similar to: maximizing a function

How does one convert objects c("a","b","c") and "d" into "abcd"? > paste(c("a","b","c"), "d") of course yields [1] "a d" "b d" "c d" -- Ajay Shah http://www.mayin.org/ajayshah ajayshah at mayin.org

I have two data frames. Suppose the first has rows r1 r2 r3 and the second has rows R1 R2 R3 I'd like to generate the data frame: r1 R1 r1 R2 r1 R3 r2 R1 r2 R2 r2 R3 r3 R1 r3 R2 r3 R3 How would I go about doing this? I'm sure there's a clean way to do it but I find myself thinking in loops. -- Ajay Shah

I wondered was people on this list felt about this article: http://www.voxeu.org/index.php?q=node/2363 which talks about the problems of obtaining sound answers in numerical optimisation in settings such as MLE or NLS. -- Ajay Shah http://www.mayin.org/ajayshah ajayshah at mayin.org http://ajayshahblog.blogspot.com <*(:-? -

Folks, I'm doing fine with using orthogonal polynomials in a regression context: # We will deal with noisy data from the d.g.p. y = sin(x) + e x <- seq(0, 3.141592654, length.out=20) y <- sin(x) + 0.1*rnorm(10) d <- lm(y ~ poly(x, 4)) plot(x, y, type="l"); lines(x, d$fitted.values, col="blue") # Fits great! all.equal(as.numeric(d$coefficients[1] + m

Folks, A nice new data resource has come up -- http://data.un.org/ I thought it would be wonderful to setup an R function like tseries::get.hist.quote() which would be able to pull in some or all of this data. I walked around a bit of it and I'm not able to map the resources to predictable URLs which can then be wget. There's some javascript going on that I'm not understanding.

How would I do something like this: f <- function(x, g) { s <- as.character(g) # THIS DOES NOT WORK sprintf("The %s of x is %.0f\n", s, g(x)) } f(c(2,3,4), "median") f(c(2,3,4), "mean") and get the results "The median of x is 3" "The mean of x is 3" -- Ajay Shah

Suppose one has x <- c(1, 2, 7, 9, 14) y <- c(71, 72, 77) How would one write an R function which alternates between elements of one vector and the next? In other words, one wants z <- c(x[1], y[1], x[2], y[2], x[3], y[3], x[4], y[4], x[5], y[5]) I couldn't think of a clever and general way to write this. I am aware of gdata::interleave() but it deals

I went to the article on np in R news 7/2 (October 2007). What's the general technique to get the source code associated with the article as a .R file that I can play with? -- Ajay Shah http://www.mayin.org/ajayshah ajayshah at mayin.org http://ajayshahblog.blogspot.com <*(:-? - wizard who doesn't know the answer.

I think I have isolated a problem with integration between Sweave and beamer. Could you please see the file: http://www.mayin.org/ajayshah/tmp/bugdemo.Rnw Unfortunately, it uses some of my internal libraries, so you can't run it. When I put it through Sweave, I get: http://www.mayin.org/ajayshah/tmp/bugdemo.tex which is, of course, a generic latex file which you can read and

Folks, I often build up R objects starting from NULL and then repeatedly using rbind() or cbind(). This yields code like: a <- NULL for () { onerow <- craft one more row a <- rbind(a, onerow) } This works because rbind() and cbind() are forgiving when presented with a NULL arg: they act like nothing happened, and you get all.equal(x,rbind(NULL,x)) or all.equal(x,cbind(NULL,x)).

1. I have used gdata::read.xls() with much happiness. But every now and then it breaks. I have not, as yet, been able to construct a mental model about the class of .xls files for which it works. Does someone have a simple rule for predicting the circumstances under which it will work? 2. Just like there is a read.xls(), it'd be great if we have a read.ods() which directly

Folks, I have a time-series of 875 readings of the weekly returns of a stock market index (India's Nifty). I am interested in the AR(1) coefficient. When I do arima(r, order=c(1,0,0)) I get a statistically significant AR1 coefficient. If we apply the ordinary bootstrap to this problem, this involves sampling with replacement, which destroys the time-series structure. Hence, if we do

Consider this code fragment: --------------------------------------------------------------------------- set.seed(42) x <- runif(20) y <- 2 + 3*x + rnorm(20) m1 <- lm(y ~ x) m2 <- lm(y ~ -1 + x) summary(m1) summary(m2) cor(y, fitted.values(m1))^2 cor(y, fitted.values(m2))^2 --------------------------------------------------------------------------- m1 is the

Folks, I have a personal package which used to build fine. Today when I tried to build the package again, some errors popped up. Could you please help? When I paste the offending function into an R it works correctly. But the package build breaks. $ R CMD check ansecon * checking for working pdflatex ... OK * using log directory '/Users/ajayshah/L/R/packages/ansecon.Rcheck' * using R

The pretty picture that I saw at: http://chartsgraphs.wordpress.com/2009/02/09/r-panel-chart-beats-excel-chart/#more-1096 inspired me to try something similar. The code that I wrote is: ------snipsnip--------------------------------------------------------------------- M <- structure(list(date = structure(c(13634, 13665, 13695, 13726, 13757, 13787, 13818, 13848, 13879, 13910, 13939, 13970,

I am using the clever formula notation of R to first do an OLS. E.g. I say m <- lm(y ~ x + f) where f is a factor, and R automatically constructs the dummy variables. Very nice. I need to then go on to do some other ML estimation using the same design matrix that's used for the OLS. I could, of course, do this manually. But it seems that lm() has done all this hard work. I wonder if

Here's a small R program: --------------------------------------------------------------------------- a <- rep(1,10000000) system.time(a <- a + 1) system.time(for (i in 1:10000000) {a[i] <- a[i] + 1}) --------------------------------------------------------------------------- and here's its matlab version:

x <- c(6.6493705109108, 7.1348436721241, 8.76886994525624, 6.12907548096037, 6.88379118678109, 7.17841879427688, 7.90737237492867, 7.1207373264833, 7.82949407630692, 6.90411547316105) plot(ecdf(x), log="x") It does the plot fine, but complains: Warning message: In xy.coords(x, y, xlabel, ylabel, log) : 1 x value <= 0 omitted from logarithmic

Folks, Based on http://www.biostat.wustl.edu/archives/html/s-news/1999-06/msg00125.html I thought I should experiment with using survreg() to estimate tobit models. I start by simulating a data frame with 100 observations from a tobit model > x1 <- runif(100) > x2 <- runif(100)*3 > ystar <- 2 + 3*x1 - 4*x2 + rnorm(100)*2 > y <- ystar > censored <- ystar <= 0

Consider this situation: > x1 <- runif(100); x2 <- runif(100); y <- 2 + 3*x1 - 4*x2 + rnorm(100) > m1 <- summary(lm(y ~ x1)) > m2 <- summary(lm(y ~ x2)) > m3 <- summary(lm(y ~ x1 + x2)) Now you have estimated 3 different "competing" models, and suppose you want to present the set of models in one table. xtable(m1) is cool, but doing that thrice would give