similar to: R/S-Plus equivalent to Genstat "predict"

Hi all I'm doing some things with a colleague comparing different sorts of models. My colleague has fitted a number of glms in Genstat (which I have never used), while the glm I have been using is only available for R. He has a spreadsheet of fitted means from each of his models obtained from using the Genstat "predict" function. For example, suppose we fit the model of the type

Does anyone know if there is a package or other method of reading Genstat files directly into R. Genstat isn't listed in the foreign package. Many thanks, Graham [[alternative HTML version deleted]]

Hello everybody, I have a question. I would like to get a correlation between constitutive and induced plant defence which I messured on 30 plant species. So I have table with Species, Induced defence (ID), and constitutive defence (CD). Since Induced and constitutive defence are not independant (so called spurious correlation) I should do a randomisation test. I have a syntax of my

Greetings. I've written a convenience function for multicollinearity diagnosis. I'd like to report to the user the formula that is used in a regression. I get output like this: > mcDiagnose(m1) [1] "The following auxiliary models are being estimated and returned in a list:" [1] "`x1` ~ ." formula(fmla)() [1] "`x2` ~ ." I'd like to fill in the period

Hi all I am converting an S-Plus function into R. The S-Plus code uses some of the glm families, and family objects. The family objects in S-Plus and R have many different features, for example: In R: > names(Gamma()) [1] "family" "link" "linkfun" "linkinv" "variance" [6] "dev.resids" "aic"

Using platform x86_64-pc-linux-gnu arch x86_64 os linux-gnu system x86_64, linux-gnu status major 2 minor 13.0 year 2011 month 04

Dear r-devel list members, I've recently encountered the following problem using predict() with a model that has raw-polynomial terms. (Actually, I encountered the problem using model.frame(), but the source of the error is the same.) The problem is technical and concerns the design of poly(), which is why I'm sending this message to r-devel rather than r-help. To illustrate:

Many thanks for your very useful comments and suggestions. Renaud 2006/5/30, Prof Brian Ripley <ripley at stats.ox.ac.uk>: > On Tue, 30 May 2006, Prof Brian Ripley wrote: > > > This is not really a bug. See > > > > http://developer.r-project.org/model-fitting-functions.txt > > > > for how this is handled in other packages. All model-fitting in R used =

Full_Name: Renaud Lancelot Version: Version 2.3.0 (2006-04-24) OS: MS Windows XP Pro SP2 Submission from: (NULL) (82.239.219.108) I think there is a bug in predict.lme, when a polynomial generated by poly() is used as an explanatory variable, and a new data.frame is used for predictions. I guess this is related to * not * using, for predictions, the coefs used in constructing the orthogonal

Hello Timo, With dovecot 2.1.rc1 (056934abd2ef) there seems to be something wrong with virtual plugin mailbox search patterns. I'm using a virtual mailbox 'unread' with the following dovecot-virtual file $ cat dovecot-virtual * unseen For testing propose I created the following folders with each containing one unread message INBOX, INBOX/level1 and INBOX/level1/level2

Hi all, I analysed my data with lme and after that I spent a lot of time for mean separation of treatments (post hoc). But still I couldn’t make through it. This is my data set and R scripts I tried. replication fertilizer variety plot height 1 level1 var1 1504 52 1 level1 var3 1506 59 1 level1 var4 1509 54 1 level1 var2 1510 48 2 level1 var1 2604 47 2 level1 var4 2606 51 2 level1 var3

> How would one express such semantics in LLVM IR with this intrinsic? By definition, %data anmd %passthrough are different IR virtual registers and there are no copy instructions in LLVM IR. You never need to express this semantic in LLVM IR, because in SSA form they are always different SSA defs for the result of the operation versus the inputs to the operation. Someplace late in the CG

I'm making some functions to illustrate regressions and I have been staring at termplot and predict.lm and residuals.lm to see how this is done. I've wondered who wrote predict.lm originally, because I think it is very clever. I got interested because termplot doesn't work with interactive models: > m1 <- lm(y ~ x1*x2) > termplot(m1) Error in `[.data.frame`(mf, , i) :

>> One clear moral seems to be don't do anything more inside a >> generic function than you really need to do. Keep it *very* >> simple indeed. >> > I recall JMC saying something like, all generic functions > should be one line long; a call to the appropriate UseMethod. It certainly is encouraging to know that others also have been confused by aspects of

Robin, the initial output from apply() is always in the form you have below, but if it can be 'simplified' into a structure like the matrix, it does so. The same thing happens with sapply(). If you want to produce a nice matrix as the out put you have to ensure that the simplification is possible. Here is one way. > apply(x2, 2, function(x, v) table(factor(x, levels=v)),

Does lme prediction work correctly with poly() terms? In the following simulated example, the predictions are wildly off. Or am I doing something daft? Milk yield for five cows is measured weekly for 45 weeks. Yield is simulated as cubic function of weekno + random cow effect (on intercept) + residual error. I want to recover an estimate of the fixed curve. ############### library(nlme)

Hello list, A change in smbclient between 3.0.24 and 3.0.25c breaks [1] the gollem filemanager [2] and maybe others. In particular folder creation is broken unless the folder is a top level folder on the share. To create a subfolder "level2" in the folder /level1 of a share gollem executes this command: /usr/bin/smbclient "//1.2.3.4/sharename" "-p139"

Hi R users, I am currently using c(...,recursive=TRUE) to handle list-structured objects. This allows to represent something like: > l1 = list(level1=1,level2=list(sub1=1,sub2=2)) as: > (l1names = names(c(l1,recursive=TRUE))) [1] "level1" "level2.sub1" "level2.sub2" Then, one can use: > (l1names = sapply(l1names,FUN=function(element)

Dear R experts, I am observing undesired behavior of predict(fit, newdata), in case when fit object is produced by lm() involving a poly(). Here is how to reproduce: x <- c(1:10) y <- sin(c(1:10)) fit <- lm(formula=y~poly(x, 5, raw=TRUE)) predict(fit, newdata=data.frame(x=c(1:10))) ## this works predict(fit, newdata=data.frame(x=c(1:1))) ## this is broken, error below Error in poly(x,

Dear Dieter, sorry for not being more specific. I would like to use R to get a prediction (with standard error) of the response in a mixed model at selected values of the fixed-effects factors. Hence, in a mixed model, say, for response body size with, say, fixed factors sex and age, I would like to get a prediction of size for each sex and at selected ages such as 5, 10, 15; and I want a SE