Exercise

# Gridded predictions

Constructing the grid is the hard part done. You can now compute kriged
estimates over the grid using the variogram model from before (`v_model`

)
and the grid of `SpatialPixels`

.

Instructions

**100 XP**

The spatial pixel grid of the region, `spgrid`

, and the variogram model of pH, `v_model`

have been pre-defined.

- Use kriging to predict pH in each grid rectangle throughout the study area.
- Call
`krige()`

. - The formula and input data are already specified.
- Pass
`spgrid`

as the new data to predict. - Pass the variogram model to the model argument.

- Call
- Calculate the probability of alkaline samples in each grid rectangle.
- The mean of the predictions is the
`var1.pred`

element of`ph_grid`

. - The variance of the predictions is the
`var1.var`

element of`ph_grid`

. Take the square root to get the standard deviation.

- The mean of the predictions is the
- Plot the alkalinity in each grid rectangle.
- Call
`spplot()`

. - Pass the alkalinity column to the
`zcol`

argument as a string.

- Call