search for: categorising

Dear all, I'm looking for a function comparable to switch, to categorize a continuous variable in a few levels. Off course that can be done with a series of ifelse statements, but that looks rather clumsy. I looked at switch, but couldn't figure out how to use it for this. I guess that's not possible, as it only works with characters or integers, not with intervals. Basically,

Dear All, R keeps crashing when I use fisher.test . I'm using it on 2 x c (where c varies from 2 to 7) table, the total number of counts is 918. It does work with some of the categorisations of the data, but it's failing on those categorisations where the Chi-squared approximation breaks down due to small expected counts. A conglomeration of categories is not feasible. Is my memory too

Hi, I have a mysql table with fields userid,track,frequency e.g u1,1,10 u1,2,100 u1,3,110 u1,4,200 u1,5,120 u1,6,130 . u2,1,23 . . where "frequency" is the number of times a music track is played by a "userid" I need to turn my 'frequency' table into a rating table (it's for a recommender system). So, for each user, I need to categorise the frequency of tracks

Here''s the scenario. I have a main tree-like table (actually it''s more of a hierarchy), where the entries (or at least some of them) can be categorised as one or more of a fixed number of types (prob about 6 poss types). So an entry could be a Type A and a Type D; a Type C, D, E; just a Type F; etc. Having a join table would seem like it might get rather expensive since

On 13 February 2013 19:37, Justin Holewinski <justin.holewinski at gmail.com> wrote: > Is anyone currently working on overhauling the command-line processing code? > I'm currently doing some work on the CommandLine library but I don't think my work is going to help you. I'm current working on... - Allowing declared command line options to be put into categories to allow a

Hi, I would like tranform a data.frame which has a structure similar to the: Col1 Col2 Col3 B1 1 0.3 B1 2 0.4 B1 3 0.3 B2 1 0.2 B2 2 0.2 B2 5 0.4 B2 6 0.1 B3 1 0.2 B3 2 0.1 B3 5 0.2 [Data Frame 1] Into the following structure (pivoting or transpose is being used sometimes to

I have some data with two categorises plus/minus (p53) and a particular time (Time) and the outcome is a continuous vairable (Result). I set up a maximum model. ancova <- lm(Result~Time*p53) > summary(ancova) .. Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.05919 0.55646 0.106 0.916 Time -0.02134 0.01785 -1.195 0.241 p53plus

How well suited is btrfs to low-end and high-end FLASH devices? Paraphrasing from a thread elsewhere: FLASH can be categorised into two classes, which have extremely different characteristics: (a) the low-end (USB, SDHC, CF, cheap ATA SSD); and (b) the high-end (SAS, PCIe, NAS, expensive ATA SSD). My own experience is that the low end (a) can have erase blocks as large as 4MBytes or more

On Friday 17 September 2004 13:52, RenE J.V. Bertin wrote: > Hello again, > > I am doing regressions (using panel.lmline() (and panel.abline( > rlm(...))) ) inside a panel method which I pass to bwplot(). > > What I would like to do is create a boxplot of categorised data > (binned on the independent variable), and superpose a regression line > which is calculated using the

Hi, I would like to ask for advice about best statistics method for my problem. I was done questionnaire about headache. My data are: Y - frequency of symptoms occur - times per month in range <0..30> (where 30 is daily and zero for never) and independent variables: X1 - sex - category {M,F} X2 - age - linear value X3 - type of work {administrative, manual, ...} ... Which model is

Dear List, I have a data set stored in the following format: > head(dat, n = 10) id sppcode abundance 1 10307 10000000 1 2 10307 16220602 2 3 10307 20000000 5 4 10307 20110000 2 5 10307 24000000 1 6 10307 40210000 83 7 10307 40210102 45 8 10307 45140000 1 9 10307 45630000 1 10 10307 45630600 41 >

Hi, I just thought of trying vmkit and checked out the source from the svn repo a few minutes back. The build is failing with the following error. llvm[4]: Compiling CGCleanup.cpp for Release+Asserts build CGCleanup.cpp:507:36: error: no member named 'getCaseSuccessor' in 'llvm::SwitchInst'; did you mean 'getSuccessor'?

Hi all... How do i set U32 to filter a port range, instead of a single port? In normal use: source port 80 we use: "... match ip sport 80 0xffff ..." - I know that is something about the 0xffff parameter.... I need to filter ports 1 ~ 1024 to a higher priority class... i tried with IPTABLES MARK and TC FW, but it''s not working.... (...) # iptables -t mangle -A PREROUTING -p

hi, I''ve a normal Category model implemented with acts_as_tree (name, parent_id) that has_many :products (name, category_id). What I''m asking for is a method to fetch every product that belongs fall into a particular category and its children, eg: if I''m asking for a root node (/category/show/1) I need to display every product that have category_id = 1 AND every product

Hi Tara, It seems that you categorise and count for each category. Could it be that the method you use puts everything that doesn't match the predefined categories in Other? I'm only guessing because without a minimal reproducible example it's difficult to do anything else. Best wishes Ulrik Rui Barradas <ruipbarradas at sapo.pt> schrieb am Do., 1. Juni 2017, 17:30: >

I am looking a risk factors for disease in cattle and am interested in modelling farm and sampling cluster as random effects (My outcome is positive or negative at the level of the farm). I am using R version 2.0.1 on a Mac and have identified glmmPQL as hopefully the correct function to use. I have run a couple of models using this but was hoping that you might be able to answer a few questions.

Dear all, Could someone point me to a function or algorithm to generate random bivariate binomial data? Some details about what I'm trying to do. I have a dataset of trees who were categorised as not damaged or damaged. Each tree is measured twice (once in two consecutive years). The trees can recover from the damage but the data is clearly correlated. As a (un)damaged tree is more likely

On 09/28/2016 06:43 AM, Jonathan Hunter via samba wrote: > On 28 September 2016 at 11:07, Sven Schwedas via samba > <samba at lists.samba.org> wrote: >> Okay, can we ban the troll already? > OK, here's a devil's advocate post from the other perspective :) Just > because someone's views don't align with ones own, doesn't make them a > troll. And it is

Have been working recently on a site that classifies posts using tags in taxonomies, so a post about the Oil spill in the Gulf of Mexico might be tagged 'Subscribers only' (access level), 'Barack Obama' (person), 'Tony Hayward' (person), 'BP' (company), 'Transocean' (company), 'Gulf of Mexico' (location). With some advice from Richard Boulton I

Check for leading or trailing spaces in the strings in your data. dput(dataset) would show them. Bill Dunlap TIBCO Software wdunlap tibco.com On Thu, Jun 1, 2017 at 8:49 AM, Ulrik Stervbo <ulrik.stervbo at gmail.com> wrote: > Hi Tara, > > It seems that you categorise and count for each category. Could it be that > the method you use puts everything that doesn't match the