On 02-Mar-10 08:02:27, Noah Silverman wrote: > Hi, > I'm just learning about poison links for the glm function. > > One of the data sets I'm playing with has several of the > variables as factors (i.e. month, group, etc.) > > When I call the glm function with a formula that has a factor > variable, R automatically converts the variable to a series of > variables with unique names and binary values. > > For example, with this pseudo data: > > y v1 month > 2 1 january > 3 1.4 februrary > 1.5 6.3 february > 1.2 4.5 january > 5.5 4.0 march > > I use this call: > > m <- glm(y ~ v1 + month, family="poisson") > > R gives me back a model with variables of > Intercept > v1 > monthJanuary > monthFebruary > monthMarch > > I'm concerned that this might be doing some strange things > to my model. > Can anyone offer some enlightenment? > Thanks!
The creation of auxiliary variables is the way to incorporate a factor variable into a model. These are usually called "dummy variables", and are essentially indicator variables. Your data above would correspond to variables I (for Intercept), J (for January), F (for February) and M (for March) in addition to the other variables y and v1 as below: y v1 I J F M # month 2 1 1 1 0 0 # january 3 1.4 1 0 1 0 # februrary 1.5 6.3 1 0 1 0 # february 1.2 4.5 1 1 0 0 # january 5.5 4.0 1 0 0 1 # march The linear predictor L in the model for y would then be L = a*I + b*v1 + c1*J + c2*F + c3*J evaluated arithmetically; e.g. for row 2 of the data it is a + b*1.4 + c2 However, as given, J + F + M = I, so there is redundancy in the variables, since there are only three independent values there (not so if you exclude the Intercept using a model formula y ~ v1 + month - 1), so R will provide estimates which are computed in terms of some pattern of differences between these four variables called contrasts. Different patterns of difference present different representations of the three independent aspects. There are many different kinds of contrasts available. One of these will be chosen as default by R (depending in particular on whether the factor variable is being used as an ordered factor or an unordered factor). See ?contrasts for an outline of what is there, ?contrast for more detail, and look at the help for particular contrasts such as ?contr.helmert, ?contr.poly, ?contr.sum, ?contr.treatment. After all that: No, R is not doing strange things to your model! ted. -------------------------------------------------------------------- E-Mail: (Ted Harding) <ted.hard...@manchester.ac.uk> Fax-to-email: +44 (0)870 094 0861 Date: 02-Mar-10 Time: 08:47:11 ------------------------------ XFMail ------------------------------ ______________________________________________ R-help@r-project.org mailing list 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.
