The lines() function and line types

As noted in Chapter 2, numerical data is often assumed to conform approximately to a Gaussian probability distribution, characterized by the bell curve. One point of this exercise is to show what this bell curve looks like for exactly Gaussian data and the other is to show how the lines() function can be used to add lines to an existing plot.

The curves you are asked to draw here have the same basic shape but differ in their details (specifically, the means and standard deviations of these Gaussian distributions are different). For this reason, it is useful to draw these curves with different line types to help us distinguish them.

Note that line types are set by the lty argument, with the default value lty = 1 specifying solid lines, lty = 2 specifying dashed lines, and lty = 3 specifying dotted lines. Also note that the lwd argument specifies the relative width.

Create a numerical variable x with 200 evenly-spaced values from 0 to 10.
Using the dnorm() function, generate a vector gauss1 of Gaussian probability densities for this range of x values, with mean 2 and standard deviation 0.2.
Using the dnorm() function, generate a vector gauss2 of Gaussian probability densities for this range of x values, with mean 4 and standard deviation 0.5.
Generate a plot of gauss1 vs. x with lines and a y-axis label "Gaussian probability density".
Using the lines() function, add a second dashed line for gauss2 vs. x with relative width 3 (refer to the line type plot to select the lty parameter).

Exercise

The lines() function and line types

Instructions