In a previous post I explained how to create all possible combinations of the levels of two factors using `expand.grid()`

. Another use for this function is to create a regular grid for two variables to create a levelplot or a contour plot.

For example, prescription let’s say that we have fitted a multiple linear regression to predict wood stiffness (stiff, and the response) using basic density (bd) and a measure of microfibril angle (t) as explanatory variables. The regression equation could be something like `stiffness = 3.439 + 0.009 bd - 0.052 t`

. In our dataset bd had a range of 300 to 700 kg m^{-3}, while t had a range from 50 to 70.

We will use the `levelplot()`

function that is part of the `lattice`

package of graphical functions, create a grid for both explanatory variables (every 10 for bd and every 1 for t), predict values of stiffness for all combinations of bd and t, and plot the results.

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library(lattice) # Create grid of data wood = expand.grid(bd = seq(300, 700, 10), t = seq(50, 70, 1)) wood$stiffness = with(wood, 3.439 + 0.009*bd - 0.052*t) levelplot(stiffness ~ bd*t, data = wood, xlab='Basic density', ylab='T') |

This code creates a graph like this. Simple.

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