similar to: attach and object masking

Hi All, I have a data frame with a group of variables named b1, b2, b3, ..., b18. These variables take the value 1, 2 or NA. For each observation, I want to do some computation by looping over the values for the group of variables: b1 to b18. In STATA I would do: forval i=1/18 { --- use b`i' for computation ---- } How can this be done in R? Deepankar

Hi! As per Thomas' advice, I upgraded R by using "update.packages()" and got the following warning messages: Warning messages: 1: installation of package 'lmtest' had non-zero exit status in: install.packages(update[, "Package"], instlib, contriburl = contriburl, 2: installation of package 'quadprog' had non-zero exit status in: install.packages(update[,

As part of carrying out a complicated maximum likelihood estimation, I am trying to learn to program likelihoods in R. I started with a simple probit model but am unable to get the code to work. Any help or suggestions are most welcome. I give my code below: ************************************ mlogl <- function(mu, y, X) { n <- nrow(X) zeta <- X%*%mu llik <- 0 for (i in 1:n) { if

I am trying to maximise a complicated loglikelihood function with the "optim" command. Is there some way to get to know the estiamtes at each iteration? When I put "control=list(trace=TRUE)" as an option in "optim", I just got the initial and final values of the loglikelihood, number of iterations and whether the routine has converged or not. I need to know the

Hi All, I have data on the sequence of births for families with completed fertility cycle (in a data frame); the relevant variables are called b1, b2, b3, b4, b5, b6 and record the birth of the first, second, ..., sixth child. So, b1=1 if the first birth is male, b1=2 if the first birth is female, and b1=NA if the family did not record any first birth. Similarly for b2, b3, b4, b5 and b6. I

Hi R users, I have a couple of questions about some problems that I am facing with regard to numerical integration and optimization of likelihood functions. Let me provide a little background information: I am trying to do maximum likelihood estimation of an econometric model that I have developed recently. I estimate the parameters of the model using the monthly US unemployment rate series

Hi All, I am trying to do an ML estimation in R. My likelihood function has several nested loops and so it takes a lot of time (days when I use the genetic algorithm for optimization) for the optimization to finish. Unable to avoid loops, I am thinking of writing the likelihood function in C++ and calling it from within R when using *optim()*. I found that one can call C functions (once they have

Hi R Users, I have produced a simulated scatter plot of y versus x tightly clustered around the 45 degree line through the origin with the following code: x <- seq(1,100) y <- x+rnorm(100,0,10) plot(x,y,col="blue") abline(0,1) Is there some way to generate a 3-dimensional analogue of this? Can I get a similar simulated scatter plot of points in 3 dimensions where the points

I tried to install the package "tseries" but could not do so. Any suggestions are welcome. I am using Ubuntu Linux and here is what I did: $ sudo R Then once in R, I did > install.packages("tseries", dep=TRUE) which gave a long list of errors. Going through them, I saw that the basic problem was that the package "grid" was not found by the packages (like

Hello, I am drawing contour lines for a function of 2 variables at one level of the value of the function and want to include a small arrow in any direction of increase of the function. Is there some way to do that? Below is an example that creates the contour lines. How do I add one small arrow on each line in the direction of increase of the function (at some central point of the contour

Hi, I have two further comments/questions about large datasets in R. 1. Does R's ability to handle large datasets depend on the operating system's use of virtual memory? In theory, at least, VM should make the difference between installed RAM and virtual memory on a hard drive primarily a determinant of how fast R will calculate rather than whether or not it can do the calculations.

R Users: I am trying to estimate a model of fertility behaviour using birth history data with maximum likelihood. My code works but is extremely slow (because of several for loops and my programming inefficiencies); when I use the genetic algorithm to optimize the likelihood function, it takes several days to complete (on a machine with Intel Core 2 processor [2.66GHz] and 2.99 GB RAM). Computing

Hi All, I am using R 2.11.0 on a Ubuntu machine. I have a time series data set and want to run rolling regressions with it. Any suggestions would be useful. Here are the details: (1) I convert relevant variables into time series objects and compute first differences: vad <- ts(data$ALLGVA/data$GDPDEF, start=1948, frequency=1) emp <- ts(data$ALLEMP, start=1948, frequency=1) vad.dif1 <-

Hi everyone - This came up within the last day -- Jim's response to Deepankar is pasted below. There are probably lots of reasons, but what is the advantage to using .temp over, say, temp? I often find myself writing temporary objects -- should I use the . preface? What would be the advantages to doing so? Thanks in advance for what will surely be a collection of illuminating responses.

Hi All, I am using R 2.11.0 on a Ubuntu machine. I estimated a model using "tsls" from the package "sem". Is there a way to get Newey West standard errors for the parameter estimates? When estimating the model by OLS, I used "NeweyWest" from the package "sandwich" to get HAC standard errors. But, I am not able to use the same method with the results of the

I'm new to bugs, so please bear with me. Can someone tell me if the following two models are doing the same thing? The reason I ask is that with the same data, the first (based on 4 separate coeffs a1--a4) takes about 50 secs, while the second (based on a vectorized form, a[]) takes about 300. The means are about the same, though R-hat's in the second version are quite a bit better.

The library() function prints an information message when objects are masked on the search path by (or from) a newly attached package. The attach() function, however, doesn't. Is there a good reason for this? I have adapted the machinery for checking conflicts used in the library () function to attach(). If there are no objections, I will check this into R-devel. I have seen new users get

Hi All, This is surely an easy question but somehow I am not being able to get it. I am using R 2.13.2 and have a data set where variable names like this appear: pci1990, pci1991, ... , pci2009. "pci1990" has data on per capita income for 1990, "pci1991" has data on per capita income for 1991, and so on. I would like to create the logarithm of per capita for each of the

Hi , I am planning to use an open source .ogg player on my ARM simulator on QEMU ( for ARM Application Baseboard ) . So far I have been able to bring up a Linux kernel 2.6.26.5 on the simulator . Right not I want an open source ogg vorbis player which I can use to call the libraries and play some files . However the catch is that I need the APIs exposed of the player application to me . I am

Hi All, I am scheduled to teach a graduate course on research methods in health sciences at a university. While drafting the course proposal, I decided to include a brief introduction to R, primarily with an objective to enable the students to do data analysis using R. It is expected that enrolled students of this course have all at least a formal first level introduction to quantitative methods