similar to: Interaction and factor ':'

_______________________________________________________________________________________ Hi, I was just wondering if there was any available R code that could handle general constrained optimisation problems. At the moment I'm using nlminb and optim, both of which allow box constraints on the parameters, but ideally I'd like to be able to specify more general constraints on the solution

_______________________________________________________________________________________ Hi, I'm writing up the online help for a package I'm developing (in-house only, sorry), and I've come across an odd glitch when trying to nest a list inside the "arguments" section of the .Rd file. I was just wondering if anyone could provide some insights. I'm using R 2.2.0 on

_______________________________________________________________________________________ Note: This e-mail is subject to the disclaimer contained at the bottom of this message. _______________________________________________________________________________________ Hi, I came across some rather unexpected behaviour the other day with assigning names and lists. Here's an example. > z

_______________________________________________________________________________________ Hi, When I create multiple graphs subsequent graphs overwrite previous graphs. How do I keep all my graphs in the current workspace (but not all on the same page) so I can scroll through them? thanks, Rohini Rohini Mulford Senior Research Analyst Technical Research Research &

Thomas, As F Harrel in a preceding message told me that you are the maintainer of Hmisc package, I write directly to you, with copy to the Rhelp list in case someone encountered the same problem and find some benefit in the response. I used quite often the function sas.get in Hmisc library which I is very, very useful. But, as trying to reuse it today it seems not to function anymore. I tried

# Hello R Users, # # I would like to fit a glm model with quasi family and # logistical link function, but this does not seam to work # with binary data. # # Please don't suggest to use the quasibinomial family. This # works out, but when applied to the true data, the # variance function does not seams to be # appropriate. # # I couldn't see in the # theory why this does not work. # Is

Dear All: I am interested in multinomial logit models (function multinon, library nnet) but I'm having troubles in choose whether to define the predictors as factors or not. I had posted earlier this example (thanks for the reply ronggui): worms<- data.frame(year= rep(2000:2004, c(3,3,3,3,3)),age=rep(1:3,5),

Dear all, I tried to use "step" function to do model selection, but I got an error massage. What I don't understand is that data as data.frame worked well for my other programs, how come I cannot make it run this time. Could you please tell me how I can fix it? ***************************************************************************************************

Full_Name: Hong Ooi Version: 2.10.0 OS: Windows XP Submission from: (NULL) (203.110.235.1) While trying to get summary statistics on a duration variable (the difference between a start and end date), I ran into the following issue. Using summary or quantile (which summary calls) on a difftime object takes an extremely long time if the object is even moderately large. A reproducible example:

_________________________________________________________________________= ______________ Note: This e-mail is subject to the disclaimer contained at the bottom of= =20this message. _________________________________________________________________________= ______________ Just a note on the reported bug; it seems to be related to having a NULL function body. # this works > f <-

Here is an example where?cbind?fails with an error when?check.names=TRUE?is set. data(airquality) airQualityBind =cbind(airquality,airquality,check.names =TRUE) ?I understand that?cbind?is a call to?data.frame?and the following works: airQualityBind =data.frame(airquality,airquality,check.names =TRUE) but I would like to understand why?cbind?throws an error. I asked this question on SO here:

If one estimates a model using multinom, is it possible to perform the omnibus LR test ( the analogue to omnibus F in linear models ) using the output from multinom ? The residual deviance is there but I was hoping I could somehow pull out the deviance based on just using an intercept ? Sample code is below from the CAR book but I wasn't sure how to do it based on that example. Thanks

What is a simple way to convert a factor into a matrix of dummy variables? fm<-lm(y~f) where f is a factor takes care of this in the estimation. I'd like to save the result of expanding f into a matrix for later use. Thanks. Charles -- Charles H. Franklin Professor, Political Science University of Wisconsin, Madison franklin at polisci.wisc.edu chfrankl at wisc.edu

Hi list, could anyone please educate me on the following: lst<-seq(47, 239, by=12) for(n in lst) { lower=n; upper=lower+10 for(i in lower+2 : upper) { print(paste(n, " i: ", i, " lower: ",lower, " upper :", upper)) } } does not stop when i = upper A while loop fixes this but, I still don't understand why the for loop doesn't stop

All: I am new to R and would like your help in identifying the appropriate process to follow in order to modify the output from an existing package. I've had difficulty finding an answer online, perhaps because I am using incorrect terminology. A package that I am using (mmlcr) invokes another package (multinom). An output of multinom is the standard errors, but this output is not

I have RedHat linux 9 with R 1.8. I'm estimating models with multinom with a dependent variable that has 3 different values. Sometimes the models run fine and I can understand the results. Sometimes when I put in another variable, I see an indication that the estimation did work, but then I can't get the summary method to work. It's like this: > votemn1 <-

Dear R users, Does anyone know if it is possible to use multinom to do a polychotomous fit using one categorical and one numeric variable as response. The doc. for multinom states that for formula , response can be K>2 classes. Is this 2 and more, or as I have understood it only greater than 2. I have tried fitting my data, but have only encountered error messages. On another note, Is it

Hi, I found that using different contrasts (e.g. contr.helmert vs. contr.treatment) will generate different fitted probabilities from multinomial logistic regression using multinom(); while the fitted probabilities from binary logistic regression seem to be the same. Why is that? and for multinomial logisitc regression, what contrast should be used? I guess it's helmert? here is an example

Hi, I am searching for methods to compare regression models with an ordered categorical response variable (polr versus multinom). The pattern of predictions of both methods (using the same predictor variables) is quite different and the AIC is smaller for the multinom approach. I guess polr has more strict premises for the structure of the response variable, which methods can be used to test for

Best R Help, I like to estimate a Multinomial Logit Model with 10 Classes. The problem is that the number of observations differs a lot over the 10 classes: Class | num. Observations A | 373 B | 631 C | 171 D | 700 E | 87 F | 249 G | 138 H | 133 I | 162 J | 407 Total: 3051 Where my data looks like: x1 x2 x3 x4 Class 1 1,02 2 1 A 2 7,2 1 5 B 3 4,2 1 4 H 1 4,1 1 8 F 2 2,4 3 7 D 1 1,2 0 4 J 2 0,9