similar to: Oaxaca Decomposition

I'm looking for a routine in R to do Blinder-Oaxaca (and related) decompositions. A very nice one has been written (by Jann) for Stata (and I'm evaluating whether I can switch over to R). I'm having a hard time finding any reference in R documentation to this pretty ubiquitous tool (in labour economics) for decomposing differences between two groups into differences in means and

Dear R-listers, does anybody know of any package developed to implement the Oaxaca-Blinder decomposition in R? I've been googling around and my reserch has been unfruitful. The latest news I've found were 1 year old. Does anybody know of any recent development? Has R ever been employed to run a Oaxaca-Blinder decomposition? Thanks for your kind support, f. [[alternative HTML version

Hello, Like QR decomposition, I am looking for decomposition to get U and V matrix of SVD decomposition (according to LINPACK, X = UDV'). Do you know if there is a function which could calculate this decomposition? Look forward to your reply, Haleh

Hi all, I am trying to analyse a time series data and want to make trend-season decomposition using STL approach in R. However I found the decomposition result seems to be sensitive to data points even with the robust option. More specifically, suppose I have a few years of monthly data. Using stl, I got a decomposition T1 + S1 + R1. Then I deleted the most recent two or three data points, the

Hi all, I am trying to analyse a time series data and want to make trend-season decomposition using STL approach in R. However I found the decomposition result seems to be sensitive to data points even with the robust option. More specifically, suppose I have a few years of monthly data. Using stl, I got a decomposition T1 + S1 + R1. Then I deleted the most recent two or three data points, the

Hi all, This question is not really R related, rather on Statistics subject itself. Even I did not do those using R. however still I want to post it here, because my hope is I could get help from great statisticians who are the very active member of this group. My problem is to interpret Variance decomposition of VAR model in layman's language. Using EViews I got following : Variance

I am reading the Mining of Massive Datasets Book by Rajaraman and Ullman. It has a good explanation of Recommendation System at Chapter 9. But what are the relationship between 1) SVD (Singular Decomposition) 2) UV-Decomposition 3) NMF (Non-negative Matrix Factorization) In particular, it seems 2) and 3) can be very similar. Is it right? Thanks. -- View this message in context:

Dear all, I have a time series with a frequency of 10 days (so 36 yearly). one year is completely NA. Now I want to do a stl-decomposition, but using e.g. na.action= na.approx makes no sense for a whole year, of course. Is there a way of simulating this single year or to just make stl not using this year for the decomposition? -- View this message in context:

Dear R list, Please, could someone help me with SSQ decomposition and contrasts. Below my data, graphic, ANOVAs and my doubt: # Data a = paste('a', gl(3, 8), sep='') b = paste('b', gl(2, 4, 24), sep='') tra = sort(paste('t', rep(1:6, 4), sep='')) y = c(26.2, 26.0, 25.0, 25.4, 24.8, 24.6, 26.7, 25.2, 25.7, 26.3, 25.1, 26.4, 19.6,

# Dear R list, # # A have a doubt about SSQ decomposition and contrasts with ANOVAs. # So, I would like a tip from more advanced R users. # Below my data, the basic script and my doubts: # Data r = paste('r', gl(3, 8), sep='') e = paste('e', rep(gl(2, 4), 3), sep='') tra = sort(paste('t', rep(1:6, 4), sep='')) y = c(26.2, 26.0, 25.0, 25.4,

I am trying to estimate a covariance matrix from the Hessian of a posterior mode. However, this Hessian is indefinite (possibly because of numerical/roundoff issues), and thus, the Cholesky decomposition does not exist. So, I want to use a modified Cholesky algorithm to estimate a Cholesky of a pseudovariance that is reasonably close to the original matrix. I know that there are R packages that

I having issues with interpreting the results of STL decomposition. The following is the data used as well as the decompsed seasonality, trend and the remainder components. It is a weekly data. The original data doesn't appear to be seasonal. But there seems to be a periodic peak in the seasonal component. Can some one please let me know how to interpret the seasonality plot in this??? Also,

Hi all, I wonder if this kind of questions are ok in this list... Quick question: What does it mean than the rank of the QR decomposition of a NxN matrix is N-1 ? m: NxN matrix qr(m)$rank equal to (N-1) Long version: I'm doing a manova on a matrix of 10 variables and 16 observations. > dim(tmp) [1] 16 10 > fit <- manova( tmp ~ treatment*mouse ) >results <-

Hi, Would anyone know if it is possible to run a Beveridge Nelson decomposition of a univariate time series object in R? I searched in the help files but didn't come across any potential methods. Thanks very much, Shruthi -- View this message in context: http://www.nabble.com/Beveridge-Nelson-Decomposition-tp21789452p21789452.html Sent from the R help mailing list archive at Nabble.com.

Hi wizards, I have seen the function svd of R for singular value decomposition, but I need to computes the ``economy size'' or ``thin'' singular value decomposition of a matrix in R. Somebody knows how to do that?. Thanks in advance. -- Web Page http://geocities.com/lord_tyranus_96/

m <- matrix(nrow=5, ncol=5) m <- ifelse(row(m)==col(m), 1, 0.2) c <- chol(m) # Choleski decomposition u <- matrix(rnorm(2000*5), ncol=5) uc <- u %*% c cr <- pnorm(uc) cr <- qbinom(cr,1,0.5) cor(cr) I expected that the cor(cr) to be 0.2 as i set in m, but the result is around 0.1. Why is that? Thanks -- View this message in context:

Dear all, I made a simple test of the Cholesky decomposition in the package 'Matrix', by considering 2 variables 100% correlated. http://blogs.sas.com/content/iml/2012/02/08/use-the-cholesky-transformation-to-correlate-and-uncorrelate-variables/ The full code is below and can be simply copy&paste in the R prompt. After uncorrelation I still have a correlation of +-100%...

I specifically need rq matrix decomposition (and not qr). Looking at netlib site for LAPACK it does provide rq whereas LINPACK not. Looking at companion qr in R I see how in base it wraps with a .Call but I do not have success in doing that for a similar .Call for rq. Anyone done this or can provide matrix rewrites that allow me to do the rq decomposition with existing R funcs? Regards MJ

Given a time series of length N, I am trying to figure out its maximum DWT decomposition level. With reference to "dwt" function of R package "wavelets", running the provided example I get the following and wonder how the maximum decomposition level (which is not an integer number) is calculated. I tried myself the formula in the on-line documentation but could not get the

Hi all, I have a time series that contains double seasonal components (48 and 336) and I would like to decompose the series into the following time series components (trend, seasonal component 1, seasonal component 2 and irregular component). As far as I know, the STL procedure for decomposing a series in R only allows one seasonal component, so I have tried decomposing the series twice. First,