similar to: Breusch-Godfrey Test

Dear R-users, I've been using R for a while and I am very satisfied! Unfortunately, I still have not figured out an efficient and general way to construct and use lags of time series, especially when I need to work with different packages. Let me give an example. I have two time series x and y and I want to estimate a variaty of distributed lags models and run different tests

Hi, R core team I am using the function {aidsEst} in package [micEcon] to do an AIDS model now. So far, everything is good. But I want to test the auto correlation and heteroskedasticity of the individual equation from AIDS demand system. How can I return the individual equation? PS: serial correlation test is {bgtest} in package [lmtest] and heteroskedasticity is {bptest} in package

Hi r-programmers, I performe Breusch-Pagan tests (bptest in package lmtest) to check the homoscedasticity of the residuals from a linear model and I carry out carry out White's test via bptest (formula, ~ x * z + I(x^2) + I(z^2)) include all regressors and the squares/cross-products in the auxiliary regression. But what can I do if I want find coefficient and p-values of variables x, z, x*z,

Hi, I am trying to learn time series, and I am attending a colleague's course on Econometrics. However, he uses e-views, and I use R. I am trying to reproduce his examples in R, but I am having problems specifying a AR(1) model. Would anyone help me with my code? Thanks in advance! Reproducible code follows: download.file("https://sites.google.com/a/proxima.adm.br/main/ex_32.csv

Hi,I want the results to be saved automatically in a output text file after the script has finished running. I used the sink function in the following example, but the results file (output.txt) was empty. net <- loadNetwork("C://Users//Priya//Desktop//Attractor analysis_all genes//synaptogenesis//regulationof_dopamine_signaling_submodule3.txt")# First I loaded theinput file for

Some comments: 1. sink() does not return a value. There is on point to set attr <- sink(...). Just give the command sink("C://....etc") 2. to complete the saving to the file you must give a second sink command with no argument: sink() So your code would be (pseudo-code, not actual code) sink( "filename" ) do something that prints output which will be captured by sink

Hi Eric,I tried as you suggested but I could not find the output in the text file I created (attr.txt) net <- loadNetwork("C://Users//Priya//Desktop//Attractor analysis_all genes//synaptogenesis//regulationof_dopamine_signaling_submodule3.txt")sink("C://Users//Priya//Desktop//Attractor analysis_all genes//synaptogenesis//attr.txt") sink() attr <- getAttractors(net,

Merry Xmas to all, I am writing a function and curiously this runs sometimes on one data set and fails on another and i cannot figure out why. Any help much appreciated. If i run the code below with data <- iris[ ,1:4] The code runs fine, but if i run on a large dataset i get the following error (showing data structures as matrix is large) > str(cluster.data) num [1:9985, 1:811] 0 0 0 0

Hi Eric,Thanks for the explanation. Is there a way to save the results automatically after the analysis gets over?. As I recently lost the results, because I didn't save the results. I don't want to run the sink or save command after the analysis is over rather run the command for saving the file before starting to run the analysis, so the file gets saved automatically after the script has

Hi Priya, You did not follow the logic of the pseudo-code. The sink("filename"), sink() pair captures whatever output is generated between the first sink statement and the second sink statement. You need (possibly) to do: sink("C://Users//Priya//Desktop//Attractor analysis_all genes//synaptogenesis//attr.txt") net <- loadNetwork("C://Users//Priya//Desktop//Attractor

Let's try a simple example. > # Create a script file of commands > # Note we must print the results of quantile explicitly > cat("x <- rnorm(50)\nprint(quantile(x))\nstem(x)\n", file="Test.R") > > # Test it by running it to the console > source("Test.R") 0% 25% 50% 75% 100% -2.4736219 -0.7915433

Hi David,Thank you for the example.When I try to use the cat function, I get an error cat(attr<-getAttractors(net, type="asynchronous"))Error in cat(attr <- getAttractors(net, type = "asynchronous")) : argument 1 (type 'pairlist') cannot be handled by 'cat' Please let me know, if I have used the function in right way?. Thank you Priya ?

I have been reading about autocorrelation in linear models over the last couple of days, and I have to say the more I read, the more confused I get. Beyond confusion lies enlightenment, so I'm tempted to ask R-Help for guidance. Most authors are mainly worried about autocorrelation in the residuals, but some authors are also worried about autocorrelation within Y and within X vectors

I have a project for which I used a Wicket template as a starting point. The original example uses prototype.js. I''m using an SVG image as a background and have included a javascript that causes the image to always fill the background, regardless of the client window dimensions. This works fine in Safari 3 (Firefox doesn''t display the background image, but I''ll deal

I was wondering if anyone could help with choosing optimal lag length for ZA test. There have been two lag order selection methods commonly used in the literature: 1) The ZA paper recommends to run the test with maximum number of lags. Then the lag order is reduced sequentially until the longest lag is statistically significant; 2) One could also use AIC or SBC or other criteria to choose lag

Dear R users, there is a new version of the package `lmtest' for testing linear regression models on CRAN. Except for a couple of minor bug fixes, there are essentially these new features: o added Breusch-Godfrey test for serial correlation (thanks to David M. Mitchell who provided the initial version of the code for bgtest) o new data sets: mandible measurements in fetuses,

Hello, On Thursday, September 20, 2018 11:15:04 AM EDT Duncan Murdoch wrote: > On 20/09/2018 6:59 AM, Ralf Stubner wrote: > > On 9/20/18 1:43 AM, Carl Boettiger wrote: > >> For a well-tested C algorithm, based on my reading of Lemire, the > >> unbiased "algorithm 3" in https://arxiv.org/abs/1805.10941 is part > >> already of the C standard library in

data:http://fmwww.bc.edu/ec-p/data/wooldridge/CRIME4.dta > a$call lm(formula = clcrmrte ~ factor(year) + clprbarr + clprbcon + clprbpri + clavgsen + clpolpc, data = cri) > bptest(a,st=F) Breusch-Pagan test data: a BP = 34.4936, df = 10, p-value = 0.0001523 > bptest(a,st=T) studentized Breusch-Pagan test data: a BP = 10.9297, df = 10, p-value = 0.363 >

Hi all, I'm getting contradictory results from bptest and ncvTest on a model calculated by GLS as: olslm = lm(log(rr)~log(aloi)*reg*inv, data) varlm = lm(I(residuals(olslm)^2)~log(aloi)*reg*inv, data) glslm = lm(log(rr)~log(aloi)*reg*inv, data, weights=1/fitted(varlm)) Testing both olslm and glslm with both ncvTest and bptest gives: > ncvTest(olslm) Non-constant Variance Score Test

Dear all, Trying to test for heteroskedasticity I tried several test from the car package respectively lmtest. Now that they produce rather different results i am somewhat clueless how to deal with it. Here is what I did: 1. I plotted fitted.values vs residuals and somewhat intuitively believe, it isn't really increasing... 2. further I ran the following tests bptest (studentized