7.9: Summary of important R code
- Page ID
- 33288
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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 %>% ggplot(mapping = aes(x = x, y = y)) + geom_point() + geom_smooth(method = “lm”)
- Provides a scatter plot with a regression line.
- Add + geom_smooth() to add a smoothing line to help detect nonlinear relationships.
- 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 8 as well).
- par(mfrow = c(2, 2)); plot(MODELNAME)
- Provides four regression diagnostic plots in one plot.
- confint(MODELNAME, level = 0.95)
- Provides 95% confidence intervals for the regression model coefficients.
- Change
levelif you want other confidence levels.
- plot(allEffects(MODELNAME))
- Requires the
effectspackage. - Provides a term-plot of the estimated regression line with 95% confidence interval for the mean.
- Requires the
- DATASETNAME
<-DATASETNAME %>% mutate(log.y = log(y)- Creates a transformed variable called log.y – change this to be more specific to your “\(y\)” or “\(x\)”.
- predict(MODELNAME, se.fit = T)
- Provides fitted values for all observed \(x\text{'s}\) with SEs for the mean.
- predict(MODELNAME, newdata = tibble(x = XNEW), interval = “confidence”)
- Provides fitted value for a specific \(x\) (XNEW) with CI for the mean. Replace
xwith name of explanatory variable.
- Provides fitted value for a specific \(x\) (XNEW) with CI for the mean. Replace
- predict(MODELNAME, newdata = tibble(x = XNEW), interval = “prediction”)
- Provides fitted value for a specific \(x\) (XNEW) with PI for a new observation. Replace
xwith name of explanatory variable.
- Provides fitted value for a specific \(x\) (XNEW) with PI for a new observation. Replace
- qt(0.975, df = n - 2)
- Gets the \(t^*\) multiplier for making a 95% confidence or prediction interval with \(n-2\) replaced by the sample size – 2.