similar to: partial mantel tests "Ecodist"

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

Hello, I'm trying to illustrate the relationships between various trait and environment data gathered from a number of sites. I've created a GAM to do this: gam1=gam(trait~s(env1)+s(env2)+te(env1,env2)) and I know how to create a 3D plot using vis.gam. I want to be able to show points on the 3D plot indicating the sites that the data came from. I can do this on a 2D plot when there is one

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",

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

I would like to perform partial mantel tests on only within group values, with "between group" values assigned to NA. This is possible in package ncf partial.mantel.test, however this sues a different permutation to that used in ecodist.ecodist will not accept data with NA values, returning a "matrix is not square error. is it possible to perform this test in ecodist? many thanks

What is the size of the boostrapped subsample in ecodist mantel() thanks [[alternative HTML version deleted]]

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. 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 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

Hi, I am running a logistic regression model using lrm library and I get the following error when I run the command: mod1 <- lrm(death ~ factor(score), x=T, y=T, data = env1) Unable to fit model using ?lrm.fit? where score is a numeric variable from 0 to 6. LRM executes fine for the following commands: mod1 <- lrm(death ~ score, x=T, y=T, data = env1) mod1<- lrm(death ~

Dear Dr.Goslee and anyone may intrested in matrix manipulate, I am using your ecodist to do mantel and partial mantel test, I have locality data and shape variation data, and the two distance matrixs are given as belowings. When I run the analysis, it is always report that the matrix is not square, but I didn't know what's wrong with my data. Would you please help me on this. I am quite

I would like to put confidence intervals on a mantel corellogram they are already calculated in the pmgram object but I am unsure how I get the x value in order to plot them? package(ecodist) X<-1:100 Y<-rnorm(1:100) Z<-rnorm(1:100) XY<-dist(data.frame(X,Y)) YX<-dist(data.frame(Y,X)) my.mgram<-mgram(XY,XZ) plot(my.mgram) print(my.mgram) > print(my.mgram) $mgram