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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 have a situation where this is fine: > if (length(x)>15) { clever <- rr.ATM(x, maxtrim=7) } else { clever <- rr.ATM(x) } > clever $ATM [1] 1848.929 $sigma [1] 1.613415 $trim [1] 0 $lo [1] 1845.714 $hi [1] 1852.143 But this variant, using ifelse(), breaks: > clever <- ifelse(length(x)>15, rr.ATM(x, maxtrim=7), rr.ATM(x))

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, I am holding a dataset where firms are observed for a fixed (and small) set of years. The data is in "long" format - one record for one firm for one point in time. A state variable is observed (a factor). I wish to make a markov transition matrix about the time-series evolution of that state variable. The code below does this. But it's hardcoded to the specific years that I

I'm in this situation: factorlabels <- c("School", "College", "Beyond") with data for 8 families: education.man <- c(1,2,1,2,1,2,1,2) # Note : no "3" values education.wife <- c(1,2,3,1,2,3,1,2) # 1,2,3 are all present. My goal is to create this table: School College Beyond

Folks, I'm in a situation where I do a few thousand regressions, and some of them are bad data. How do I get back an error value (return code such as NULL) from lm(), instead of an error _message_? Here's an example: > x <- c(NA, 3, 4) > y <- c(2, NA, NA) > d <- lm(y ~ x) Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 0 (non-NA) cases

I am using R 2.1 on Apple OS X. When I get the ">" prompt, I find it works well with emacs commandline editing. Keys like M-f C-k etc. work fine. The one thing that I really yearn for, which is missing, is bracket matching When I am doing something which ends in )))) it is really useful to have emacs or vi-style bracket matching, so as to be able to visually keep track of whether I

I have a program which is doing a few thousand runs of lm(). Suppose it is a simple model y = a + bx1 + cx2 + e I have the R object "d" where d <- summary(lm(y ~ x1 + x2)) I would like to obtain Var(x2) out of "d". How might I do it? I can, of course, always do sd(x2). But it would be much more convenient if I could snoop around the contents of summary.lm and

I'm in a situation where I say: > predict(m.rpart, newdata=D[N1+t,]) 0 1 173 0.8 0.2 which I interpret as meaning: an 80% chance of "0" and a 20% chance of "1". Okay. This is consistent with: > predict(m.rpart, newdata=D[N1+t,], type="class") [1] 0 Levels: 0 1 But I'm puzzled at the following. If I say: > predict(m.rpart,

I have a data frame with one column "x": > str(data) `data.frame': 20 obs. of 1 variable: $ x: num 0.0495 0.0986 0.9662 0.7501 0.8621 ... Normally, I know that the notation dataframe[indexes,] gives you a new data frame which is the specified set of rows. But I find: > str(data[1:10,]) num [1:10] 0.0495 0.0986 0.9662 0.7501 0.8621 ... Here, it looks like the operation

I am doing > library(rpart) > m <- rpart("y ~ x", D[insample,]) > D[outsample,] y x 8 0.78391922 0.579025591 9 0.06629211 NA 10 NA 0.001593063 > p <- predict(m, newdata=D[9,]) Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : invalid result from na.action How do I persuade him to give me NA

Folks, The betareg package appears to be polished and works well. But I would like to look at the exact formulas for the underlying model being estimated, the likelihood function, etc. E.g. if one has to compute \frac{\partial E(y)}{\partial x_i}, this requires careful calculations through these formulas. I read "Regression analysis of variates observed on (0,1): percentages, proportions and

I learned R & MLE in the last few days. It is great! I wrote up my explorations as http://www.mayin.org/ajayshah/KB/R/mle/mle.html I will be most happy if R gurus will look at this and comment on how it can be improved. I have a few specific questions: * Should one use optim() or should one use stats4::mle()? I felt that mle() wasn't adding much value compared with optim, and

Folks, I am using R 2.1.1 on Apple OS X 10.3. Earlier, I used to say $ sudo R > update.packages() and all the packages used to get installed. For several weeks, I noticed that nothing has been coming through. I used the R-for-Mac graphics console and I find that there are many packages where new versions have come out which I don't have. Is something wrong with update.packages()? I

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

When using get.hist.quote(), I find the dates are broken. This is with R 2.1.1 on Mac OS X `panther'. > library(tseries) Loading required package: quadprog 'tseries' version: 0.9-27 'tseries' is a package for time series analysis and computational finance. See 'library(help="tseries")' for details. > x <-

I have a situation with a large dataset (3000+ observations), where I'm doing lags as regressors, where I get: Call: lm(formula = rj ~ rM + rM.1 + rM.2 + rM.3 + rM.4) Residuals: 1990-06-04 1994-11-14 1998-08-21 2002-03-13 2005-09-15 -5.64672 -0.59596 -0.04143 0.55412 8.18229 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.003297 0.017603

R is so smart! I found that when you switch a column from integer to factor, the memory consumption goes down rather impressively. Now I'd like to learn more. How does R do this? What does R do? How do I learn more? I got to thinking: If I was really smart, I'd see that a factor with 2 levels requires only 1 bit of storage. So I'd be able to cram 8 such factors into a byte. But this

I wrote a simple log likelihood (for the ordinary least squares (OLS) model), in two ways. The first works out the likelihood. The second merely calls the first, but after transforming the variance parameter, so as to allow an unconstrained maximisation. So the second suffers a slight cost for one exp() and then it pays the cost of calling the first. I did performance measurement. One would

Thanks to Gabor for setting me right. My code is as follows. I found it useful for learning optim(), and you might find it similarly useful. I will be most grateful if you can guide me on how to do this better. Should one be using optim() or stats4::mle? set.seed(101) # For replicability # Setup problem X <- cbind(1, runif(100)) theta.true <- c(2,3,1) y <- X