I'll preface my question by stating that I am new to using R. I am trying to use logistic regression on my data. I typed glm(formula = purchase ~ predictor vars separated by + sign, family=binomial, data=modeldata)

The solution does not produce the multiple intercept and beta coefficients for up to 10 categorical variables that I am expecting.

I am trying to replicate what I get using a subset of the data on SPSS. SPSS does generate the intercepts for all the levels of the categorical variables that fit the model . SPSS would be too slow to run the entire database.

My questions are:

If R is not reading these variables as categorical, how do I specify them as categorical?

When I get the output from R, will it generate just the final solution after attempting to fit all the predictors? Or will it only include those predictors that have a P(z) < .05?

How do I go about achieving my desired solution? Thanks for your attention.

William Cooper

The solution does not produce the multiple intercept and beta coefficients for up to 10 categorical variables that I am expecting.

I am trying to replicate what I get using a subset of the data on SPSS. SPSS does generate the intercepts for all the levels of the categorical variables that fit the model . SPSS would be too slow to run the entire database.

My questions are:

If R is not reading these variables as categorical, how do I specify them as categorical?

When I get the output from R, will it generate just the final solution after attempting to fit all the predictors? Or will it only include those predictors that have a P(z) < .05?

How do I go about achieving my desired solution? Thanks for your attention.

William Cooper

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