We use regression models to predict a system’s behavior by extrapolating from previously measured output values when the system is tested with known input parameter values. The simplest regression model is a straight line. It has the mathematical form:
y = a0 + a1x1
where x1 is the input to the system, a0 is the y-intercept of the line, a1 is the slope, and y is the output value the model predicts.
R provides the function
lm() that generates a linear model from the data contained in a data frame. For this one-factor model, R computes the values of a0 and a1 using the method of least squares. This method finds the line that most closely fits the measured data by minimizing the distances between the line and the individual data points. For the data frame
int00.dat, we compute the model as follows:
> attach(int00.dat) > int00.lm <lm(perf ~ clock)
The first line in this example attaches the
int00.dat data frame to the current workspace. The next line calls the
lm() function and assigns the resulting linear model object to the variable
int00.lm. We use the suffix
.lm to emphasize that this variable contains a linear model. The argument in the
(perf ~ clock), says that we want to find a model where the predictor
clock explains the output
Typing the variable’s name,
int00.lm, by itself causes R to print the argument with which the function
lm() was called, along with the computed coefficients for the regression model.
> int00.lm Call: lm(formula = perf ~ clock) Coefficients: (Intercept) clock 51.7871 0.5863
In this case, the y-intercept is a0 = 51.7871 and the slope is a1 = 0.5863. Thus, the final regression model is:
perf = 51.7871 + 0.5863 ∗ clock.
The following code plots the original data along with the fitted line, as shown in Figure 3.2. The function
abline() is short for (a,b)-line. It plots a line on the active plot window, using the slope and intercept of the linear model given in its argument.
> plot(clock,perf) > abline(int00.lm)