obtain.spatialtrend: Predictions of the spatial trend from an 'SpATS' object

Description Usage Arguments Details Value References See Also Examples

View source: R/obtain.spatialtrend.R

Description

Takes a fitted SpATS object produced by SpATS() and produces predictions of the spatial trend on a regular two-dimensional array.

Usage

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obtain.spatialtrend(object, grid = c(100, 100), ...)

Arguments

object

an object of class SpATS as produced by SpATS()

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.

Details

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.

Value

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).

References

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. (2017). Correcting for spatial heterogeneity in plant breeding experiments with P-splines. Spatial Statistics (to appear). https://doi.org/10.1016/j.spasta.2017.10.003.

See Also

SpATS, plot.SpATS, predict.SpATS

Examples

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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)

Example output

Effective dimensions
-------------------------
It.     Deviance           R           Cf(col,row)|colf(col,row)|row
  1 686384.843129       1.196       0.962      11.769      15.563
  2  2107.446777       1.234       3.922      12.391      15.060
  3  2079.185180       1.722       6.767      11.306      13.853
  4  2072.650074       2.628       7.316      10.554      12.547
  5  2069.709184       3.827       7.264      10.199      11.474
  6  2067.893125       5.141       7.153      10.046      10.721
  7  2066.842352       6.330       7.061       9.992      10.215
  8  2066.299268       7.234       6.989       9.987       9.886
  9  2066.044505       7.834       6.933      10.006       9.678
 10  2065.929488       8.199       6.889      10.035       9.552
 11  2065.876579       8.411       6.855      10.066       9.477
 12  2065.851059       8.531       6.829      10.095       9.435
 13  2065.838130       8.600       6.809      10.120       9.411
 14  2065.831319       8.639       6.793      10.142       9.400
 15  2065.827632       8.663       6.782      10.160       9.395
 16  2065.825598       8.678       6.773      10.175       9.393
 17  2065.824460       8.688       6.766      10.187       9.394
 18  2065.823818       8.694       6.761      10.196       9.396
Timings:
SpATS 0.763 seconds
All process 1.374 seconds

SpATS documentation built on Nov. 17, 2017, 4:41 a.m.