similar to: multicollinearity in nlme models

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 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

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 <-

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: 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

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-help, I'm very sorry to ask 2 questions in a week. I am using the package 'crr' and it does exactly what I need it to when I use the dataset a. However, when I use dataset b I get the following error message: Error in drop(.Call("La_dgesv", a, as.matrix(b), tol, PACKAGE = "base")) : system is computationally singular: reciprocal condition number =

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 having trouble figuring out the right form for the nlme arguments. I do have examples in Modern and Applied Statistics with S and from other sources, but I still can't figure it out. I am trying to estimate species richness (sr) in streams across minnesota. My predictor variables are depth (d), habitat diversity (habdiv), drainage area (da) and an indicator variable representing the

I'll try to spell out the problem as best I can. There are three computers on a LAN, "ADS1", "ADS2" and "DRS2". ADS1 and ADS2 run samba-3.5.8-76.fc14.i686 under Fedora 14. DRS2 runs Windows XP professional, SP3. Each of them can successfully ping all the others by name. They are all in the same WORKGROUP (GAMMA5). The Fedora machines (ADS1, ADS2) can see all

Marc, et al, Thanks for your reply. Sorry, I didn't mean to be confusing. Summary of problem: 1) XP sees samba shares on fedora14/samba3 machine, but not on fedora20/samba4 machines. 2) all fedora machines see all shares including on XP machine. 3) clicking on f20 machine from XP tries to log in rather than showing the shares. Login is not successful. 4) all machines can ping each

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

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

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

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

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 (