FeedbackTS (version 1.5)

FeedbackTS-package: Analysis of Feedback in Time Series

Description

Analysis of fragmented time directionality to investigate feedbacks in time series. Tools provided by the package allow the analysis of feedback for a single time series and the analysis of feedback for a set of time series collected across a spatial domain.

Details

Package:	FeedbackTS
Type:	Package
Version:	1.5
Date:	2020-01-22
License:	GPL (>=2.0)
Depends:	methods, maps, mapdata, proj4, sp, gstat, automap, date

To analyze feedback in a single time series create a KDD object (Key Day Dataset) with the construction function kdd.from.raw.data and test fragmented time directionality with the function feedback.test.

To analyze the spatial pattern of feedback from a set of time series collected across a spatial domain, create indices of feedback with the function feedback.stats, map the index with map.statistic, krige the index with krige and test spatial variation in feedback with krige.test.

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 {
#### load library
# }
# NOT RUN {
library(FeedbackTS)
# }
# NOT RUN {
#### load data for site 6008 (Callagiddy station)
data(rain.site.6008)

#### 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]


########  ANALYSIS OF FEEDBACK WITH A SINGLE TIME SERIES

#### build KDD objects from raw data (site 6008: Callagiddy station)
## using a threshold value equal to 25
KDD=kdd.from.raw.data(raw.data=rain.site.6008,keyday.threshold=25,nb.days=20,
   col.series=5,col.date=c(2,3,4),na.rm=TRUE,filter=NULL)

#### test feedback and change in feedback with a single data series
## using the thresholded data series
## using difference of means of positive indicator values (i.e. rainfall occurrence)
## computer intensive stage
# }
# NOT RUN {
par(mfrow=c(1,2), mar=c(5.1,4.1,4.1,2.1))
feedback.test(object=KDD, test="feedback", operator="dmpiv", nb.rand=10^3, plots=TRUE)
# }
# NOT RUN {
########  ANALYSIS OF FEEDBACK WITH A SET OF TIME SERIES COLLECTED ACROSS SPACE

#### map of feedback index computed from the whole data series
par(mfrow=c(1,1), mar=c(0,0,0,0))
stat1=rain.feedback.stats[["Feedback.whole.period"]]
map.statistic(coord,stat1,cex.circles=c(3,0.2),
   region=list(border="Australia",xlim=c(110,155)),
   legend=list(x=c(rep(114,3),rep(123,2)),y=-c(37,39.5,42,37,39.5),
      xtext=c(rep(114,3),rep(123,2))+1,ytext=-c(37,39.5,42,37,39.5),digits=2),
  main="Feedback")

#### variogram analysis and kriging of feedback index
## computer intensive stage
# }
# 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)
# }