similar to: `as.data.frame.matrix()` can produce a data frame without a `names` attribute

>>>>> Davis Vaughan via R-devel >>>>> on Thu, 21 Mar 2024 15:10:29 -0400 writes: > Hi all, > I recently learned that it is possible for > `as.data.frame.matrix()` to produce a data frame with 0 > columns that is also entirely missing a `names` attribute, > and I think this is a bug: > ``` # No `names`, weird! >

Here is a possible BUG with as.data.frame() and/or [.data.frame which broke Michael Lapsleys RODBC-Code. Can anyone confirm it is a bug or a 'feature' of the prototype? tablename <- "abc" a <- as.data.frame(cbind("abc", 1:3)) b <- as.data.frame(cbind(tablename, 1:3)) # ok > a V1 V2 1 abc 1 2 abc 2 3 abc 3 # missing column name > b tablename 1

When syncing one backup system (running dirvish, but that's not really relevant) to a second long-term backup system which uses btrfs snapshots, I get the following output: rsync: link "/parasite.oob.telegraaf.net/vservers-var-www/tree/elo/var/www/lokaalx/openx-htdocs/etc/xmlcache/.svn/prop-base/cache_etc-changes_schema-tables-core-12934.bin.svn-base" (in backup-aquagirl) =>

Hi all, Had struggled in getting "Strata" in randomForest to work on this. Can I get randomForest for each of its TREE, to get ALL sample from some strata to build tree, while leaving some strata TOTALLY untouched as oob? e.g. in below, how I can tell RF to, - for tree 1 in the forest, to use only Site A and B to build the tree, while using the WHOLE Site C data for the oob error

I have a question on the output generated by randomForest in classification mode, specifically, the confusion matrix. The confusion matrix lists the various classes and how the forest classified each one, plus the classification error. Are these numbers essentially averages over all the trees in the forest? If so, is there a way I can get the standard deviation values out of the randomForest,

Just curious if anyone else has got this error before, and if so, would know what I could do (if anything) to get past it: > mtry <- tuneRF(training, trainingdata$class, ntreeTry = 500, stepFactor = 2, improve = 0.05, trace = TRUE, plot = TRUE, doBest = FALSE) mtry = 13 OOB error = 0.62% Searching left ... mtry = 7 OOB error = 1.38% -1.222222 0.05 Searching right ... mtry = 26

Hello! I think I am relatively clear on how predictor importance (the first one) is calculated by Random Forests for a Classification tree: Importance of predictor P1 when the response variable is categorical: 1. For out-of-bag (oob) cases, randomly permute their values on predictor P1 and then put them down the tree 2. For a given tree, subtract the number of votes for the correct class in the

Hi all, I've been using a custom summary function to optimise regression model methods using the caret package. This has worked smoothly. I've been using the default bootstrapping resampling method. For bagging models (specifically randomForest in this case) caret can, in theory, uses the out-of-bag (oob) error estimate from the model instead of resampling, which (in theory) is largely

Ok, it's taken me a while, but here's what seems to be the problem. In OSX I see a lock on the file. If that lock is ON, it disappears. But what is that lock and where did it come from.... Is it like a read only lock? On Thu, Mar 30, 2017 at 11:04 AM Curtis Vaughan <cavaughan at gmail.com> wrote: > Ok, here's what seems to be going on. When a JPG is put into a share it >

Reporting a possible inconsistency or bug in handling stringsAsFactors in as.data.frame.table() Here is a simple test > options()$stringsAsFactors [1] TRUE > x<-c("a","b","c","a","b") > d<-as.data.frame(table(x)) > d x Freq 1 a 2 2 b 2 3 c 1 > class(d$x) [1] "factor" >

Hello R-user! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) I tried rfImpute (randomForest) and as far as I understood should it replace NA`s using a proximity matrix: > set.seed(100000) > Subset5Imputed<-rfImpute(Sex~., data=Subset5) ntree OOB 1 2 300: 11.78% 12.36% 11.21% ntree OOB 1 2 300: 12.07% 12.64%

What seems to be the problem? I think you forgot to include a description of the issues you're seeing. On Sep 18, 2017 11:23 PM, <ygrishin-lists at mail2.ca> wrote: Hello looks like the device is not supported, the logs are as follows: --- # dmesg | grep nouve [ 2.789899] fb: switching to nouveaufb from VESA VGA [ 2.790060] nouveau 0000:01:00.0: NVIDIA GT218 (0a8d00b1) [

Hello everyone, We are making extensive use of the homedrive mapping fields in AD. (automagically mapping a drive letter to a UNC path without a logon script) Since we sp2 applied to our clients, none of them have been able to map their home drives to our samba servers. According to Microsoft's q-article, http://support.microsoft.com/default.aspx?scid=KB;EN-US;Q308580&, "this

Just out of curiosity, I took the default "iris" example in the RF helpfile... but seeing the admonition against using the formula interface for large data sets, I wanted to play around a bit to see how the various options affected the output. Found something interesting I couldn't find documentation for... Just like the example... > set.seed(12) # to be sure I have

now I am learining random forest and using random forest package, I can get the OOB error rates, and test set rate, now I want to get the training set error rate, how can I do? pgp.rf<-randomForest(x.tr,y.tr,x.ts,y.ts,ntree=1e3,keep.forest=FALSE,do.trace=1e2) using the code can get oob and test set error rate, if I replace x.ts and y.ts with x.tr and y.tr,respectively, is the error rate

[global] workgroup = NPC server string = Samba Server %v netbios name = Atlantica security = user map to guest = bad user dns proxy = no hide files = /~*/ hide unwriteable files = yes #============================ Share Definitions ============================== [homes] comment = Home Directories path = /home/%S valid users = %S browseable = no guest ok = no writable = yes

Generically, yes, the hardware is supported. On Mon, Sep 18, 2017 at 11:42 PM, <ygrishin-lists at mail2.ca> wrote: > I will update the thread as soon as I get the hardware back. It was > something serious like artifacts or no video in X so that I had to resort to > proprietary drivers. > So the question is whether this hardware is supported or not. > > > On 2017-09-18

Dear mailinglist members, I was wondering if there was a way to re-evaluate the instances of a tree (in the forest) again after I have manually changed a splitpoint (or split variable) of a decision node. Here's an illustration: library("randomForest") forest.rf <- randomForest(formula = Species ~ ., data = iris, do.trace = TRUE, ntree = 3, mtry = 2, norm.votes = FALSE) # I am

kernel use callback linked in panic_notifier_list to notice others when panic happens. NORET_TYPE void panic(const char * fmt, ...){ ... atomic_notifier_call_chain(&panic_notifier_list, 0, buf); } When xen aware this, it will call xen_reboot(SHUTDOWN_crash) to send out an event with reason code - SHUTDOWN_crash. xen_panic_handler_init() is defined to register on panic_notifier_list but

kernel use callback linked in panic_notifier_list to notice others when panic happens. NORET_TYPE void panic(const char * fmt, ...){ ... atomic_notifier_call_chain(&panic_notifier_list, 0, buf); } When xen aware this, it will call xen_reboot(SHUTDOWN_crash) to send out an event with reason code - SHUTDOWN_crash. xen_panic_handler_init() is defined to register on panic_notifier_list but