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Last data update: 2014.03.03 |
Median polish Spline.DescriptionThe "splineMPST" is dessigned to represent the variability of effects of spatio - temporal data on a surface, from robust median polish algoritm and planar interpolation. UsagesplineMPST(Grid,Ef_t,MPST,eps, maxiter) Arguments
ValueData frame, where columns show the trend in each spatio - temporal location. ReferencesBerke, O. (2001). Modified median polish kriging and its application to the wolfcamp - aquifer data. Environmetrics, 12(8):731-748.[link] Cressie, N. (1993). Statistics for spatial data. Wiley series in probability and statistics.[link] Examples
## Not run:
data(Metadb)
x<-matrix(0,1,37)
for(i in 1:37){
x[,i] <- 2007 + (seq(0, 36)/12)[i]
}
x<-as.Date (as.yearmon(x), frac = 1)
time = as.POSIXct(x, tz = "GMT")
length(time)
MPST<-ConstructMPst(Metadb[,-c(1:4)],time,pts=Metadb[,2:4],Delta=c(7,6,5))
MpSTData<-MedianPolishM(MPST,eps=0, maxiter=5, na.rm=TRUE)
data(DemMeta)
xy = SpatialPoints(Metadb[,2:4],CRS(proj4string(DemMeta)))
data(HZRMeta)
proj4string(HZRMeta)<-CRS(proj4string(DemMeta))
polygon1 = polygons(HZRMeta)
Gridxy<- spsample(polygon1, cellsize=2000, n=300,"regular")
Grid<-data.frame(Gridxy,over(Gridxy,DemMeta))
colnames(Grid)<-c("East", "North","height")
TendenciaGrilla<-splineMPST(Grid,Ef_t=time[10:15],MPST,eps=0.01, maxiter=2)
IDs = paste("ID",1:length(TendenciaGrilla[,5]))
mydata = data.frame(values = TendenciaGrilla[,5], ID=IDs)
wind.ST1 = STFDF(SpatialPixels(Gridxy),time[10:15],mydata)
stplot(wind.ST1,col.regions=bpy.colors(40),par.strip.text = list(cex=0.7)
,main="Spline median polish: Monthly Precipitation")
## End(Not run)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
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> library(STMedianPolish)
Loading required package: maptools
Loading required package: sp
Checking rgeos availability: TRUE
Loading required package: reshape2
Loading required package: spacetime
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/STMedianPolish/splineMPST.Rd_%03d_medium.png", width=480, height=480)
> ### Name: splineMPST
> ### Title: Median polish Spline.
> ### Aliases: splineMPST
>
> ### ** Examples
>
> ## Not run:
> data(Metadb)
> x<-matrix(0,1,37)
> for(i in 1:37){
+ x[,i] <- 2007 + (seq(0, 36)/12)[i]
+ }
> x<-as.Date (as.yearmon(x), frac = 1)
> time = as.POSIXct(x, tz = "GMT")
> length(time)
[1] 37
>
> MPST<-ConstructMPst(Metadb[,-c(1:4)],time,pts=Metadb[,2:4],Delta=c(7,6,5))
| | | 0% | |== | 3% | |==== | 5% | |====== | 8% | |======== | 11% | |========= | 14% | |=========== | 16% | |============= | 19% | |=============== | 22% | |================= | 24% | |=================== | 27% | |===================== | 30% | |======================= | 32% | |========================= | 35% | |========================== | 38% | |============================ | 41% | |============================== | 43% | |================================ | 46% | |================================== | 49% | |==================================== | 51% | |====================================== | 54% | |======================================== | 57% | |========================================== | 59% | |============================================ | 62% | |============================================= | 65% | |=============================================== | 68% | |================================================= | 70% | |=================================================== | 73% | |===================================================== | 76% | |======================================================= | 78% | |========================================================= | 81% | |=========================================================== | 84% | |============================================================= | 86% | |============================================================== | 89% | |================================================================ | 92% | |================================================================== | 95% | |==================================================================== | 97% | |======================================================================| 100%
>
> MpSTData<-MedianPolishM(MPST,eps=0, maxiter=5, na.rm=TRUE)
>
> data(DemMeta)
> xy = SpatialPoints(Metadb[,2:4],CRS(proj4string(DemMeta)))
>
> data(HZRMeta)
> proj4string(HZRMeta)<-CRS(proj4string(DemMeta))
>
> polygon1 = polygons(HZRMeta)
> Gridxy<- spsample(polygon1, cellsize=2000, n=300,"regular")
>
> Grid<-data.frame(Gridxy,over(Gridxy,DemMeta))
> colnames(Grid)<-c("East", "North","height")
>
> TendenciaGrilla<-splineMPST(Grid,Ef_t=time[10:15],MPST,eps=0.01, maxiter=2)
| | | 0% | |============ | 17% | |======================= | 33% | |=================================== | 50% | |=============================================== | 67% | |========================================================== | 83% | |======================================================================| 100%
>
> IDs = paste("ID",1:length(TendenciaGrilla[,5]))
> mydata = data.frame(values = TendenciaGrilla[,5], ID=IDs)
> wind.ST1 = STFDF(SpatialPixels(Gridxy),time[10:15],mydata)
> stplot(wind.ST1,col.regions=bpy.colors(40),par.strip.text = list(cex=0.7)
+ ,main="Spline median polish: Monthly Precipitation")
> ## End(Not run)
>
>
>
>
>
> dev.off()
null device
1
>
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Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and