem with nested functions and I don't manage to get it solved. I know I should be looking in environments, and I have tried a lot, but it keeps on erroring. An easy version of the problem is as follows: innerfunction<-function() { print(paste(a, " from inner function")) print(paste(b, " from inner function")) setwd(wd) }

While using parallelization R seems to clone all environments (that are normally passed by reference) that are returned from a child process. In particular, consider the following example: library(parallel) env1 <- new.env() envs2 <- lapply(1:4, function(x) env1) cl<-makeCluster(2, type="FORK") envs3 <- parLapply(cl, 1:4, function(x) env1) envs4 <- parLapply(cl, 1:4,

Hi there, as a student sociology, I'm starting to learn about SEM. The course I follow is based on LISREL, but I want to use the SEM-package on R parallel to it. Using LISREL, I found it to be very usable to be able to see the total direct and total indirect effects (standardized and unstandardized) in the output. Can I create these effects using R? I know how to calculate them

Hi. contrary to other environments, the attributes of a *package* environment are dropped (from the new environment) when attach():ing it to the search() path. This might or might not be surprising, but have some side effects if rearranging/attaching package environments. # Example - Regular environments env <- new.env() attr(env, "foo") <- "bar" print(env) ##

Hi, In fitting GAMs to assess environmental preferences, I use the part of the fit where the lower confidence interval is above zero as my criterion for positive association between the environmental variable and species abundance. However I like to plot this on the original scale of species abundance. To do so I extract the fit and SE using predict.gam. Lately I compared more

Hi all, I made sure that it's "env$sRes1$nPositionsOptimizedM" that's correct... not the "env$sRes1$nPositionsOptimized"... But it seems both point to the same memory area... This is very dangerous because I have used naming conventions such as: MyLongVariableNameForA MyLongVariableNameForB MyLongVariableNameForC ... ... Then if internally they are actually the