similar to: Finding out collinearity in regression

Hi, I have a column of a dataframe which has time stamps like: > eh$t[1] [1] 06/05/2005 01:15:25 and was wondering how to convert it to chron variable. Thanks a lot. Young.

Dear Users, I try to calculate condition indexes and variance decomposition proportions in order to test for collinearity using colldiag() in perturb package, I got a large index and two variables with large variance decomposition proportions,but one of them is constant item.I also checked the VIF for that variable, the value is small.The result is as follows: Index intercept V1

Dear all, How can I test for collinearity in the predictor data set for multiple linear regression. Thanks Alex [[alternative HTML version deleted]]

Hi everyone, I'm trying to vectorize an application of lda to each 2D slice of a 3D array, but am running into trouble: It seems there are quite a few 2D slices that trigger either the "variables are collinear" warning, or worse, trigger a "variable appears to be constant within groups" error and fails (i.e., ceases computation rather than skips bad slice). There are

I am running a logistic regression model with a random effect using lmer. I am uncertain how to check for collinearity between my parameters. I have already run cor() and linear regression for each combination of parameters, and all Rsqr values were <0.8….but I am analyzing ecological data so a 0.8 cutoff may be unrealistic. -is there a way to check variance inflation factors or tolerance

I've uploaded the R package "perturb" to CRAN. Perturb contains two programs for evaluating collinearity. "Colldiag" calculates condition indexes and variance decomposition proportions to detect and track down collinear sets of variables. "Perturb" takes a different approach. It re-estimates the model a specified number of times, adding random noise

I've uploaded the R package "perturb" to CRAN. Perturb contains two programs for evaluating collinearity. "Colldiag" calculates condition indexes and variance decomposition proportions to detect and track down collinear sets of variables. "Perturb" takes a different approach. It re-estimates the model a specified number of times, adding random noise

Dear all, Suppose I have this data frame: > data_main V1 V2 foo 13.1 bar 12.0 qux 10.4 cho 20.33 pox 8.21 And I want to split the data into two parts first part are the one contain in the source array: > src [1] "bar" "pox" and the other one the complement. In the end we hope to get this two dataframes: > data_child1 V1 V2 bar 13.1 pox

You are using a method that needs to estimate the covariance matrix of all the variables. If you have 80 variables, there are (80+1)*80/2 = 3240 variances and covariances to estimate. How many data points do you think you need to do that? Some people assume the covariance matrix is diagonal (i.e., assuming all the variables are uncorrelated). Even then you still have 80 variances to estimate.

Hi, I have a problem in getting step function work. I am getting the following error: > fit1 <- lm(Response~1) > fmla <- as.formula(paste(" ~ ",paste(colnames,collapse="+"))) > sfit <- step(fit1,scope=list(upper= fmla,lower= ~1),k=log(nrow(dat))) Start: AIC= -1646.66 Response ~ 1 Error in "contrasts<-"(`*tmp*`, value =

> Hi, Check if the shared directory has the write permission for all. i.e. chmod 777 for the particular directory > Date: Sun, 19 Sep 1999 21:06:36 -0700 > From: Kenny Cho <Kenny.Cho@Corp.Sun.COM> > To: samba@samba.org > Subject: Problem copying files to samba mount. > Message-ID: <37E5B2CC.9ADC9F8A@ha1pal.corp.sun.com> > MIME-Version: 1.0 >

Dear R-Help list, I have a nonlinear least squares problem, which involves a changepoint; at the beginning, the outcome y is constant, and after a delay, t0, y follows a biexponential decay. I log-transform the data, to stabilize the error variance. At time t < t0, my model is log(y_i)=log(exp(a0)+exp(b0)) at time t >= t0, the model is log(y_i)=log(exp(a0-a1*(t_i - t0))+exp(b0=b1*(t_i -

Dear all, I have one dependent variable y and two independent variables x1 and x2 which I would like to use to explain y. x1 and x2 are design factors in an experiment and are not correlated with each other. For example assume that: x1 <- rbind(1,1,1,2,2,2,3,3,3) x2 <- rbind(1,2,3,1,2,3,1,2,3) cor(x1,x2) The problem is that I do not only want to analyze the effect of x1 and x2 on y but

Hi, I was wondering if there is a way, or function in R to find confounders. For istance, > a = sample( c(1:3), size=10,replace=T) > X1 = factor( c('A','B','C')[a] ) > X2 = factor( c('Aa','Bb','Cc')[a] ) > Xmat = data.frame(X1,X2,rnorm(10),rnorm(10)) > dimnames(Xmat)[[2]] = c('z1','z2','z3','y') Now,

Dear R users, I'm new of the R, I really don't know much. I want classification some data (two class, many features and huge size of data) by using R. At this case, I want using Support Vector Machine, Bayes theory based classifier, Discriminant Analysis, Regression based at least. Which package should I using, and can I compare each classifier result by predictions? Thank you.

https://bugzilla.netfilter.org/show_bug.cgi?id=1206 Bug ID: 1206 Summary: segfault when snat map rule has been added Product: nftables Version: unspecified Hardware: x86_64 OS: Ubuntu Status: NEW Severity: normal Priority: P5 Component: nft Assignee: pablo at netfilter.org

https://bugzilla.samba.org/show_bug.cgi?id=5811 Summary: rsync error: error allocating core memory buffers (code 22) at io.c(635) Product: rsync Version: 3.0.4 Platform: x64 OS/Version: Linux Status: NEW Severity: blocker Priority: P3 Component: core AssignedTo: wayned@samba.org

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Greetings all, Working in concert with the original authors, Klaus Rechert and Patrick McHardy, I have translated their article "HFSC Scheduling mit Linux" [0] on Hierarchical Fair Service Curve (HFSC) into English [1]. http://linux-ip.net/tc/hfsc.en/ Although the original content is not available directly from their website,

Dear colleagues, May you point me out to the PLS module in R system? I could not find it at all using "PLS" or "partial" as the search keywords. Thank you. Sokratis. ---------------------------------- Sokratis ALIKHANIDI, Ph.D. Department of Knowledge-based Information Engineering Toyohashi University of Technology 1-1 Hibarigaoka, Tempaku-cho, Toyohashi 441, JAPAN TEL:

Hi, thanks everyone for any help in advance. I found myself dealing with a tabular time-series data formatted each row like [ time stamp, ID, values]. I made a small examples: X = data.frame(t=c(1,1,1,2,2,2,2,3,3,3,4,4,4,5,5),id =