similar to: logistic regression based on principle component analysis

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

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 vectors, one should

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]]

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.

Dear all, I have a dataset where I reduced the dimensionality, and now I have a response variable with probabilities/proportions between 0 and 1. I wanted to do a logistic regression on those, but the function glm refuses to do that with non-integer values in the response. I also tried lrm, but that one interpretes the probabilities as different levels and gives for every level a different

Hi R experts, I'm attempting to run Linear Discriminant Analysis using the lda function in the MASS package. I've got around 50 predictor variables and one response variable. My response variable has 5 numeric categories that represent different clusters of fish abundance data (clusters were developed using Bray-Curtis and NMDS), and my predictor variables are environmental variables that

Greetings, I have a question that I'd like to get input on. I have a classic toxicology study where I artificially fertilized and exposed embryos to a chemical and counted defects. In addition, I kept track of male-female pairs that I used to artificially fertilize and generate embryos with. I need to use logistic regression to model the response, but also check that the genetics of the

Hi all, I'm trying to do model reduction for logistic regression. I have 13 predictor (4 continuous variables and 9 binary variables). Using subject matter knowledge, I selected 4 important variables. Regarding the rest 9 variables, I tried to perform data reduction by principal component analysis (PCA). However, 8 of 9 variables were binary and only one continuous. I transformed the data by

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 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]]

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 list, first accept my apologies for asking a non-R question. Can anyone point me to a good reference on logistic regression? web or book references would be great. I am interested in the use and interpretation of dummy variables and prediction models. I checked the contributed section in the CRAN homepage but could not find anything (Julian Faraway?s "practical Regression and ANOVA

I am looking for R packages that can make a Logistic Regression model with parameter estimation by Maximum Likelihood Estimation. Many thanks for helping out.

 Hi, Is there any function available to do stepwise selection of variables in Conditional(matched) logistic regression( clogit)? step, stepwise  etc are failing in case of conditional logistic regression. Please help.  Thanks P.T. Subha [[alternative HTML version deleted]]

Hi all, I'm trying to discover the options available to me for logistic and linear regression. I'm doing some tests on a dataset and want to see how different flavours of the algorithms cope. So far for logistic regression I've tried glm(MASS) and lrm (Design) and found there is a big difference. Is there a list anywhere detailing the options available which details the specific

Can someone clarify what I cannot make out from the documentation? The function 'clogit' in the 'survival' package is described as performing a "conditional logistic regression". Its return value is stated to be "an object of class clogit which is a wrapper for a coxph object." This suggests that its usefulness is confined to the sort of data which arise in

Hello! I need to classify a data set with 19 variables and 9 classes using Logistic Regression(on R). I know that when we have only 2 classes we can use glm() to estimate the coefficients of the model. But I don?t understand how can I do a classification task with Logistic Regression on a data set with 9 classes! Does anybody know how can I estimate these coefficients (of a model with 9

I was just curious if anyone knew of an alternative model to logistic regression where the probabilities seems pretty linear to the predictor rather than having that S shape that probit and logit assume. Maybe there is there some kind of other GLM that could accomplish that. Any textbook references or suggestions are appreciated. I have most of the texts but if someone knows of a text that talks

Hello, I can not get the function plot.logistic.fit.fnc() working....it returns "Error: could not find function "plot.logistic.fit.fnc"". Do I need to upload as specific package first? I am trying to check the fit of a mixed logistic model. Also, any advice for checking the assumption of independence in a mixed logistic model? Many thanks in advance, Sarah Foster