similar to: abline and linearity over groups

Hi All, is there any chance of vectorising the two ifelse() statements in the following code: for(i in gp){ new[i,1] = ifelse(srow[i]>0, new[srow[i],zippo[i]], sample(1:100, 1, prob =Y1, rep = T)) new[i,2] = ifelse(drow[i]>0, new[drow[i]>0,zappo[i]], sample(1:100, 1, prob =Y1, rep = T)) } Where I am forced to check if the value of drow and srow are >0 for each line... in

Dear list, I often need to convert several variables from numeric or integer into factors (before plotting, for instance), as in the following example, d <- data.frame( x = seq(1, 10), y = seq(1, 10), z = rnorm(10), a = letters[1:10]) d2 <- within(d, { x = factor(x) y = factor(y) }) str(d) str(d2) I'd like to write a function factorise() which takes a data.frame and

Hello! After upgrading my sid (debian) to samba-3.0 i cannot connect to my homes-dir. I have the same configuration using with 2.2. I do not know, where is my error. I will send you my smb.conf hopping you can help me [global] panic action = /usr/share/samba/panic-action %d netbios name = zolnott01 workgroup = DROW server string = File- & Printserver Zolnott [%v] syslog = 0

Estimado Jesús Para Fernández En teoría es ese material, lo vi muy rápido y en la parte genética tiene cosas que biológicamente no son así, hay un libro de Falconer, Introducción a la genética cuantitativa, que tiene escrita la parte matemática, hay un abismo entre la biología y los ingenieros que se inspiran en la biología. Yo pensaba en la resolución de un problema real, aunque relativamente

Hello, What is the best way of solving this problem? answer <- ifelse(tf=TRUE, i * 5, previous answer) where as an initial condition tf[1] <- TRUE For example if, tf <- c(T,F,F,F,T,T,F) over i = 1 to 7 then the output of the function will be answer = 5 5 5 5 25 30 30 Thank you. Phil,

OK. Another amateur question. I have a list with attributes on pine trees, like the stem's location, a logical value set to T if it's alive, some parameters for growth, diameter, etc. The tree list has another list in it which is a new data type for me. I want to make a new list that retains all the live trees. That is where Living == T. Here's the summary of the list: >

Hi all, I am trying to filter the element of a df that start with "TF", like this: alfa = c(123221,"TF13124",41243,"TF1234",32414,"TF13124","TF14333",2134123,"TF1234") beta =

This is a not too old R-devel on Linux, it already fails in R 3.4.4, and on macOS as well. The tar file seems valid, external tar can untar it, so maybe an untar() bug. setwd(tempdir()) dir.create("pkg") cat("foobar\n", file = file.path("pkg", "NAMESPACE")) cat("this: that\n", file = file.path("pkg", "DESCRIPTION"))

According to the Assembly language reference: "In LLVM, opaque types can eventually be resolved to any type (not just a structure type)." But the only way I can think of to do so is to give it a type name, then later redeclare the name. But that gives an error: %TO = type opaque %TF = type i32 ( %TO* ) %TO = type %TF gives: $ llvm-as types1.ll llvm-as: types1.ll:3:1: error:

Hi, I have a data frame with two columns: dt and tf. The dt column is datetime and the tf column is a temperature. dt tf 1 2009-06-20 00:53:00 73 2 2009-06-20 01:08:00 73 3 2009-06-20 01:44:00 72 4 2009-06-20 01:53:00 71 5 2009-06-20 02:07:00 72 ... I need a subset of the rows where the minutes are 53. The hour is immaterial. I can not find a wildcard

Hola, Acabo de calcular tf-idf y me surge una duda. ¿Habría un valor de idf o tf-idf que se considerara como umbral para establecer que una palabra es muy común o no? Los valores de idf en mis datos van entre 0 y 3.78 y los de tf-idf ente 0 y 0.07. Un saludo El Mar, 28 de Abril de 2020, 12:53, Carlos Ortega escribió: > Hola, > Yo de primeras los quitaría para qué otros topics aparecen.

I have a list (length 750), each element containing a vector of unique strings (unique gene ids), with length up to ~40 (median 15). I want to compile a matrix of all possible triplets and their frequency within gene elements. Using combn and a lot of looping, I am accomplishing this but it is VERY slow. I've tried to figure out a way to vectorize this, using "match" and

Another solution is to only save the parts of the model object that interest you. As long as they don't include the formula (which is what drags along the environment it was created in), you will save space. E.g., tfun2 <- function(subset) { junk <- 1:1e6 list(subset=subset, lm(Sepal.Length ~ Sepal.Width, data=iris, subset=subset)$coef) } saveSize(tfun2(1:4)) #[1] 152 Bill

Hi everybody, I noticed a strange behavior when using loops versus apply() on a data frame. The example below "explicitly" computes a distance matrix given a dataset. When the dataset is a matrix, everything works fine. But when the dataset is a data.frame, the dist.for function written using nested loops will take a lot longer than the dist.apply ######## USING FOR ####### dist.for

> I’ve seen the same problem, but didn’t find solution back then. > I can give a hint that it is related to a thread local storage (notice TLS in the name). > > The same result can be reproduced by this simple program: > > thread_local int x = 0; > int main() { > return 0; > } > >When compiled into IR it produces similar error: > >LLVM ERROR:

What is puppet doing here? Nov 17 00:02:01 gumby yum: Installed: tf-starterkit-tfel0-1.0-1.tf.noarch Nov 17 00:02:01 gumby puppetd[720]: (//Node[gumby.fr.xxx.com]/Starterkit::Setup[tfel0]/Package[tf-starterkit-tfel0]/ensure) created Nov 17 00:02:07 gumby yum: Installed: tf-jboss-server-4.2.3.GA-1.tf.noarch Nov 17 00:02:07 gumby puppetd[720]:

Full_Name: simon wood Version: 2.2.0 (and lower) OS: linux/windows Submission from: (NULL) (86.135.153.59) I think that the documentation for the `specials' attribute of a `terms.object' is not quite right: specials: If the 'specials' argument was given to 'terms.formula' there is a 'specials' attribute, a list of vectors indicating the terms

Tf-idf is most used used weighting scheme is easy to understand and has been used in other frameworks like lucene and many other places. okapi bm25(implemented in xapian) is theoretically better/improved measure than tf-idf and i am looking into various other weighting scheme which are there in xapian or can be implemented like TF-ICF(term frequecy inverse corpus frequency),TF-RF(term

Hi there, I have the following problem and I hope somebody might help me. First of all: I am using WinXP SP3 (english and/or german) with R in Version 2.10.0. Now I am trying to install some packages but unfortunately I am getting a weird error. No matter which package I am trying to install - I nearly get the same error. It looks like this:

Hello R community, I need to do some aggregation based on the test data below. The below code works ok, but when its applied to my real data which includes over 9,000 records the process runs for over an hour. I know there is a more efficient way of doing this. I want to Sum the below data's volumes where FNODE and TNODE match, i.e. FNODE=1 and TNODE =20 -> Volume=100 ,