55 lines
2.0 KiB
R
55 lines
2.0 KiB
R
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# Goal: "Dummy variables" in regression.
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# Suppose you have this data:
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people = data.frame(
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age = c(21,62,54,49,52,38),
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education = c("college", "school", "none", "school", "college", "none"),
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education.code = c( 2, 1, 0, 1, 2, 0 )
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)
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# Here people$education is a string categorical variable and
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# people$education.code is the same thing, with a numerical coding system.
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people
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# Note the structure of the dataset --
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str(people)
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# The strings supplied for `education' have been treated (correctly) as
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# a factor, but education.code is being treated as an integer and not as
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# a factor.
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# We want to do a dummy variable regression. Normally you would have:
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# 1 Chosen college as the omitted category
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# 2 Made a dummy for "none" named educationnone
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# 3 Made a dummy for "school" named educationschool
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# 4 Ran a regression like lm(age ~ educationnone + educationschool, people)
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# But this is R. Things are cool:
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lm(age ~ education, people)
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# ! :-)
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# When you feed him an explanatory variable like education, he does all
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# these steps automatically. (He chose college as the omitted category).
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# If you use an integer coding, then the obvious thing goes wrong --
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lm(age ~ education.code, people)
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# because he's thinking that education.code is an integer explanatory
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# variable. So you need to:
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lm(age ~ factor(education.code), people)
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# (he choose a different omitted category)
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# Alternatively, fix up the dataset --
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people$education.code <- factor(people$education.code)
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lm(age ~ education.code, people)
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#
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# Bottom line:
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# Once the dataset has categorical variables correctly represented as factors, i.e. as
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str(people)
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# doing OLS in R induces automatic generation of dummy variables while leaving one out:
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lm(age ~ education, people)
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lm(age ~ education.code, people)
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# But what if you want the X matrix?
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m <- lm(age ~ education, people)
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model.matrix(m)
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# This is the design matrix that went into the regression m.
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