search for: wak

I've made an LDA model on some data from one source. I have some new data that I want to see if I can "place" to the sources in the LDA model. I used the predict function as follows: predict(wak.insitu.ld, wak.alr.alluvial) where wak.insitu.ld is an LDA model generated from some data and wak.alr.alluvial is new data of similar origin. When I look at the results, there is 86 observations which is the number in the original model, NOT in the new data (nrow=53). Am I doing this correctly....

...mit is entirely based on gcc5. In order to do a test with gcc4.9, I reverted: https://github.com/triton/nixpkgs/commit/8ccc1f121f379f4d66ce0a66f581c49d25fb4e15#diff-d7222640d82ff920625e9311d05a0137 and then built two images, one entirely based on gcc4.9 and one entirely based on gcc5: https://pub.wak.io/nixos/nixos-minimal-new-kernel-gcc49-x86_64-linux.iso https://pub.wak.io/nixos/nixos-minimal-new-kernel-gcc5-x86_64-linux.iso The gcc4.9 one boots fine on all of my bios machines, but the gcc5 one does not even in the hyperv and virtualbox testing i did.

...ndering if there were other ways to get this speed improvement: it is otherwise quite difficult to explore such big matrices, especially given that X11 redraws the plot whenever its window is covered/uncovered by another window, or when I switch virtual desktops. Best regards, -- Charles Plessy, Wak?, Saitama, Japan

Dear R forum >From the given data, I have estimated the parameters of Wakeby distribution using lmomco package as library(lmomco) (amounts <- read.csv("input_S.csv")$amount) # ___________________________________________________________ # Wakeby distribution - Parameter estimation N                      = length(amounts) lmr                    = lmom.ub...

Hi all, I recently found out that R does not utilize fully the Duo Core capability when you only run one instance of R. I did some number crunching today and it seems that if I only open 1 R console, it uses 50% of my CPU (either 50-50 or 100-0 on 2 cores). Then, I open the second instance and divide the work into two parts, and run them parallelly, they seems to utilize 100% of my CPU. So, my

Hi! I am using R version 2.7.0 and am working on a panel dataset read into R as a dataframe; I call it "ex". The variables in "ex" are: id year x id: a character string which identifies the unit year: identifies the time period x: the variable of interest (which might contain NAs). Here is an example: > id <- rep(c("A","B","C"),2) >

Test =) soddsjbmshdwqwu -- Test, yep.