similar to: help with likelihood contour plot

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

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)

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

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

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),

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

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

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

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 have a dataset with two variables (>20000 observations, two samples from same subject) and I used "kernSur" from library(Genkern) to get a estimated bivariate density and corresponding plots as follows: new.data.normal<-data.normal[!is.na(data.normal[,2]),] x<-new.data.normal[,2] y<-new.data.normal[,3] op <- KernSur(x,y, xgridsize=50, ygridsize=50,

New to R, and didn't find the following in the docs. Main use for R. to get feet wet, is to pipe in x,y,z data, one point per line, and plot it in 3d, like this: { echo 1,2,3 echo 2,4,6 ... } | R ... But functions like contour seem to require index vectors that are sorted. I am curious why, and how to convert. Also, I am curious whether any Lisp person has ever written a front end to R

I've just done a fresh lenny amd64 install, and taken the xm-debian.cfg example: http://people.debian.org/~joeyh/d-i/images/daily/netboot/xen/xm-debian.cfg and built a 32 bit lenny inside a 64 bit lenny dom0 # xm create -c xm-th2.cfg install=true install-installer=http://people.debian.org/~joeyh/d-i/images/daily/ When I try to boot the domU: # xm create -c xm-debian.cfg it

Dear all, I want to solve a score function by using Newton-Raphson algorithm. Is there such a fucntion in R? I know there's one called optim, but it seems only doing minimizing or maximizing. Thanks, Jimmy

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 all, Are there any packages can estimate the partial linear model. Or any one can give me any suggestions. Many thanks in advance. Jin

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-ext doc say, but it still does not work. it is interesting that if i put the R code in the main

Hello R people, i'm trying to implement the Partial Least Squares algorithm called SAMPLS from "J.Comp-Aided Molecular Design", 7 (1993), 587-619. It's faster than the classical PLS algorithm for fat matrix (m>>n). Here's the algorithm from the article of Bush B. L. and Nachbar R.B.: X is the matrix of explanatories proprieties (m*n) , y the matrix of responses, h

Hello I will make an interpolation of data which represents azimuth direction ( angle from north in clockwise direction) values. But there is a problem. Say, for instance, while 1 and 359 indicate somewhat same direction, interpolation puts values in the range from 1 to 359. What can I do to solve the problem ? Anything you offer ? thanks in advance Ahmet Temiz General Directory of