similar to: R-1.3.0-1.3.1.diff does not patch correctly

[Sorry, In case this is repeating a message already sent to the list]. I am trying to move a project to R (base or nlme), for which I have a partial solution in BMDP 8V. Here is the 8V control language: /input title='Augenbewegungen'. variables=4. file='latm.dat'. format='11x,f7.0,f7.0,f12.4,f10.0'. /variables names= llat,rlat,vg,diff. /design

I tried to reproduce a result from a former colleague which he got with S-plus bootstrap method. I don't have S-plus at hand. In R, there are 2 packages related to bootstrap method, bootstrap and boot. The former has a function called 'bootstrap' but this does not seem to conform either to the function used in S-plus nor to that described in MASS, 3d ed., p.144. The latter seems to be

I just detected, that det() is not working on complex matrices any more, due to the fix to the bug reports noted above. I am not happy with this, as determinants are perfectly usable on complex matrices. AFAIUI the bugs resulted from less than optimal behaviour of qr() in certain cases. IMHO this is due to the unhappy decision to use a default for parameter tol to decide whether the the

I am trying to translate a given BMDP 8V problem treatment to something approximately equivalent in nlme package (or base R, if sufficient). (Of course, it is not my goal to end there. I want to get access to the advanced flexibility of a more modern treatment.) Here is the problem statement in BMDP control language: /input title='Augenbewegungen'. variables=4.

As the subject says, I am unhappy with R's aov performance. I have a data set containing 25000 cases. This causes thrashing even with very moderate formulae, because the model matrix has quite a lot of lines. The study has 9x3x2x2x2 (or so) design factors. Is there a recommended method for pre-condensing the data before inputting them into aov in R? I want to be able to preserve the person

I have a problem moving from multistratum aov analysis to lmer. My dataset has observations of ampl at 4 levels of gapf and 2 levels of bl on 6 subjects levels VP, with 2 replicates wg each, and is balanced. Here is the summary of this set with aov: >> summary(aov(ampl~gapf*bl+Error(VP/(bl*gapf)),hframe2)) > >Error: VP > Df Sum Sq Mean Sq F value Pr(>F) >Residuals

Given a matrix B, where B=A'A, how can I find A? In other words, if I have a matrix B which I know is another matrix A times its transpose, can I find matrix A? Thanks, Mike

We get caught occasionally, and we all receive spam; sorry for the last one (on R-help). Of course, there will never be a 100% sure prevention... Question is what should happen when mail from R-help to an adressee is refused with the following message : At least I won't be able to easily send e-mail to them {and am not willing to use a non-easy way}.... ----- Transcript of session

Dear R-experts, Here below the reproducible example. I am trying to get the average of the 100 results coming from the "lst" function. I have tried lst$mean and mean(lst). It does not work. Any help would be highly appreciated. #################### ?## R script for getting MedAe and MedAeSQ from HBR model on Testing data install.packages("robustbase") install.packages(

Full_Name: W.B.Kloke Version: 1.9.1 OS: FreeBSD-5.2.1 Submission from: (NULL) (195.253.16.182) Subject line says it. I had problems installing lme4. 1. The dependency on package Matrix was not resolved (I am not sure that this is really a bug; but it is annoying, anyway). 2. Installing Matrix failed with a message saying something like "no rule for %_D.o" after compiling a lot of

On 5/8/2018 12:26 PM, varin sacha via R-help wrote: > > Dear R-experts, > > Here below the reproducible example. I am trying to get the average of the 100 results coming from the "lst" function. I have tried lst$mean and mean(lst). It does not work. > Any help would be highly appreciated > > #################### > > ?## R script for getting MedAe and

Full_Name: W.B.Kloke Version: 1.8.0 OS: FreeBSD-4.7 Submission from: (NULL) (195.253.22.63) I found a parameter evaluation inconsistency in NLME package. I tried to use gls() inside a function, and I wanted use gls() for different subsets of a data frame: prgls <- function(name){ gls( log10(Y)~(cond-1)+(cond-1):t ,pr,subset=subject==name)} Applying this function with a string as parameter

Is there a way in R (12.x) to avoid the implicit coercion of factors to integers in the context of subscripts? If this is not possible, is there a way to get at least a warning, if any coercion of this type happens, given that the action of this coercion is almost never what is wanted? Of course, in the rare case that as.integer() is applied explicitly onto a factor, the warning is not needed,

Ok do not know what is causing this problem and would appreciate some help in tracking down the cause. FreeBSD 4.7 System runs fine for maybe a week and then all networking stops - without warning. I do not know anyway of persuading it to resume networking without rebooting. Does anyone have some useful suggestions on how to diagnose the cause? David

Hi There, I have a problem with OggEnc v1.0.2. Can you tell me, where to put or to post a bug report? The problems eare with some speciel charakters - used in Europe (Denmark, Norway, Sweden, Germany, Austria, etc..) Some Examples: Hin Vordende Sod & S? ^ 02_Bruderov paa H?gstadtun.ogg ^ 03_Huldradans - Hin Gr?nnkledde.ogg ^

Yesterday I was biten by a feature, which I find too dangerous. I wanted to use a factor `Subject?? as index into a data frame, whose row names were the levels of this factor. So there a 2 different possible interpretations of this: Either Subject is coerced to numeric or to character. The intended interpretation was, of course, `as.character(Subject)'. R did `as.numeric(Subject)??. This will

Dear R-experts, I am trying to do cross-validation for different models using the cvTools package. I can't get the CV for the "FastTau" and "hbrfit". I guess I have to write my own functions at least for hbrfit. What is going wrong with FastTau ? Here below the reproducible example. It is a simple toy example (not my real dataset) with many warnings, what is important to

Dear R-experts, Doing cross-validation for 2 robust regressions (HBR and fast Tau). I can't get the 2 errors rates (RMSE and MAPE). The problem is to predict the response on the testing data. I get 2 error messages. Here below the reproducible (fictional example) R code. #install.packages("MLmetrics") # install.packages( "robustbase" ) # install.packages(

Dear R-experts, I guess I have a problem with my fast function (fast tau estimator) here below. Indeed, zero errors look highly suspicious. I guess there is a bug in my R code. How could I correct my R code ? # install.packages( "robustbase" ) # install.packages( "MASS" ) # install.packages( "quantreg" ) # install.packages( "RobPer" ) #

I am seeking for a method to calculate, given a fitted lme model and some data for a subject, the random effects predictors for this subject. I can only find predictors for the subjects used in creating the fit. Of course I could just add the subject and redo the fit. But I want to avoid just this refitting. Thanks for help wbk