Takes a fitted `SpATS`

object produced by `SpATS()`

and produces predictions of the spatial trend on a regular two-dimensional array.

1 | ```
obtain.spatialtrend(object, grid = c(100, 100), ...)
``` |

`object` |
an object of class |

`grid` |
a numeric vector with the number of grid points along the x- and y- coordinates respectively. Atomic values are recycled. The default is 100. |

`...` |
further arguments passed to or from other methods. Not yet implemented. |

For each spatial coordinate, `grid[k]`

equally spaced values between the minimum and the maximum are computed (k = 1, 2). The spatial trend is then predicted on the regular two-dimensional array defined by each combination of the x- and y- coordinate values.

A list with the following components:

`col.p` |
x-coordinate values at which predictions have been computed. |

`row.p` |
y-coordinate values at which predictions have been computed |

`fit` |
a matrix of dimension length(row.p) x length(col.p) with the predicted spatial trend (excluding the intercept). |

`pfit` |
for the PS-ANOVA approach, a list with 5 matrices of dimension length(row.p) x length(col.p) with each predicted spatial component (2 main effects, 2 linear-by-smooth components and 1 smooth-by-smooth component). |

Lee, D.-J., Durban, M., and Eilers, P.H.C. (2013). Efficient two-dimensional smoothing with P-spline ANOVA mixed models and nested bases. Computational Statistics and Data Analysis, 61, 22 - 37.

Rodriguez-Alvarez, M.X, Boer, M.P., van Eeuwijk, F.A., and Eilers, P.H.C. (2016). Spatial Models for Field Trials. Available on arXiv at http://arxiv.org/abs/1607.08255.

`SpATS`

, `plot.SpATS`

, `predict.SpATS`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
library(SpATS)
data(wheatdata)
wheatdata$R <- as.factor(wheatdata$row)
wheatdata$C <- as.factor(wheatdata$col)
m0 <- SpATS(response = "yield", spatial = ~ SAP(col, row, nseg = c(10,20)),
genotype = "geno", fixed = ~ colcode + rowcode, random = ~ R + C,
data = wheatdata, control = list(tolerance = 1e-03))
spat.trend.1 <- obtain.spatialtrend(m0)
spat.trend.2 <- obtain.spatialtrend(m0, grid = c(10, 10))
colors = topo.colors(100)
op <- par(mfrow = c(1,2))
fields::image.plot(spat.trend.1$col.p, spat.trend.1$row.p, t(spat.trend.1$fit),
main = "Prediction on a grid of 100 x 100", col = colors, xlab = "Columns", ylab = "Rows")
fields::image.plot(spat.trend.2$col.p, spat.trend.2$row.p, t(spat.trend.2$fit),
main = "Prediction on a grid of 10 x 10", col = colors, xlab = "Columns", ylab = "Rows")
par(op)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.