similar to: pairwise linear regression between two large datasets

I am trying to finalize the use of rsync for updatiung a new nfs server before we take the old one offline. I keep getting the following warning during the rsync process: Warning: unexpected rad size of 0 in map_ptr Any ideas where this comes from and how to make it go away? I am using rsync 2.5.2 on Solaris 8 to pull data from rsync 2.5.2 on Solaris 7. Bob roconnor@vectorpartners.com

Dear Achim, R Users, What am I doing wrong in this example ? a<-zoo(rnorm(100),order.by=1:100) b<-lag(a) regr<-na.exclude(merge(a,b)) plot(regr) grangertest(regr[,1],regr[,2],3) > a<-zoo(rnorm(100),order.by=1:100) > b<-lag(a) > regr<-na.exclude(merge(a,b)) > plot(regr) > grangertest(regr[,1],regr[,2],3) Error in solve(vc[ovar, ovar]) : subscript out of bounds

Hi Sacha, On second thought, perhaps this is more the direction that you want ... X2 = cbind(X_train,y_train) colnames(X2)[3] = "y" regr2<-randomForest(y~x1+x2, data=X2,maxnodes=10, ntree=10) regr regr2 #Make prediction predictions= predict(regr, X_test) predictions2= predict(regr2, X_test) HTH, Eric On Tue, May 9, 2023 at 6:40?AM Eric Berger <ericjberger at gmail.com>

Hello, Some context:  let's say I have a data series (let's call it PRICE, for simplicity), sample size N.  I have a desire to regress that on TIME, and then to use the TIME and intercept coefficients to predict the price in the next period and to use the standard error to calculate a confidence interval.  This is all very easy. However, what I need help for is to calculate a

Dear R Users, I am attempting to use the strucchange package but get an error which is difficult to decipher. I get the following error with breakpoints: > bp<-breakpoints(regr[,1]~regr[,2]+regr[,3]) Error in my.RSS.table[as.character(i), 3:4] <- c(pot.index[opt], break.RSS[opt]) : nothing to replace with I was wondering if anyone has seen this issue before and off the top of

flight 16260 xen-4.2-testing real [real] http://www.chiark.greenend.org.uk/~xensrcts/logs/16260/ Regressions :-( Tests which did not succeed and are blocking, including tests which could not be run: test-i386-i386-xl 5 xen-boot fail REGR. vs. 16228 test-i386-i386-xl-qemuu-winxpsp3 5 xen-boot fail REGR. vs. 16228 test-i386-i386-win 5

flight 22184 xen-unstable real [real] http://www.chiark.greenend.org.uk/~xensrcts/logs/22184/ Regressions :-( Tests which did not succeed and are blocking, including tests which could not be run: test-amd64-amd64-xl-qemuu-winxpsp3 7 windows-install fail REGR. vs. 22106 test-amd64-i386-xl-win7-amd64 9 guest-localmigrate fail REGR. vs. 22106 Regressions which are regarded as

flight 13439 xen-unstable real [real] http://www.chiark.greenend.org.uk/~xensrcts/logs/13439/ Regressions :-( Tests which did not succeed and are blocking, including tests which could not be run: test-i386-i386-xl-qemuu-winxpsp3 9 guest-localmigrate fail REGR. vs. 13379 test-amd64-amd64-xl-qemuu-win7-amd64 9 guest-localmigrate fail REGR. vs. 13376 Regressions which are regarded as

Dear R Users, I am looking for an alternative to AIC or BIC to choose model parameters. This is somewhat of a general statistics question, but I ask it in this forum as I am looking for a R solution. Suppose I have one dependent variable, y, and two independent variables, x1 an x2. I can perform three regressions: reg1: y~x1 reg2: y~x2 reg3: y~x1+x2 The AIC of reg1 is 2000, reg2 is

Hi all, I need to test the significnce of difference between slopes of two regression lines and regression line with theoretical line. I try to use Slope.test function from emu package, but an error occured... library(emu) d1<-data.frame(P1=c(1,2,3,5,7,8,9,13,14,15), P2=c(1,2,5,8,11,13,15,15,18,24), R=c(2,7,8,9,16,21,27,31,33,36)) # First data set m1<-lm(R~P1+P2+P1*P2,data=d1) # Regr.

Dear list, I have encountered a special case for searching a linear regression where I'm not satisfied with the results obtained using the traditional least squares method (sometimes called OLS) for estimating/optimizing the residues to the regression line (see code below). Basically, a group of my x-y data are a bit off the diagonal line (in my case the diagonal represents the ideal or

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:

Dear R'ers, I am fighting with a problem that is driving me crazy. I use "lm" in my user-defined function, but it seems to be looking for weights outside of my function's environment: ### Generating example data: x<-data.frame(y=rnorm(100,0,1),a=rnorm(100,1,1),b=rnorm(100,2,1)) myweights<-runif(100) data.for.regression<-x[1:3] ### Creating function

flight 22043 xen-unstable real [real] http://www.chiark.greenend.org.uk/~xensrcts/logs/22043/ Regressions :-( Tests which did not succeed and are blocking, including tests which could not be run: test-amd64-i386-xl 9 guest-start fail REGR. vs. 22033 build-armhf 4 xen-build fail REGR. vs. 22033 test-amd64-i386-xl-qemut-winxpsp3-vcpus1

Hi. I need to apply run a regression analysis for groups of data of fixed length:100 As, 100 Bs, 100 Cs etc. eg x Key Value A 1 A 21.2 A 4 A 6.5 ...repeat 96 times with differing values of A B 1 B 2.3 B NA B 6.5 ...repeat 96 times with differing values of B etc I run these against a linear model using tapply(data$Value, data$Key,FUN=regr,100)

Hi, I am trying to run many different regressions using a FOR Loop. The input data that is read into R has the following variables · Volume · Price1 · Price2 · Price3 · Price4 · Price5 · Trend · Seasonality I want to run 5 regressions, with the Volume as an dependent variable and Price, Trend & Seasonality as

Hi. First, a little preliminary observation: in this thread, it is indeed a (multiple linear) regression model, the real problem is but my opinion on general questions about R assign. So as I said, I want to do a regression analysis, however, for several target variables or data tables. This case, although the target variable is always the same, but the data comes from different sectors, ie, I

Hello! I am trying to create an R optimization routine for a task that's currently being done using Excel (lots of tables, formulas, and Solver). However, otpim seems to be finding a local minimum. Example data, functions, and comparison with the solution found in Excel are below. I am not experienced in optimizations so thanks a lot for your advice! Dimitri ### 2 Inputs:

flight 8803 xen-unstable real [real] http://www.chiark.greenend.org.uk/~xensrcts/logs/8803/ Regressions :-( Tests which did not succeed and are blocking: test-amd64-i386-rhel6hvm-intel 4 xen-install fail REGR. vs. 8769 test-amd64-i386-xl 4 xen-install fail REGR. vs. 8769 test-amd64-i386-pair 6 xen-install/dst_host fail REGR. vs. 8769

flight 13339 xen-unstable real [real] http://www.chiark.greenend.org.uk/~xensrcts/logs/13339/ Regressions :-( Tests which did not succeed and are blocking, including tests which could not be run: test-amd64-amd64-xl-sedf-pin 9 guest-start fail REGR. vs. 13025 Regressions which are regarded as allowable (not blocking): test-amd64-amd64-xl-sedf 9 guest-start