similar to: extracting values from data.frame given arbitrary keys

Hi all. I have a numerical function f(x), with x being a vector of generic size (say k=4), and I wanna take the numerically computed gradient, using deriv or numericDeriv (or something else). My difficulties here are that in deriv and numericDeric the function is passed as an expression, and one have to pass the list of variables involved as a char vector... So, it's a pure R programming

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Hi all. A statistical question. I have to estimate the variance of the sum: X(1) + X(2) + ...+ X(n) from an observed sample, where X(i) are *correlated* and not necessarly identically distributed. Someone can suggest a simple strategy (I hope by exploiting some already present R package) for obtaining such estimate from an observed vector X[1:n]? Tnx all, Antonio, Fabio Di Narzo.

This code gives an error: a <- ts(1:10, start=0, freq=10) b <- ts(1:10, start=1, freq=10) ts.intersect(a,b) This one works normally (and correctly): a <- ts(1:10, start=0) b <- ts(1:10, start=1) ts.intersect(a,b) Antonio, Fabio Di Narzo. P.S. How to switch off italian error messages to post on r-help? > version _ platform i386-pc-mingw32 arch i386 os

Full_Name: Antonio, Fabio Di Narzo Version: 2.6.1 OS: linux Submission from: (NULL) (213.140.16.187) In 'outer' man page, there is no mention of the Vectorize function. Moreover, I think it isn't underlined enough that the FUN argument to 'outer' must be a vectorized function (doc speaks about a function which has to 'operate elementwise'). A cross-reference from

Hi all. I have unexpected reshape results on datasets with certain variable names. Here a reproducible example: d <- matrix(seq_len(7*7), 1, 7*7) vnames <- c('acc','ppeGross','CF','ROA','DeltaSales','invTA','DeltaRevDeltaRec') varying <- unlist(lapply(vnames, paste, 1:7, sep='.')) d <- data.frame(d) names(d) <- varying

I've enconuntered this problem with the last cran version of geoR: > library(geoR) > day <- rep(1:2, each=5) > coords <- matrix(rep(runif(10),2), 10, 2) > data <- rnorm(10) > data[1] <- NA > as.geodata(cbind(coords, data, day), realisations=4) as.geodata: 1 points removed due to NA in the data Errore in as.geodata(cbind(coords, data, day), realisations = 4) :

(Moved from r-devel to r-bugs) On 3/24/2006 5:03 AM, Antonio, Fabio Di Narzo wrote: > Hi all. > There's a bug in plot.acf, when plotting acf for multivariate time series. > Here a reproducible example: > > X <- rnorm(1000) > Y <- -X + rnorm(1000, sd=0.6) > Z <- cbind(X,Y) > > In > acf(Z) > cross-correlation plot y-axis is limited to 0-1. But: >

Dear R developers, these days I'm working on some R code for fitting completely generic Bayesian Hierarchical Models in R, a la OpenBUGS and JAGS. A key feature of OpenBUGS and JAGS is that they automatically build an appropriate MCMC sampler from a generic model, specified as a directed acyclic graph (DAG). The spirit of my (would-be) implementation is instead more focused on experimentation

Dear all, When using gdb to debug my C code, I use R_PV to show the content of SEXP variables: SEXP sexp; // it is a data.frame SEXP colNames = getAttrib(sexp, R_NameSymbol); A strange thing is that after halting the program: (gdb) p R_PV(colNames) does not show the content of colNames. I am positive my code is right because if I insert "PrintValue(colNames);" in the c code,

Dear all, I have noticed a little change in the behaviour of as.factor from R-2.2.1 to R-2.3.0, and can't find it in the NEWS. In R-2.3.0: > times <- 1:5 > class(times) <- "Date" > as.factor(times) [1] 1 2 3 4 5 Levels: 1 2 3 4 5 In R-2.2.1: > as.factor(times) [1] 1970-01-02 1970-01-03 1970-01-04 1970-01-05 1970-01-06 Levels: 1970-01-02 1970-01-03 1970-01-04

Dear all, I have noticed a little change in the behaviour of as.factor from R-2.2.1 to R-2.3.0, and can't find it in the NEWS. In R-2.3.0: > times <- 1:5 > class(times) <- "Date" > as.factor(times) [1] 1 2 3 4 5 Levels: 1 2 3 4 5 In R-2.2.1: > as.factor(times) [1] 1970-01-02 1970-01-03 1970-01-04 1970-01-05 1970-01-06 Levels: 1970-01-02 1970-01-03 1970-01-04

Dear all, I have the following problem. Given an expression object 'expr' containing a certain set of symbols (say 'a', 'b', 'c'), I would like to translate the expression object in an R function of, say, 'a', programmatically. Here an example of what I mean. Given: > expr <- expression(a+b+c) a call like: > asFunctionOf(expr, 'a',

Hi guys. I recently stumbled on an unexpected behavior of R when using functions created in a loop. The problem is silly enough to me that I had hard time choosing a good mail subject, not talking about searching in the archives... After some experiments, I trimmed down the following minimal reproducible example: ####### makeF <- function(i) function() i fList <- list(makeF(1), makeF(2))

Hi all. I'm looking for robust ways of building lagged variables in a dataset with multiple individuals. Consider a dataset with variables like the following: ## set.seed(123) d <- data.frame(id = rep(1:2, each=3), time=rep(1:3, 2), value=rnorm(6)) ## >d id time value 1 1 1 -0.56047565 2 1 2 -0.23017749 3 1 3 1.55870831 4 2 1 0.07050839 5 2 2 0.12928774 6

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Dear all, I'm indirectly faced with the fact that setting the 'tsp' attribute of an object modifies its class definition: > class( structure(1:2, tsp=c(1,2,1), class=c("myts","ts")) ) [1] "ts" "myts" In general, this is of really little (ok, I admit: totally no) interest for me because 'myts' class is added just after assigning the

Hello everybody, I am wondering if it is possible to create a gui to plot a time series that is very big, it's an EEG signal of 20mins. What I would like to do is plot the first 5mins, then have a button on the gui that plots the next 5mins when pushed. Is it possible? Thanks in advance ! Gael.