similar to: TukeyHSD model thing

Hi there, ============================= x=c(145,40,40,120,180, 140,155,90,160,95, 195,150,205,110,160, 45,40,195,65,145, 195,230,115,235,225, 120,55,50,80,45 ) y2=c( rep(as.character(1),5), rep(as.character(2),5), rep(as.character(3),5), rep(as.character(4),5), rep(as.character(5),5), rep(as.character(6),5) ) crd2=data.frame(x,y2) model1=aov(x~y2,data=crd2) TukeyHSD(model1)

Hello! I often plot average of y instead of all y values, since one can easily see the trend if there is to many points and/or x might be like 1, 2, 3, 4, 5, ... and you might get a cloud (which can also be informative) and/or columns of points. Anyway, learning with R i get stucked. I have the data in table burnins1, with the following names: names(burnins1) [1] "Model"

Dear all, I find some troubles about how to extact the row from csv. file by using if-statement condition. I want to extract the row if the rainfall is greater than the mean of rainfall and using the wrfta divided into 3 groups that's rainfall greater than mean -> group A ( create file group A_rain) -> groupB ( create file group B_rain)

hi, - big and ugly mail. If you don't like them, delete it now :-) - I have collected and classified some information of: http://home.earthlink.net/~rwhron/kernel/bigbox.html And I observed that ext3 performance is worse than previous kernels(2.4.19...). -ac and -aa are here only as reference. Complete information is in the upper URL. dbench: Performance is worse. dbench (Numbers are in

hi Michael, am sorry for the incomplete reply . csv file data havinmg like this , >getSymbols("IBM") >weekly_data = to.weekly(week_name) >dataframe=data.frame(weekly_data) >outputfile_name="F:\\R-programs\\Outputfile.csv" >write.table(dataframe, file =outputfile_name,sep = ",",col.names =TRUE,row.names = T) > datafrom_table <-

On 18/09/17 17:23, Ben Turner wrote: > Do you want tuned or untuned? If tuned I'd like to try one of my tunings for metadata, but I will use yours if you want. (Re-CC'd list) I would be interested in both, if possible: To confirm that it's not only my machines that exhibit this behaviour given my settings, and to see what can be achieved with your tuned settings. Thank you!

In R 2.8.0 on Windows (tested both under ESS and under R Console in case there was an I/O issue) There is a bug in printing val <- matrix(list(NA_complex_,NA_complex_),1). > dput(val) structure(list(NA_complex_, NA_complex_), .Dim = 1:2) > print(val) [,1] [1,] [,2] [1,] Note that a large number of spaces are printed instead of NA. Compare the unproblematic real case:

New user here. My goal is pull daily averages from a long dataset. I've been working with some code I got from this list from https://stat.ethz.ch/pipermail/r-help/2009-March/191302.html The code how I have been using it is as follows: library(zoo) library(chron) DB<-read.table("/Users/me/Desktop/R/data.csv", sep=",", header=TRUE, as.is =TRUE) z<-zoo(LTER6$temp,

Dear R-helpers, an rgl-ers in particular, what is the easiest way to plot a section of a plane in 3D, that is given by the xyz coordinates of the outline? Suppose I have a polygon - which I know for sure is a set of coordinates on the same plane. One method I found is to use surf.tri from the geometry package, and then plot the triangles with rgl.triangles. This method is not perfect though,

Thanks everyone for you help with my last question, and now I have one last one... Here is a sample of my data in .csv format site,time_local,time_utc,reef_type_code,sensor_type,sensor_depth_m,temperature_c 06,2006-04-09 10:20:00,2006-04-09 20:20:00,BAK,sb39, 2, 29.63 06,2006-04-09 10:40:00,2006-04-09 20:40:00,BAK,sb39, 2, 29.56 06,2006-04-09 11:00:00,2006-04-09 21:00:00,BAK,sb39, 2, 29.51

Thanks for everyone's input so far, it is greatly appreciated. But I've got one last task I could use some advice on Here are the first few lines of my data set: site,time_local,time_utc,reef_type_code,sensor_type,sensor_depth_m,temperature_c 06,2006-04-09 10:20:00,2006-04-09 20:20:00,BAK,sb39, 2, 29.63 06,2006-04-09 10:40:00,2006-04-09 20:40:00,BAK,sb39, 2, 29.56 06,2006-04-09

Dear Rusers, I have met a difficult problem on explaining the differences of principal component analysis(PCA) between R,S-PLUS and SAS/STATA/SPSS, which wasn't met before. Althought they have got the same eigenvalues, their coeffiecients were different. First, I list my results from R,S-PLUS and SAS/STATA/SPSS, and then show the original dataset, hoping sb. to try and explain it.

Hello, Using the following lines of code, I created the following graph:

Hello, Using the following lines of code, I created the following graph:

On Sat, 2011-10-29 at 15:16 -0500, Hal Finkel wrote: > On Sat, 2011-10-29 at 14:02 -0500, Hal Finkel wrote: > > On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > > > Ralf, et al., > > > > > > Attached is the latest version of my autovectorization patch. llvmdev > > > has been CC'd (as had been suggested to me); this e-mail contains > >

On Sat, 2011-10-29 at 14:02 -0500, Hal Finkel wrote: > On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > > Ralf, et al., > > > > Attached is the latest version of my autovectorization patch. llvmdev > > has been CC'd (as had been suggested to me); this e-mail contains > > additional benchmark results. > > > > First, these are preliminary

On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > Ralf, et al., > > Attached is the latest version of my autovectorization patch. llvmdev > has been CC'd (as had been suggested to me); this e-mail contains > additional benchmark results. > > First, these are preliminary results because I did not do the things > necessary to make them real (explicitly quiet the

Hello, I have a 32 GB RAM Mac Pro with a 2*2.4 GHz quad core processor and 2TB storage. Despite this having so much memory, I am not able to get R to utilize much more than 3 GBs. Some of my scripts take hours to run but I would think they would be much faster if more memory is utilized. How do I optimize the memory usage on R by my Mac Pro? Thank you! Kurinji [[alternative HTML version

Ralf, et al., Attached is the latest version of my autovectorization patch. llvmdev has been CC'd (as had been suggested to me); this e-mail contains additional benchmark results. First, these are preliminary results because I did not do the things necessary to make them real (explicitly quiet the machine, bind the processes to one cpu, etc.). But they should be good enough for discussion.

List users, Using Asterisk in an inbound call center environment has led us to pushing the limits of vertical scaling. In order to treat each caller fairly and to utilize our agents as efficiently as possible, it is desirable to configure each client as a single queue. As far as I know, Asterisk's queues cannot be distributed across servers, so the size of the largest queue we service