similar to: HAC and Kmean

Hi all, I have a data.frame with the following colnames pattern: x1 y11 x2 y21 y22 y23 x3 y31 y32 ... I.e. I have an x followed by a few y's. What I would like to do is turn this wide format into a tall format with two columns: "x", "y". The structure is that xi needs to be associated with yij (e.g. x1 should next to y11 and y12, x2 should be next to y21, y22, and

Hi, Does anyone read Verbeek's "A Guide to Modern Econometrics"? In its Section 4.11, how does the last two equations' HAC calculate? I've tried several groups of parameters in sandwich::NeweyWest, but I still cannot get the same result. I've tried lag=2 and lag=3, as long as prewhite=FALSE and prewhite=TRUE yet, but... Sincerely Hsiao-nan Cheung

Dear all, I am looking for an equivalent to the "newey2"-extension in Stata, in order to compute Newey-West HAC standard errors in a regression using panel data. I would be very grateful for advice which R-package could do this. I thank you very much in advance. Dirius

Dear everyone: I am doing a research on several stock markets. And I need to construct an Bartlett HAC covariance matrix estimator for Sigma(Cov(Y0,Yj)), j is from 0 to T. Can you tell me how to do it. Your Sincerely! Nigel Gregory 01/09/11 [[alternative HTML version deleted]]

# Using Moving/Rolling Windows, here we do an OLS Regression with corrections for #Heteroscedasticity and Autocorrelations (HAC) using Newey West Method. This code is a #extension of Ajay Shah?s code for moving windows simple OLS regression. # The easiest way to adjust for Autocorrelations and Heteroscedasticity in the OLS residuals is to #use the coeftest function that is included in the

Hello, I would like to estimate a VAR model with HAC corrected standard errors. I tried to do this by using the sandwich package, for example: > library(vars) > data(Canada) > myvar = VAR(Canada, p = 2, type = "const") > coeftest(myvar, vcov = vcovHAC) Error in umat - res : non-conformable arrays Which suggests that this function is not compatible with the VAR command.

Hi, I have a simple linear regression for which I want to obtain HAC corrected standard errors, since I have significant serial/auto correlation in my residuals, and also potential heteroskedasticity. Would anyone be able to direct me to the function that implements this in R? It's a basic question and I'm sure I'm missing something obvious here. I looked up this post:

Hello: My dataset set contains several thousand rows of data, with each row containing information for a house. The variables include the sale price of the house, the quarter and year of sale, the attributes of the house, and the attributes of the neighborhood and the city in which the house is located. The data is for a 10-year period. No house is repeated in the dataset. In summary, the dataset

I'm trying to pass-through my VGA card to a guest session. I found: http://www.linux-kvm.org/page/How_to_assign_devices_with_VT-d_in_KVM - which is very frustrating because there's no date on the documentation. I suspect it's old. It does clearly say that you must have VT-d support for pci pass-through. It then goes on to say "Some work towards allowing this ["software

I am in the process of getting multiple Desktops to run linuxfor Research and Development, at the moment am comparing the following intel chipset H81, Q87, Q77, H61...What is the difference?What would be best performance issue?

OK looking for some constructive critiuquing and ideas. This is just a ruff draft 1. First we need to know what type of graphics card you have. Please do the following at the command window. To get to the command window do the following [root at nVidia ~]#lspci -v "note the difference in board manufactures." These drivers will work on all Aftermarket

Hi, I have used the kmean fucntion in R to produce some results for my analysis. I like to know the specific underlying algorithm used for the implementation of the function kmean in R. I tried looking for some documents but could not find any. I obtained the kmean result for k ranging from 2 to 10. When i did this initally it worked perfectly. When i tried running again i get the error

I am trying to generate kmean of 10 clusters for a 165 x 165 matrix. i do not see any errors known to me. But I get this error on running the script Error: empty cluster: try a better set of initial centers the commands are M <-matrix(scan("R_mutual",n = 165 * 165),165,165,byrow = T) cl <- kmeans(M,centers=10,20) len = dim(M)[1] .... .... I ran the same script last night and

Hi, I have 4 huge tables on which i want to do a PCA analysis and a kmean clustering. If i run each table individually i have no problems, but if i want to run it in a for loop i exceed the memory alocation after the second table, even if i save the results as a csv table and i clean up all the big objects with rm command. To me it seems that even if i don't have the objects anymore, the

Hello, I've hit an assertion in SelectionDAG where we try to merge 2 loads that have the same operands but their MMO flags differ. One is dereferenceable and one is not. I'm not sure what the underlying issue here is: 1) MDSDNode with the same operands should have the same flags set on their respective MMO. The fact the flags differ when the opcode,types,operands and address-space are

Hi! I am new to R just 3 days in it and i apologize if my questions seem very trivial and consumed your valuable time. I am coding in perl and i stumbled upon R regarding plotting good statistical graphs. I tried the kmean clustering for a large matrix ,say > 150 * 150 . I tried the example code given in the tutorial to perform 2d plot # i ranges from 2 to 10 cl <- kmeans(x, i, 20)

I have a binary vector whose length is known. Such a vector contains an unspecified number of 1s. My goal is 1. to generate as many clusters as the number of 1s 2. to place the 1 as much as possible at the center of its own cluster Example. Say I have the following binary vector: v <- c(0,0,1,0,0,0,0,1,0,1,0,0) Then I have to get 3 clusters. I can generate a matrix containing the distance of

Sanjay, I'm looking at some missed optimizations caused by D70246. Here's a test case: define <4 x float> @f(i32 %t32, <4 x float>* %t24) { .entry: %t43 = insertelement <3 x i32> undef, i32 %t32, i32 2 %t44 = bitcast <3 x i32> %t43 to <3 x float> %t45 = shufflevector <3 x float> %t44, <3 x float> undef, <4 x i32> <i32 0, i32 undef,

Hi, I'm an italian R (and data mining) beginner. I installed R, Rattle and rggobi with no trouble. But I'm not able to do clustering (both kmean and hierarchical). When I press F5 in Rattle Cluster tab nothing happens (and no log rows in the Log Tab). On the R console I get the following error (in italian): "Errore in sprintf(fmt, ...) : argomento di lunghezza zero" (it means

Hi, I have a very large dataset of 3008 individuals and 800 numerical variables. In fact it is a table of 3008 36-monthes multivariated time series that I would like to classify with an unsupervised algorithm I had a look at the function kkmeans of e1071 package, which seems to be a kernel weighted version of the algorithm algorithm, and the bclust from the same package which does bootstrapping