search for: collinearities

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

...a function for variance inflation factors available in the car package; however, Belsley (1991) Conditioning Diagnostics (Wiley) notes that there are several weaknesses of VIFs: e.g. 1) High VIFs are sufficient but not necessary conditions for collinearity 2) VIFs don't diagnose the number of collinearities and 3) No one has determined how high a VIF has to be for the collinearity to be damaging. He then develops and suggests using condition indexes instead, so I was wondering if anyone had programmed them. Thanks Peter Peter L. Flom, PhD Assistant Director, Statistics and Data Analysis Core Cen...

hello list, when I use method lda of the MASS package I experience a warning: variables are collinear in: lda.default(data[train, ], classes[train]) Is there an easy way to recover from this issue within the MASS package? Or how can I tell how severe this issue is at all? I understand that I shouldn't use lda at all with collinear data and should use "quadratische" (squared?)

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

Dear R-help list, apparently lda from the MASS package can be used in situations with collinear variables. It only produces a warning then but at least it defines a classification rule and produces results. However, I can't find on the help page how exactly it does this. I have a suspicion (it may look at the hyperplane containing the class means, using some kind of default/trivial

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

Is there a colinearty function implemented in R? I have tried help.search("colinearity") and help.search("collinearity") and have searched for "colinearity" and "collinearity" on http://www.rpad.org/Rpad/Rpad-refcard.pdf but with no success. Many thanks in advance, Peter Lauren.

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'm writing a program to perform linear regressions to estimate the number of bank teller transactions per hour of various types based upon day of week, time of day, week of month and several prices. I've got about 25,000 records in my dataset, 85 columns of transaction counts (used 1 at a time), about 50 columns of binary indicators (day, week, pay period, hour, branch), and a half dozen

Hello R-user I want to compute a multiple regression but I would to include a check for collinearity of the variables. Therefore I would like to use a ridge regression. I tried lm.ridge() but I don't know yet how to get p-values (single Pr() and p of the whole model) out of this model. Can anybody tell me how to get a similar output like the summary(lm(...)) output? Or if there is

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 am trying to find out a collinearity in explanatory variables with alias(). I creat a dataframe: dat <- ds[,sapply(ds,nlevels)>=2] dat$Y <- Response Explanatory variables are factor and response is continuous random variable. When I run a regression, I have the following error: fit <- aov( Y ~ . , data = dat) Error in "contrasts<-"(`*tmp*`, value =

I have a linear model y~x1+x2 of some data where the coefficient for x1 is higher than I would have expected from theory (0.7 vs 0.88) I wondered whether this would be an artifact due to x1 and x2 being correlated despite that the variance inflation factor is not too high (1.065): I used perturbation analysis to evaluate collinearity library(perturb)

Hi i need to create a model from 250 + variables with high collinearity, and only 17 data points (p = 250, n = 750). I would prefer to use Cp, AIC, and/or BIC to narrow down the number of variables, and then use VIF to choose a model without collinearity (if possible). I realize that having a huge p and small n is going to give me extreme linear dependency problems, but I *think* these model

Hello, in the paper "Avoiding the effects of concurvity in GAM's .." of Figueiras et al. (2003) it is mentioned that in GLM collinearity is taken into account in the calc of se but not in GAM (-> results in confidence interval too narrow, p-value understated, GAM S-Plus version). I haven't found any references to GAM and concurvity or collinearity on the R page. And I

Dear all, I have a problem with interactions in lmer. I have 2 factors (garden and gebiet) which interact, plus one other variable (home), dataframe arr. When I put: / lmer (biomass ~ home + garden:gebiet + ( 1|Block), data = arr)/ it writes: /Error in lme4::lFormula(formula = biomass ~ home + garden:gebiet + (1 | : rank of X = 28 < ncol(X) = 30/ In the lmer help I found out that if not

Dear R-help list, two issues regarding lda. 1) I'm puzzled by the fact that lda's in-build cross-validation gives results different from the manual cross-validation routine that I run (of course mine may be wrong, but I don't think so). See here: library(MASS) set.seed(12345) n <- 50 p <- 10 # or p<- 200 testdata <- matrix(ncol=p,nrow=n) for (i in 1:p) testdata[,i]

Dear all, I am a newcomer to R. I intend to using R to do stepwise regression and PLS with a data set (a 55x20 matrix, with one dependent and 19 independent variable). Based on the same data set, I have done the same work using SPSS and SAS. However, there is much difference between the results obtained by R and SPSS or SAS. In the case of stepwise, SPSS gave out a model with 4 independent