6.13: Summary of important R code
- Page ID
- 33276
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The main components of the R code used in this chapter follow with the components to modify in lighter and/or ALL CAPS text where y is a response variable, x is an explanatory variable, and the data are in DATASETNAME.
- DATASETNAME %>% ggpairs()
- Requires the
GGallypackage. - Makes a scatterplot matrix that also displays the correlation coefficients.
- Requires the
- cor(y ~ x, data = DATASETNAME)
- Provides the estimated correlation coefficient between \(x\) and \(y\).
- plot(y ~ x, data = DATASETNAME)
- Provides a base R scatter plot.
-
DATASETNAME %>% ggplot(mapping = aes(x = x, y = y) +
geom_point() +
geom_smooth(method = “lm”)- Provides a scatter plot with a regression line.
- Add color = groupfactor to the aes() to color points and get regression lines based on a grouping (categorical) variable.
- Add + geom_smooth(se = F, lty = 2) to add a smoothing line to the scatterplot as a dashed line.
- MODELNAME
<-lm(y ~ x, data = DATASETNAME)- Estimates a regression model using least squares.
- summary(MODELNAME)
- Provides parameter estimates and R-squared (used heavily in Chapter 7 and 8 as well).
- par(mfrow = c(2, 2)); plot(MODELNAME)
- Provides four regression diagnostic plots in one plot.