search for: colinear

...ke this: Bstaph.aureus:Dvan:Sr:U:ICU There are a good number of hits but there's also a staggering number of complete misses, due to a combination of scare data in that particular niche and actual lack of deviation from the categorical mean. My suspicion is that there's a large degree of colinearity in some of these variables that serves to reduce the total effect of either of a nearly colinear pair to an insignificant level; my hope is that removing one of a mostly colinear group would allow the other variables' possibly significant effects to be measured. Question 1) Is this legitima...

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 Laure...

I'm sorry to all those who are tired of seeing my email appear in need of help. But, I've never coded in any program before, so this has been a difficult process for me. Is there a simple function to test for colinearity in R? I'm running a logistic regression and a linear regression. Thanks for the help! [[alternative HTML version deleted]]

Hi all, I read in a text book, that you can examine a variable that is colinear with others, and giving different ANOVA output and explanatory power when ordered differently in the model forula, by modelling that explanatory variable, against the others colinear with it. Then, using that information to split the vector (explanatory variable) in question, into two new vect...

...composition, but I can't figure out how this technique would be combined with intermediate summary results which is required for the parallelism technique to work. Does anyone have any pointers how this might be accomplished? Thanks, Caleb ------- # Bigger example # Introduce artificial colinearity to test case P = cbind(USArrests[2]*2, USArrests) names(P) = c("Introduced", "Murder", "Assault", "UrbanPop", "Rape") # Split the data into partitions to be calculated separately # In the real case the full data would have been too large and # e...

Hi, Can anybody tell me what the message below means and how to overcome it. Thanks, Neil Warning message: X matrix deemed to be singular; variable 2 in: coxph(Surv(age_at_death, death) ~ project$pluralgp + project$yrborn + ......... >

Dear all: I try to analyse a dataset which contain one binary response variable and serveral predict variables, but multiple colinear problem exists in my dataset, some paper suggest that logistic regression for principle components is suit for these noise data, but i only find R can done principle component regression using "pls" package, is there any package that can do the task i need - logistic regression based on...

Hi group: I have a data set, which has severe colinearity problem. While running linear regression in R and SPSS, I got different models. I am wondering if somebody knows how to make the two software output the same results. (I guess the way R and SPSS handling singularity is different, which leads to different models.) Thanks. [[alternative HTML...

...ouping, ...) : variables 10 38 42 appear to be constant within groups When I look at the variables listed, they don't appear "constant within the groups" to me. I'm new to LDA and am wondering what this error means... Are my data somehow not in the right format? Should I remove colinear variables? (All variables have been normalized.) Thanks very much! Katie [[alternative HTML version deleted]]

...gt;<st1:place w:st="on">Crawley</st1:place> its single term has to be added to the multiple model: lrm(N~a*b+a+b).<o:p></o:p> This nearly always leads to high correlation rates between the interaction term a*b and its single term a or b. With regards to the law of colinearity modelling should not include correlated variables with an Spearman index >0,7. Does this mean that the interaction term has to be discarded or can the variables stay within the model when correlated? I do not necessarily want to do a PCA on this issue.<o:p></o:p> Thanks for helpi...

Dear all users, I"d like to ask you how to make decision about colinearity among categorical independent variables when the model is multinomial logistic regression. Any help is appreciated, Niklas [[alternative HTML version deleted]]

...+ G4 + G5 +G6 Gs represent a B-spline basis functions so they sum to 1 and I can't estimate the model as is without getting the last coefficient to be NA, which makes sense given the perfect collinearity. without getting in lengthy details about my code, let me just say that to avoid the colinearity problem,. I do not want to omit G1 from the regression. Instead, I want to fix the regression coefficient of one of the regressors, G1, to 1. I have read the R manual section on formulae but I have not found how to do fix a regression coefficient. Conceptually speaking it seems to me that...

...ns about MANOVA which I am still not sure if appropriately I should use it. For example I have a data set like this: BloodPressure (BP) Weight Height 120 115 165 125 145 198 156 99 176 I know that BloodPressure is correlated with both Weight and Height, however colinearity exists between Weight and Height. When I use BP = Weight + Height as the model, one is got to be insignificant. I was trying to use a BP + Weight = Height model, but not sure how to use it. Should I use MANOVA? or I just have to do two equations as BP = Weight & Weight = Height Any sugges...

Hi, I have a data frame with 6563 observations. I can run a regression with loess using four explanatory variables. If I add a fifth, R crashes. There are no missings in the data, and if I run a regression with any four of the five explanatory variables, it works. Its only when I go from four to five that it crashes. This leads me to believe that it is not an obvious problem with the data,

...ng team because such teams are intrinsically more likely to score. *For the estimation controlled for fixed effects, a series of dummy variables are added. Two dummy variables are added for each team, one for home ground and another for away ground (with the exception of Manchester City to prevent colinearity and act as baseline hazard). * As there are in total 25 teams6 involving in the English Premiership, there are 48 dummy variables added. Similarly, there are 22 and 25 teams in the German Bundesliga and Spanish Primera Liga and therefore I add 42 and 48 dummy variables respectively. Fitting the...

Hi, Is there any facility in R to perform a stepwise process on a model, which will remove any highly-correlated explanatory variables? I am told there is in SPSS. I have a large number of variables (some correlated), which I would like to just chuck in to a model and perform stepwise and see what comes out the other end, to give me an idea perhaps as to which variables I should focus on. Thanks

...plot 8.Generalized Linear Models a. Model preview b. Intuitive model builder c. diagnostic plots d. Component residual and added variable plots e. Anova (type II and III implementing LR, Wald and F tests) f. Parameter summary tables and parameter correlations g. Influence and colinearity diagnostics h. Post-hoc tests and confidence intervals with (or without) adjustments for multiple testing. i. Custom linear hypothesis tests j. Effect mean summaries (with confidence intervals), and plots k. Exports: Residuals, Standardized residuals, Studentized residuals, P...

...plot 8.Generalized Linear Models a. Model preview b. Intuitive model builder c. diagnostic plots d. Component residual and added variable plots e. Anova (type II and III implementing LR, Wald and F tests) f. Parameter summary tables and parameter correlations g. Influence and colinearity diagnostics h. Post-hoc tests and confidence intervals with (or without) adjustments for multiple testing. i. Custom linear hypothesis tests j. Effect mean summaries (with confidence intervals), and plots k. Exports: Residuals, Standardized residuals, Studentized residuals, P...

...tions because specific hypotheses hinge on the results. Two other points that may be relevant: 1) The original design was balanced, but the current results are unbalanced because of data loss (e.g. children failing to respond) and this is not randomly distributed across groups. 2) There is some colinearity between the conditions (corr between subord/when = .44) and between the groups (corr between SLI/MLU =.48). This is somewhat logical given the targets, but is not easily reduced. The syntax I'm using for the analysis is: clauseOPCyesI <- lmer(OPCorrect == "past" ~ group*Con...

Hello, I am hoping someone can help me with the following multivariate issue: I have a model consisting of about 50 covariates. I would like to reduce this to about 5 covariate for the reduced model by combining cofactors that are strongly correlated. Is there a package or function that would help me with this in R? I appreciate any suggestions. Thanks, Ian