search for: nsteps

...panel.background = element_blank(), axis.title.x = element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y = element_text(angle = 90, hjust = 0.5)) And my data is this: > dput(data) structure(list(specie = c("gmx mdrun -gpu_id 1 -ntomp 16 -s benchMEM.tpr -nsteps 10000", "gmx mdrun -gpu_id 1 -ntomp 16 -s benchMEM.tpr -nsteps 10000", "gmx mdrun -gpu_id 1 -ntomp 16 -s benchMEM.tpr -nsteps 10000", "gmx mdrun -gpu_id 1 -ntomp 16 -s benchMEM.tpr -nsteps 10000", "gmx mdrun -gpu_id 1 -ntomp 16 -s MD_15NM_WATER.tpr -nsteps 10...

Hi, I have a data frame like this: > dput(df) structure(list(ID = 1:8, Type = c("gmx mdrun -ntmpi 8 -ntomp 1 -s benchPEP.tpr -nsteps 10000 -resethway", "gmx mdrun -ntmpi 8 -ntomp 1 -s benchPEP.tpr -nsteps 10000 -resethway", "gmx mdrun -ntmpi 8 -s benchPEP.tpr -nsteps 4000 -resetstep 3000", "gmx mdrun -ntmpi 8 -s benchPEP.tpr -nsteps 4000 -resetstep 3000", "gmx mdrun -ntmpi 8 -s benchPEP.tp...

...rkspace on the fly (I mean put some command, before the loop that call genoud(), to export the workspace in its actual state to other computers) 2) divide the task in the number of available computers in the network;e.g, if I've got my "localhost" and 3 computers more: n.comp <- 4 nsteps <- 1987 steps.c <- trunc(nsteps/n.comp) steps.c <- (1:n.comp)*steps.c steps.c <- c(steps.c[1:(n.comp-1)],nsteps) steps.i <- c(1,steps.c[-n.comp]+1) for(ic in 1:n.comp){ Somehow start remotely R, read the copied workspace and execute in computer ic for(i in steps.i[ic]:steps.c[ic])...

Hi, I am trying to simulate animal movement in a gridded landscape made up of cells. At each time step (iteration), the animal moves from one cell to another in a random fashion. This is how I am simulating movement, where a and b are the x,y co-ordinates of the animal at the previous time step: for (i in 1:no.of.steps){ direction <- sample(1:8, 1) if(direction == 1){ a <- a b <- b -

...ot;, file=0xb7dc7f11 "gconv_db.c", line=232, function=0xb7dc7f36 "__gconv_release_step") at assert.c:78 #4 0xb7cd4054 in __gconv_release_step (step=<value optimized out>) at gconv_db.c:232 #5 0xb7cd40eb in __gconv_close_transform (steps=0x8401f30, nsteps=2) at gconv_db.c:791 #6 0xb7cd3dbe in __gconv_close (cd=0x8412a50) at gconv_close.c:64 #7 0xb7cd338c in iconv_close (cd=0x0) at iconv_close.c:36 #8 0x0829106d in smb_iconv_close (cd=0x8412a28) at lib/iconv.c:315 #9 0x08265ca0 in charset_name (ch=CH_DISPLAY) at lib/charcnv.c:82 #10 0x082...

Dear wonderful R community, I've been creating color-coded concentric circles using the "points" function, but I just realized that what I would really like to do is draw color-coded concentric half-circles. (Because I want to communicate information about the diameters-- half-circles are sufficient to show diameters AND they leave ample room to one side of the figure to label

GENERAL REFERENCE ON NONLINEAR OPTIMIZATION? What are your favorite references on nonlinear optimization? I like Bates and Watts (1988) Nonlinear Regression Analysis and Its Applications (Wiley), especially for its key insights regarding parameter effects vs. intrinsic curvature. Before I spent time and money on several of the refences cited on the help pages for "optim",

Hello list-mailers, I'm trying to use the 'Tps' function from the 'fields' package and I can getting this error message: > fishplot <- Tps(nodes.catch, res2) Error in if ((il > 1) & (il < nstep.cv)) { : missing value where TRUE/FALSE needed In addition: Warning message: no non-missing arguments to min; returning Inf nodes.catch is a 1600x2 matrix and

...t;-ts(cbind(a,b),start=c(1.1,1.1), names=c("a","b")) fit.bvar <- szbvar(K, p = 1, lambda0=0.6, lambda=0.1, lambda3=2, lambda4=0.25,lambda5=0,mu5=0, mu6=0, prior=0) y <- matrix(c(rep(0,8),c(1.10,1.10,1.10,100.1,1.10,1.10,1.0,100.10)),ncol=2) h <- hc.forecast(fit.bvar,y,nsteps=8,burnin=300, gibbs=500,exog=NULL)

Dear peRsons, I have a Sweave document which demonstrates the usage of step() function. With current R version 2.5.0 the step() function was changed so that the heading of trace=TRUE output for each model is printed using command message(): if (trace) message("\nStep: AIC=", format(round(bAIC, 2)), "\n",

Saludos Cordiales Estoy tratando de obtener la descomposición de varianza para un modelo de vectores autorregresivos estructural pero el módulo vars no permite hacerlo de manera directa, sin embargo, encontré programaciones de funciones específicas para los vectores autorregresivos estructurales que se acoplan directamente al modelo VARs, alguien me puede decir en que parte debo incorporar la

I am trying to use optim() to minimize a sum-of-squared deviations function based upon four parameters. The basic function is defined as ... SPsse <- function(par,B,CPE,SSE.only=TRUE) { n <- length(B) # get number of years of data B0 <- par["B0"] # isolate B0 parameter K <- par["K"]

Dear all, I have tried to modify the code of MASS:::stepAIC.default(), dropterm() and addterm() to use AICc instead of AIC for model selection. The code is appended below. Somehow the calculations are still not correct and I would be grateful if anyone could have a look at what might be wrong with this code... Here is a working example: ## require(nlme) model1=lme(distance ~ age + Sex, data =

...rkspace on the fly (I mean put some command, before the loop that call genoud(), to export the workspace in its actual state to other computers) 2) divide the task in the number of available computers in the network;e.g, if I've got my "localhost" and 3 computers more: n.comp <- 4 nsteps <- 1987 steps.c <- trunc(nsteps/n.comp) steps.c <- (1:n.comp)*steps.c steps.c <- c(steps.c[1:(n.comp-1)],nsteps) steps.i <- c(1,steps.c[-n.comp]+1) for(ic in 1:n.comp){ Somehow start remotely R, read the copied workspace and execute in computer ic for(i in steps.i[ic]:steps.c[ic]){...