similar to: About " Error: C stack usage is too close to the limit"--resend

Hi everyone! I meet one problem when embedding R in C code, when I run the the R code in one child thread , it always print error info: Error: C stack usage is too close to the limit I also try to set R_CStackLimit = (uintptr_t)-1 to disable the C stack check as the R-ext doc say, but it still does not work. it is interesting that if i put the R code in the main

Hi everyone! I meet one problem when embedding R in C code, when I run the the R code in one child thread , it always print error info: Error: C stack usage is too close to the limit I also try to set R_CStackLimit = (uintptr_t)-1 to disable the C stack check as the R-exts doc say, but it still does not work, the error info still exist. Besides it is interesting that if i

Dear R users; I'm looking for some hint about how to deal with the following situation: Response = Y Factor A = levels: 0, 1 Factor B = levels: 0, 1 Factor C = levels: 1,2,3,4 Model: Logistic 3-parms. where th1~1+A+C, th2~1+C; th3~1 For 'simplicity' (for me) I'm using the SAS contrast parameterization. The output looks like Beta p-value th1.(Intercept) 550

I am working on a nlme model that has multiple fixed effects (linear and nonlinear) with a nonlinear (asymptotic) random effect. asymporig<-function(x,th1,th2)th1*(1-exp(-exp(th2)*x)) asymporigb<-function(x,th1b,th2b)th1b*(1-exp(-exp(th2b)*x)) mod.vol.nlme<-nlme(fa20~(ah*habdiv+ads*ds+ads2*ds2+at*trout)+asymporig(da.p,th1,th2)+ asymporigb(vol,th1b,th2b),

I am examining the following nlme model. asymporig<-function(x,th1,th2)th1*(1-exp(-exp(th2)*x)) mod1<-nlme(fa20~(ah*habdiv+ad*log(d)+ads*ds+ads2*ds2+at*trout)+asymporig(da.p,th1,th2), fixed=ah+ad+ads+ads2+at+th1+th2~1, random=th1+th2~1, start=c(ah=.9124,ad=.9252,ads=.5,ads2=-.1,at=-1,th1=2.842,th2=-6.917), data=pca1.grouped) However, the two random effects (th1 and th2)

Dear all, I am using the function 'D' in the 'stats' package to perform symbolic derivation. This works very well and it is much faster than e.g. Mathematica (at least for my purposes). First, I would like to thank the development team for this excellent function. However, I run into trouble in some cases, particularly when I am to do some operations on long expressions

Can some kind person point out my error here? I'm trying to set up a grid for a countour plot of a likelihood function. > u <- rnorm(20,9.5,2.5) > # sample of size 20 from N(9.5,2.5^2) > loglik <- function(th1,th2) { + n <- length(u) + -(n/2)*log(2*pi*th2^2)-0.5*sum((u-th1)^2/th2^2) + } > x <- seq(4.5,14.5,len=50) > y <- seq(0.5,6,len=50) > f <-

Hi all I don't know if my message are correct in this forums. I create a program in c++ who use statistical function. I want to execute this function in R (in particular for use packages ade4, lattice, bioconductor...) Until now, my program work for simple function ("plot", "rnorm"...) but I can't use library My class are : // in constructor int argc = 1;

How to use "contour" function? I type "contour(th1,th2,SumofSquares,levels=c(seq(1000,4000,200)))" But I didn't see contour in the plot, I guess it is because my data is so big that contour is out of range. Does anybody know how to fix this problem? here th1 is a 1x101 array(158741.8-298529.6) th2 is another 1X101 array(-0.0058352269-0.2093140935) SumofSquares is a

Hi everyone, I meet the problem to call R from C. Can someone tell me is it possible to call time series functions(such as ar and arima)from C program on linux platform, and then compile the C program to a standalone executable file? I know we can call some function defined in Rmath.h , but I have not found the entry point for time series function. I would very much appreciate

Hello! This is my first post here. I came across the very same problem. It can be reproduced within modified tests/Embedding/RParseEval.c Actually this example has another issue, namely it doesn't wrap everything in R_ToplevelExec . This is a major show stopper for newcomers as that function is barely mentioned anywhere and longjmp into terminated setuploop function followed by R_suicide

On 4/5/19 8:14 AM, Mikhail Titov wrote: > Hello! > > This is my first post here. I came across the very same problem. > It can be reproduced within modified tests/Embedding/RParseEval.c Please check https://www.r-project.org/posting-guide.html and update your post if you still need to get help here - from your current post I am not sure what you did, what was the error you got and

Hi: I'm trying to estimate a latent variable model in mnl discrete choice framework using R. I need to do first a uni dimensional integral within each observation (row) in the database and then sum over observations. I'm stacked in the point shown below. Apparently I have a dimensionality problem in the definition of the integral. Maybe it does not identify that what I need is only one

Hi: I'm trying to estimate a model which involves the estimation of double integrals, so I'm using adapt procedure. I need to calculate the integrals trough my 2000 size database, so I do it using a loop. My code correctly estimates the integral for the first row, but for the second R crashes. I tried changing the order of the data but the result is the same so I guess there is something

On Wed, Apr 10, 2019 at 5:06 AM, Tomas Kalibera <tomas.kalibera at gmail.com> wrote: >> This is my first post here. I came across the very same problem. >> It can be reproduced within modified tests/Embedding/RParseEval.c > > Please check https://www.r-project.org/posting-guide.html and update > your post if you still need to get help here - from your current post >

Dear All, I am attempting to use forward and/or backward selection to determine the best model for the variables I have. Unfortunately, because I am dealing with patients and every patient is receiving treatment I need to force the variable for treatment into the model. Is there a way to do this using R? (Additionally, the model is stratified by randomisation period). I know that SAS can be

Hello, I'm trying to make a 3d plot using rgl in which the size and color of each point corresponds to certain attributes of each data point. The color attribute, let's call it X, is scaled to go from 0 to 1. The rainbow(64,start=0.7,end=0.1) palette is perfect for what I want but I don't know how to take that palette and pick a color from it based on the value of X for a given data

Using the small reproducible example below, I'd like to know if one can somehow use the matrix "sig" (defined below) to add a black outline (with lwd=2) to all pixels with a corresponding value of 1 in the matrix 'sig'? So for example, in the ggplot2 plot below, the pixel located at [1,3] would be outlined by a black square since the value at sig[1,3] == 1. This is my first

Dear friends: It's possible make a legend in the coplot output? My data are: "dados"<-structure (list ( Qu1 = c(1,2,3,4,5,6,7,8,3,2), Qu2 = c(3,4,5,6,7,8,9,9,6,3), Ku1 = structure (factor ( c(1,1,1,1,2,1,2,2,1,1),levels=1:2),.Label=c("Kmin","Kmax")), Ph1 = structure (factor (

Hi Eric, you can use an annotate-layer, eg ind<-which(sig>0,arr.ind = T) ggplot(m1.melted, aes(x = Month, y = Site, fill = Concentration), autoscale = FALSE, zmin = -1 * zmax1, zmax = zmax1) + geom_tile() + coord_equal() + scale_fill_gradient2(low = "darkred", mid = "white", high = "darkblue",