Setting aside the calendrical detail that there was no such year, such an intercept is often absurdly large positive or negative, which is logical but a distraction in interpretation and presentation (even to well-informed audiences). Here a regression of some response on date expressed as dates like 2000 or 2010 implies an intercept which is the value of response in year 0. Perhaps the simplest case is linear regression on a date variable in years. Usually, but not necessarily, the origin should be a date within the time period of study or very close to it. However, it usually makes most sense when dates are shifted to an origin that makes sense for the study. (Non-identity link functions complicate this, naturally.) The numerator units depend on those of the response or dependent variable. The coefficient associated with each date has denominator units which are whatever the units of the date are. In my view, as this is now on Cross-Validated, we should not focus too narrowly on the poster's favourite software, important though that is for like-minded people.ĭates in any software if not numeric can be converted to numeric variables, expressed in years, days, milliseconds or whatever since some time origin. Here I address the general question and am happy to let R experts fill in the crucial details.
Building on earlier comments on Stack Overflow:
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