search for: tarpinian

I apologize if this has been addressed before; recently I read an article in Forbes which discussed how SCO was going after companies that have been using Linux. The article made the point that the ideas behind GPL are under attack precisely because no one is making sure that the code being put into the freely avail. packages isn't owned by someone else. Here's my question: Is R

I have a question that must have a simple answer (but eludes me). I need a row-by-row logical comparison across three numeric variables in a data frame: foo$x, foo$y, foo$z. The logic is if( x < y || x > z ) 1 else 0 for a particular row. It is simple and very inefficient to use for(i in 1:length(foo$x)){ } loops. How can I accomplish this using sappy( ) / lapply( ) / apply( ) or

I apologize if this question is not completely appropriate for this list. I have been using SAS for a while and am now in the process of learning some C and R as a part of my graduate studies. All of the statistical packages I have used generally yield p-values as a default output to standard procedures. This week I have been reading "Testing Precise Hypotheses" by J.O. Berger

I am a relative newcomer to both the R and C/C++ software worlds -- I'm taking a C Programming class currently. I noticed the other day that the C:\Program Files\R1_8_1\src\include\R_ext directory on my WinXP box has the header files BLAS.h Lapack.h Linpack.h RLapack.h I am interested in (perhaps) using one or more of these header files in a straight C program I'm working on in Visual

Greg Tarpinian wrote: > I apologize if this question is not completely appropriate for this > list. > > I have been using SAS for a while and am now in the process of > learning some C and R as a part of my graduate studies. All of the > statistical packages I have used generally yield p-valu...

I have been learning about some outlier tests -- Dixon and Grubb, specifically -- for small data sets. When I try help.start() and search for outlier tests, the only response I manage to find is the Bonferroni test avaiable from the CAR package... are there any other packages the offer outlier tests? Are the Dixon and Grubb tests "good" for small samples or are others more

R2.3, WinXP Dear all, I am using the following functions: f1 = Phi1+(Phi2-Phi1)/(1+exp((log(Phi3)-log(x))/exp(log(Phi4))) f2 = Phi1+(Phi2-Phi1)/(1+exp((log(Phi3)-log(r)-log(x))/exp(log(Phi4))) subject to the residual weighting Var(e[i]) = sigma^2 * abs( E(y) )^(2*Delta) Here is my question, in steps: 1. Function f1 is separately fitted to two different datasets corresponding to

R 2.2, WinXP. I am having problems getting the right kind of xyplot( ) to be generated. The first of these works fine, but doesn't overlay a reference grid (which I need): xyplot(Y ~ X | Factor1, type = 'b', groups = GROUP, col = c(1,13), pch = c(16,6), lty = 1, lwd = 2, cex = 1.2, data = FOO.Frame, between = list(x = .5, y = .5), scales = list(alternating = TRUE)) The second

R2.2, WinXP: I am using xyplot( ) to generate time plots of plasma concentration data. The following is an edited version of my code: xyplot(log.conc ~ time| group, data = foo, groups = subject, panel = function(x, y, panel.number, ...) { panel.superpose(x, y, subscripts = TRUE, groups = foo$group, type = 'l', col

All, I have been reading Dr. Harrell's excellent "Regression Modeling Strategies" book and trying out the exercises. I understand that contrast( ) is used to obtain contrasts between two variables for given levels of other nuisance variables; is there a way to use contrast( ) to obtain, for example, Scheffe confidence intervals / hypothesis tests for many post hoc contrasts at

Hello, I am using WinXP, R version 2.3.1, and SAS for PC version 8.1. I have mostly used SAS over the last 4 years and would like to compare the output of PROC DISCRIM to that of lda( ) with respect to a very specific aspect. My data have k=3 populations and there are 3 variates in the feature space. When using using the code PROC DISCRIM DATA = FOO OUT = FOO_OUT OUTSTAT = FOOSTAT

NOTE: WinXP, R2.2.0 All, a while back I posted a question about using relative filereferencing. The responses have allowed me to successfully set up the following directory structure: ...\data\raw ...\data\derived ...\prog ...\lst ...\log In the \prog directory I have put an RGui.exe shortcut and "pointed it" at \prog as the "Start In" location. In the same

R2.2 for WinXP, Splus 6.2.1 for HP 9000 Series, HP-UX 11.0. I am trying to get a handle on why the same lme( ) code gives such different answers. My output makes me wonder if the fact that the UNIX box is 64 bits is the reason. The estimated random effects are identical, but the fixed effects are very different. Here is my R code and output, with some columns and rows deleted for space

All, I have been able to successfully use the optim( ) function with "L-BFGS-B" to find reasonable parameters for a one-compartment open pharmacokinetic model. My loss function in this case was squared error, and I made no assumptions about the distribution of the plasma values. The model appeared to fit pretty well. Out of curiosity, I decided to try to use nlminb( ) applied to a

Hello list, I am dealing with a noisy function (gradient,hessian not available) with simple boundary constraints (x_i>0). I've tried constrOptim() using nelder mead to minimize it but it is way too slow and the returned results are not satisfying. simulated annealing is so hard to tune and it always crashes R program in my case. I wonder if there are any packages or functions can do

The following were the replies to my question about using R's LAPACK and other .h files in some of my C programs. From what was said, it appears that buying a ready-made library (MKL = $200, for example) or using CLapack according to the Shumway lecture notes are the best approaches: -------------------------------- >From Andy Liaw: (1) If you just want linear algebra routines in your C

All, In looking at `optim', it doesn't appear that it is possible to impose nonlinear constraints on Nelder- Mead. I am sufficiently motivated to try to code something in C from scratch and try to call it from R.... Does anyone have some good references to barrier and/or penalization methods for Nelder-Mead? I would ideally like some papers with pseudocode for method(s) that are in

I have successfully fitted the model loess.fit1 <- loess(response ~ X*Y) and plotted it in 3D using X.grid <- seq(0,10,length=100) Y.grid <- seq(0,1000,length=100) pred.loess1 <- predict(loess.fit1, expand.grid(x = X.grid, y = Y.grid)) persp(X.grid, Y.grid, pred.loess1, theta = 0, phi = 12) I would like to add a series of points along the fitted surface at X.grid =

All, I have been trying to get the following code to work: A.quantiles <- quantile(foo.frame$A, probs = seq(from = 0.05, to = 0.95, by = 0.05)) base.quantiles <- quantile(Efficacy205$BASELINE_RANK, probs = seq(from = 0.05, to = 0.95, by = 0.05)) gender <- levels(Efficacy205$GENDER) contrast.1 <- contrast(Model.1, list(TPCODE= 'A', AGE =

All, I have been learning about mixed models and have been able to successfully use lme( ) and nlme( ) to fit some simple linear and 4PL logistic models. As a relative "newbie" I am at a loss as to how I can do the following: (1) Import a SAS dataset with DATE9. formatted time values and get them converted into a convenient time variable for use with the nlme package. In