A residual plot is a graph that displays the difference between observed values and predicted values in a regression model. It is useful in R because it allows you to visually assess the fit of the model and identify any patterns or trends in the residuals.
To create a residual plot in R, you can use the "residuals()" function, which calculates the residuals for a given model. Then, you can use the "plot()" function to plot the residuals against the predicted values. Alternatively, you can use the "plot()" function on the model object itself, which will automatically generate a residual plot.
Flagged values in a residual plot indicate that there are potential outliers or influential points in the data. These values may significantly impact the results of the regression model and should be further investigated.
In R, you can use the "identify()" function to interactively label points on a plot. If you use this function on a residual plot, it will allow you to click on any points that you want to label and then display their corresponding data values. This can help you identify flagged values that may need further analysis.
If you find flagged values in a residual plot, it is important to investigate them further to determine their potential impact on the model. This may involve removing outliers, transforming the data, or using a different regression model. It is also helpful to consult with other experts in the field to get their perspective on the flagged values and potential solutions.