Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 7
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Revision as of 05:39, 16 February 2017 by Benjamin Mako Hill (talk | contribs) (Created page with "* fitting a linear model with one variable: lm() ** module formulae, which we've already seen! ** looking at model objects: summary(); m$<tab> or names(m) *** m$fitted.values...")
- fitting a linear model with one variable: lm()
- module formulae, which we've already seen!
- looking at model objects: summary(); m$<tab> or names(m)
- m$fitted.values;
- residuals: mtcars$mpg - m$fitted.values OR m$residuals
- also functions: coefficients(m) (or coef), predict(m), residuals(m) (or resid)
- plotting residuals:
- hist(residuals(m))
- plot against our x: plot(mtcars$hp, residuals(m)
- QQ-plots with qqnorm(residuals(m))
- doing a plot with ggplot just involves making a dataset: d.fig <- data.frame(hp=mtcars$hp, resids=residuals(m))
- adding controls: just make our formula more complex
- update.formula()
- or just a write a new one
- generating nice regression plots:
- one of many options: stargazer(m1, m2, type="text") or type="html"
