similar to: Counting

Hi, I have two data frames (u and v). > u   coe      nam 1   0     Time 2   0    Poten 3   0   AdvExp 4   0    Share 5   0   Change 6   0 Accounts 7   0     Work 8   0   Rating > v       coeff    enter 1 0.7272727 Accounts 2 0.3211112     Time 3 0.0500123    Poten I want to update the values of coe in u by using the values ofcoeff in v. That is, I want to get the following result >

I have a 45 x 16 data frame consisting of dissimilarities among 10 colors, giving in each column the 45 = 10*9/2 pairwise judgments for one of 16 subjects. The rownames identify each pair of colors, e.g, "AC" = ("A","C"), and the pairs are ordered by columns in the lower triangle of each distance matrix. > helm.raw <-

Dear List-members, I found a strange behaviour in the lqs function. Suppose I have the following data: y <- c(7.6, 7.7, 4.3, 5.9, 5.0, 6.5, 8.3, 8.2, 13.2, 12.6, 10.4, 10.8, 13.1, 12.3, 10.4, 10.5, 7.7, 9.5, 12.0, 12.6, 13.6, 14.1, 13.5, 11.5, 12.0, 13.0, 14.1, 15.1) x1 <- c(8.2, 7.6,, 4.6, 4.3, 5.9, 5.0, 6.5, 8.3, 10.1, 13.2, 12.6, 10.4, 10.8, 13.1, 13.3, 10.4, 10.5, 7.7, 10.0, 12.0,

Hello, I am relatively new to R and I am working through the code that is provided in the book Numerical Ecology with R and I have run across an error message that I can't seem to figure out. I am using the vegan, ade4, gclus and cluster packages. The code is as follows: # Ordered community table # Species are ordered by their weighted averages on site scores or <- vegemite(spe,

Dear all, I am attempting to explain patterns of arthropod family richness (count data) using a regression model. It seems to be able to do a pretty good job as an explanatory model (i.e. demonstrating relationships between dependent and independent variables), but it has systematic problems as a predictive model: It is biased high at low observed values of family richness and biased low at

Startsituation: structure(c(1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1), .Dim = 4:5, .Dimnames = structure(list(subject = c("s1", "s2", "s3", "s4"), class = c("c1", "c2", "c3", "c4", "c5")), .Names = c("subject", "class")), class = c("xtabs",

Hola!! tengo un data.frame donde cada fila corresponde a un año y cada columna a un mes (De enero a diciembre) > head(valT) V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 1941 18.0 16.3 15.2 10.1 8.1 8.3 8.8 9.2 7.9 12.2 11.9 14.6 1942 17.2 15.9 13.6 11.6 8.7 6.2 6.4 7.2 9.7 12.0 14.1 16.7 1943 17.6 17.3 13.5 12.5 10.5 7.0 8.2 7.9 -999.9 -999.9

Hi, For example, the data.frame like: origdata.long <- read.table(header=T, con <- textConnection(' subject sex condition measurement 1 M control 7.9 1 M first 12.3 1 M second 10.7 2 F control 6.3 2 F first 10.6 2 F second 11.1 3 F control 9.5 3

Dear all, Suppose I have this data frame: > data_main V1 V2 foo 13.1 bar 12.0 qux 10.4 cho 20.33 pox 8.21 And I want to split the data into two parts first part are the one contain in the source array: > src [1] "bar" "pox" and the other one the complement. In the end we hope to get this two dataframes: > data_child1 V1 V2 bar 13.1 pox

On 03/25/2015 07:48 PM, Simon Urbanek wrote: > On Mar 25, 2015, at 3:46 PM, Valerie Obenchain <vobencha at fredhutch.org> wrote: > >> Hi Simon, >> >> I'm having trouble with nested parallel workers, specifically, forking inside socket connections. >> > > You simply can't by definition - when you fork *all* the workers share the same connection

Hello, It's an alternative to use SAS by function in R? I want to plot d histograms by plot.from example bellow: Thank you! plot d 1 1 16.3 2 1 25.0 3 1 57.8 4 1 17.0 5 2 10.8 13 2 96.4 17 3 76.0 18 3 32.0 19 3 11.0 20 3 11.0 24 3 106.0 25 3 12.5 21 4 19.3 22 4 12.0 26 4 15.0 27 5 99.3 32 7 11.0 36

? data=read.table("http://statcourse.com/research/boston.csv", , sep=",", header = TRUE) ? library(rpart) ? fit=rpart (MV~ CRIM+ZN+INDUS+CHAS+NOX+RM+AGE+DIS+RAD+TAX+ PT+B+LSTAT) predict(fit,data[4,]) plot only reveals part of the tree in contrast to the results on obtains with CART or C5 -------- Original Message -------- Subject: Re: [R] help with rpart From: Sarah

I have a dataframe with several different treatment variables, and would like to calculate the mean and standard deviation of the replicates for each day and treatment variable. It seems like it should be easy, but I've only managed to do it for one treatment at a time using subset and tapply. Here is an example dataset: > `exampledata` <- structure(list(day = c(1L, 1L, 1L, 1L, 1L,

Hi, Has anyone successfully used RCurl for posting data to a password-protected site? I have tired using option netrc=1 with both postForm and curlPerform (with postfields option) but can't authenticate. I would happily provide more details if some one has had some experience with this. Thanks very much. Valerie

On Tue, January 27, 2015 1:58 pm, Peter Lawler wrote: > On 28/01/15 04:47, Always Learning wrote: >> >> Saw this on the Exim List:- >> > <SNIP> >> >> I use Exim on C5 and C6 - should I be worried about Exim on C6 ? >> > > upstream references: > https://rhn.redhat.com/errata/RHSA-2015-0092.html When I read this I read that it is fixed in

On 06/12/2015 12:18 PM, Jonathan Billings wrote: > On Sat, Jun 13, 2015 at 12:05:16PM -0600, jd1008 wrote: >> Mark, please be aware that noscript has also a whitelist >> that is not viewable by the user. >> The whitelist tab does NOT list the hidden white listed >> entries. > You mean the noscript.mandatory about:config entry? I looked at it on > my computer and

Good afternoon, I've encountered a little bit of a problem, would appreciate any help here. I made a small vector consisting of ones and zeros. Something like this x <- c(0,1,0,1,0,0,1,0), and all I need is to count how many times "0" becomes "1". Tried various, of what I thought, methods with built in functions. Didn't get any further. Thank you very much. --

Hi Simon, I'm having trouble with nested parallel workers, specifically, forking inside socket connections. When mclapply is called inside a SOCK, PSOCK or FORK worker I get an error in unserialize(). cl <- makeCluster(1, "SOCK") fun = function(i) { library(parallel) mclapply(1:2, sqrt) } Failure occurs after multiple calls to clusterApply: > clusterApply(cl, 1,

Hello. I'm trying to compute median for a filtered column based on other column but there was something wrong. I'll show how I did step by step. Here's the data: a b c class 1 12 0 90 A-B2 3 97 11 A-B3 78 NA 123 A-C4 NA NA 12 A-C5 8 33 2 A-B6 12 NA 0 A-D On the command I typed: 1) data =

Hi all I'm doing some things with a colleague comparing different sorts of models. My colleague has fitted a number of glms in Genstat (which I have never used), while the glm I have been using is only available for R. He has a spreadsheet of fitted means from each of his models obtained from using the Genstat "predict" function. For example, suppose we fit the model of the type