# 7.9: Summary of important R code

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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 level if you want other confidence levels.
• plot(allEffects(MODELNAME))
• Requires the effects package.
• Provides a term-plot of the estimated regression line with 95% confidence interval for the mean.
• 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 x with name of explanatory variable.
• predict(MODELNAME, newdata = tibble(x = XNEW), interval = “prediction”)
• Provides fitted value for a specific $$x$$ (XNEW) with PI for a new observation. Replace x with name of explanatory variable.
• 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.

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