similar to: Help with colinearity problem in multiple linear regression

Hello, I'm new using R and developing tables. I have a problem in developing a table. In a questionaire I made I ask this question "Please tell me the first three sympthoms caused by Respiratory tract infection you've caught this year", then the people answer three sympthoms, the first mention (Top of mind) is saved in a variable called "P2_A", the second mention in

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'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! I'm having some issues on both conceptual and technical levels for selecting the right combination of variables for this model I'm working on. The basic, all inclusive form looks like lm(mic ~ B * D * S * U * V * ICU) Where mic, U, V, and ICU are numeric values and B D and S are factors with about 16, 16 and 2 levels respectively. In short, there's a ton of actual explanatory

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 version deleted]]

Hello,<?xml:namespace prefix = o ns = "urn:schemas-microsoft-com:office:office" /><o:p></o:p> while doing my thesis in habitat modelling I´ve come across a problem with interaction terms. My question concerns the usage of interaction terms for linear regression modelling with R. If an interaction-term (predictor) is chosen for a multiple model, then, according to

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]]

Dear R users, I want to estimate a Cox PH model with time-dependent covariates so I am using a counting process format with the following formula: Surv(data$start, data$stop, data$event.time) ~ cluster(data$id) + G1 + G2 + G3 + 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

Hi All, I have questions 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

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

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,

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

6.4.1 Estimation of fixed effects Heterogeneous team ability is a possible explanation for the result in Section 6.3. That result simply indicates that the more goals a team scores, the higher the probability that it will score more. However, teams that can score more goals also indicate teams with greater ability, or just greater scoring ability, than their opponents. As mentioned in Section

Deducer 0.1 has been released to CRAN Deducer is designed to be a free, easy to use, alternative to proprietary software such as SPSS, JMP, and Minitab. It has a menu system to do common data manipulation and data analysis tasks, and an excel-like spreadsheet in which to view and edit data frames. The goal of the project is to two fold. 1. Provide an intuitive interface so that non-technical

Deducer 0.1 has been released to CRAN Deducer is designed to be a free, easy to use, alternative to proprietary software such as SPSS, JMP, and Minitab. It has a menu system to do common data manipulation and data analysis tasks, and an excel-like spreadsheet in which to view and edit data frames. The goal of the project is to two fold. 1. Provide an intuitive interface so that non-technical

Hello, I am a relative novice at both using regression analysis and at using R in general (and at object oriented programing). A colleague convinced me that binary logistic regression is the most appropriate analysis for the data that I have though, so I've been trying to muddle through. I'm currently stumped on how to interpret/compute odds ratios for two and three way interactions

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

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 vectors, one should

Dear list, i'm a new R user, so I apologize if the topic is already being addressed by some other user. I'm trying to determine if the reproductive success of a species of bird is related to a list of covariates. These are the covariates: ? elev: elevation of nest (meters) ? seadist: distance from the sea (meters) ? meanterranova: records of temperature ? minpengS1: records

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 + ......... >