search for: sasprog474

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

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

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

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

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

WinXP, Splus7 and R2.3.1. All, I have been using the SSfpl and SSlogis self-starting functions in the nlme library to fit 4PL and restricted 4PL models. I need to adapt these routines to fit the alternative model f(x) = A + (B-A)/(1 + abs(x/EC50)^C) My Question: How do I obtain good starting values for this alternative model? (The pseudo-code found on pages 517 - 520 of "Mixed

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