FeedbackTS (version 1.5)

KT.output-class: Class "KT.output"

Description

Output of the krige.test function.

Objects from the Class

Objects are created by calls of the krige.test function.

Slots

krige.output:

Object of class "list", output of the krige function.

subregion:

Object of class "list", two vectors x and y containing the latitudes and the longitudes, respectively, of the vertices of a polygon. The polygon defines a subregion where one supposes variation in the predicted index.

averageKrigingPrediction.rand:

Object of class "numeric" specifying the averages of the kriging predictions in subregion obtained with randomized data (here, a randomization is a random translation on a torus).

averageKrigingPrediction.obs:

Object of class "numeric" specifying the average of the kriging prediction in subregion obtained with observed data.

alternative:

Object of class "character", "greater" or "less".

p.value:

Object of class "numeric", p-value of the test.

Methods

[<-

signature(x = "KT.output", i = "ANY", j = "ANY", value = "ANY")

%\item{[<-}{\code{signature(x = "KT.output")}: Change one of the slots.} %\item{[}{\code{signature(x = "KT.output", i = "ANY", j = "ANY", drop = "ANY")}}

[

signature(x = "KT.output"): Extract one of the slots.

names

signature(x = "KT.output"): Prints slot names.

show

signature(object = "KT.output"): Prints all slots of the KDD object.

summary

signature(object = "KT.output"): Prints summary characteristics of the KDD object.

plot

signature(x = "KT.output"), i="ANY": Graphically displays contents of the object.

References

Soubeyrand, S., Morris, C. E. and Bigg, E. K. (2014). Analysis of fragmented time directionality in time series to elucidate feedbacks in climate data. Environmental Modelling and Software 61: 78-86.

Examples

# NOT RUN {
showClass("KT.output")

#### load data of feedback and change-in-feedback indices in 88 sites across Australia
data(rain.feedback.stats)

#### spatial coordinates of the 88 sites
coord=rain.feedback.stats[,3:4]

#### map of feedback index computed from the whole data series
stat1=rain.feedback.stats[["Feedback.whole.period"]]

#### variogram analysis and kriging of feedback index
# }
# NOT RUN {
par(mfrow=c(2,2), mar=c(5.1,4.1,4.1,2.1))
kr1=krige(coordinates=coord, statistic=stat1,
    grid=list(x=seq(110,155,0.25),y=seq(-45,-11,0.25),border="Australia",
        proj="+proj=lcc +lat_1=-18 +lat_2=-36 +lat0=-25 +lon_0=140",degrees=TRUE),
    plots=TRUE)
# }
# NOT RUN {
#### test spatial variation in feedback index and plot test output
## computer intensive stage
# }
# NOT RUN {
kt1=krige.test(krige.output=kr1,subregion=list(x=c(138,152,152,138),y=-c(40,40,33,33)),
   alternative="greater", nb.rand=2000)
par(mfrow=c(1,2), mar=c(5.1,4.1,4.1,2.1))	
plot(kt1,digits=list(predict=3,pvalue=3),breaks=12) 
# }