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
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