Hi Gabrielle, With that number of binary predictors it would be no surprise if some were linear combinations of others.
Jim On Tue, Mar 28, 2017 at 2:23 AM, Gabrielle Perron <gabrielle.per...@mail.mcgill.ca> wrote: > Hi, > > > This is my first time using this mailing list. I have looked at the posting > guide, but please do let me know if I should be doing something differently. > > > Here is my question, I apologize in advance for not being able to provide > example data, I am using very large tables, and what I am trying to do works > fine with simpler examples, so providing example data cannot help. It has > always worked for me until now. So I am just trying to get your ideas on what > might be the issue. But if there is any way I could provide more information, > do let me know. > > > So, I have a vector corresponding to a response variable and a table of > predictor variables. The response vector is numeric, the predictor variables > (columns of the table) are in the binary format (0s and 1s). > > > I am running the glm function (multivariate linear regression) using the > response vector and the table of predictors: > > > fit <- glm(response ~ as.matrix(predictors), na.action=na.exclude) > > coeff <- as.vector(coef(summary(fit))[,4])[-1] > > > When I have been doing that in the past, I would extract the vector of > regression coefficient to use it for further analysis. > > > The problem is that now the regression returns a vector of coefficients which > is missing some values. Essentially some predictor variables are not > attributed a coefficient at all by glm. But there are no error messages. > > > The summary of the model looks normal, but some predictor variables are > missing like I mentioned. Most other predictors have assigned data > (coefficient, pvalue, etc.). > > About 30 predictors are missing from the model, over 200. > > > I have tried using different response variables (vectors), but I am getting > the same issue, although the missing predictors vary depending on the > response vector... > > > Any ideas on what might be going on? I think this can happen if some > variables have 0 variance, but I have checked that. There are also no NA > values and no missing values in the tables. > > > What could cause glm to ignore/remove some predictor variables? > > > Any suggestion is welcome! > > > Thank you, > > > Gabrielle > > > > > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
