search for: ziem

I want to find the last record for each person_id in a data frame (from a SQL database) ordered by date. Is there a better way than this for loop? for (i in 2:length(history[,1])) { if (history[i, "person_id"] == history[i - 1, "person_id"]) history[i, "order"] = history[i - 1, "order"] + 1 # same person else history[i,

When I try to install the ROCR package (which requires gplots) on Ubuntu 9.10 (Xubuntu Karmic Koala) 64-bit on R version 2.9.2 (2009-08-24), it eats up all my RAM (2GB) and swap (4GB) and keeps allocating more memory until Linux's out of memory (OOM) killer kills the perl process. This problem is special to Ubuntu because I can install other packages (such as party) on this Ubuntu system, and

...or take a couple of incorrect inflexions. I.e. "Entities should > > not multiply unless necessary" -- do not invent more elaborate > > explanations than what is necessary to explain the facts. > > A little web search gave the following at > http://www2.jonction.net/~ziem/occam.html > > "Pluralitas non est ponenda sine neccesitate" > "Frustra fit per plura quod potest fieri per pauciora" > "Entia non sunt multiplicanda praeter necessitatem" > > The principle states that "Entities should not be multiplied &gt...

Like earth can be trained simultaneously for degree and nprune, is there a way to train ctree simultaneously for mincriterion and maxdepth? Also, I notice there are separate methods ctree and ctree2, and if both options are attempted to tune with one method, the summary averages the option it doesn't support. The full log is attached, and notice these lines below for

I extracting splitting rules from Greg Ridgeway's GBM 1.6-3.2 in R 2.15.2, so I can run classification in a production system outside of R. ?I have it working and verified for a dummy data set with all variable types (numeric, factor, ordered) and missing values, but in the titanic survivors data set the splitting rule for factors does not make sense. ?The attached code and log below explains

After ctree builds a tree, how would I determine the direction missing values follow by examining the BinaryTree-class object? For instance in the example below Bare.nuclei has 16 missing values and is used for the first split, but the missing values are not listed in either set of factors. (I have the same question for missing values among numeric [non-factor] values, but I assume the answer

I can create a temp table with MySQL and R DBI[1], but I don't see how to do the same with Microsoft SQL 2005 and RODBC. R 2.8.1 creates the table, but then it can never see it. I'm looking to avoid replacing the convenience functions like sqlSave(). [1] https://stat.ethz.ch/pipermail/r-help/2009-March/190033.html > library(RODBC) > channel <-

*Hi, *Does anyone know how can I show an *ROC curve for R-SVM*? I understand in R-SVM we are not optimizing over SVM cost parameter. Any example ROC for R-SVM code or guidance can be really useful. Thanks, Angel. [[alternative HTML version deleted]]

I can't run anything with Wine 20031016 and Mandrake Linux 9.1. Using default configuration. # rpm -q wine libwine1 perl wine-20031016-1mdk libwine1-20031016-1mdk perl-5.8.0-19mdk # rpm -V wine libwine1 perl Unsatisfied dependencies for wine-20031016-1mdk: perl(strict) .M...... c /etc/rc.d/init.d/wine S.5..... /usr/lib/perl5/5.8.0/i386-linux-thread-multi/signal.ph #uname -a Linux

Hi, When an mbox has a filename with a period, the Dovecot 1.0.1 convert plugin will endlessly convert the same mbox causing duplicate messages. To reproduce, create try converting two mboxes: one with a valid name and another with an invalid name. Then, repeat the login procedure a few times. Dovecot should do something more graceful such as replacing periods with another character.

Creating a temp table isn't completely intuitive with MySQL 5 and R 2.8..1, but it can be done. > library(RMySQL) Loading required package: DBI > con <- dbConnect(dbDriver("MySQL"), dbname = "foo", user="me",password="secret") > x<- data.frame(1:10) > colnames(x) <-c("x") > dbWriteTable(con, "#x", x,

What is the best way to produce a barplot from my data? I would like the barplot to show each person with the values stacked val1+val2+val3, so there is one bar for each person When I use barplot(data.matrix(realdata)), it shows one bar for each value instead. To post here, I created an artificical data set, but it works fine. fakedata <- as.data.frame(list(LETTERS[1:3])) colnames(fakedata)