Typical usage is to create a generic linear model with the
lm(…) command, and supply that as the input to this function.
For example, a general design of experiments with 4 factors: A, B, C, and D can be built using
lsmodel <- lm(y ~ A*B*C*D), and then the 2^4=16 coefficients visualized with this function using
paretoPlot(lsmodel). Since the largest magnitude coefficients are mostly of interest, the coefficient bars are shown sorted from largest to smallest absolute magnitude. The sign information is retained though with the bar's colour: grey for negative and black for positive coefficients.
The coefficients are the exact coefficients from the linear model. When the linear model is in coded units (i.e. -1 for the low level, and +1 for the high level), then the coefficients represent the change in the
experiment's response variable (y), for a 2-unit (not 1-unit) change in the input variable. This interpretation of course assumes the factors are independent, which is the case in a full factorial.
Please see the reference for more details.