Spatio-Temporal Kernel Density Estimation with Significant P-Value contours

Description

stkde.sig calculates the three dimensional kernel density estimation of spatio-temporal mixed data,continous space and discrete time. And also obtain the significant p-value contours to indicate the TRUE significant areas by the method of Monte Carlo.

Usage

stkde.sig(xlong,ylat,ztime,xgrids,ygrids,breaks,sim,alpha,nrowspar,...)

Arguments

Details

stkde is a method to conduct the spatio-temporal kernel density estimation with significant p-value contours to indicate the statistically significant area, when the time variable is discrete or categorial variable,not continuous variable.

Value

stkde returns a stkde object, with the following two arrays. Their dimensions are xgrids, ygrids and tlength, respectively:
dens: kernel estimation of the density (cumulative distribution) at the evaluation points.
pvalue: P values for the high density to be significant high values.

Note

This method is important for deleteing the false-positive resutls of stkde.

Author(s)

Zhijie Zhang, epistat@gmail.com

References

Li, Q. and Racine, J.S.Nonparametric Econometrics: Theory and Practice, Princeton University Press. 2007.
Hayield, T. and Racine,J.S. “Nonparametric Econometrics: The np Package,”.Journal of Statistical Software,2008,27(5):http://www.jstatsoft.org/v27/i05/.
Zhang Z, Clark AB, Bivand R, Chen Y, Carpenter TE, Peng W, Zhou Y, Zhao G, Jiang Q.“Nonparametric spatial analysis to detect high-risk regions for schistosomiasis in Guichi, China,”. Trans R Soc Trop Med Hyg. 2009,103(10):1045-1052.

See Also

npudensbw(np), npudens(np),stkde

Examples

## Not run: 
#Example1-uneven number of years
#Dataset1
x1<-c(runif(100,0,1),runif(50,0.67,1))
y1<-c(runif(100,0,1),runif(50,0.67,1))
d1<-data.frame(x1,y1)
colnames(d1)<-c("x","y")
x2<-c(runif(100,0,1),runif(50,0.33,0.67))
y2<-c(runif(100,0,1),runif(50,0.33,0.67))
d2<-data.frame(x2,y2)
colnames(d2)<-c("x","y")
x3<-c(runif(100,0,1),runif(50,0,0.33))
y3<-c(runif(100,0,1),runif(50,0,0.33))
d3<-data.frame(x3,y3)
colnames(d3)<-c("x","y")
d<-rbind(d1,d2,d3)
d$tf<-c(rep(1,150),rep(2,150),rep(3,150))
colnames(d)<-c("xlong","ylat","ztime")
#Running the function
stkde.sig(d[,1],d[,2],d[,3],xgrids=20,ygrids=20,breaks=0.05,sim=3,alpha=0.05,nrowspar=1)
#reports the time spent in garbage collection so far in the R session while GC timing was enabled
gc.time(stkde.sig(d[,1],d[,2],d[,3],xgrids=20,ygrids=20,breaks=0.05,sim=3,alpha=0.05,nrowspar=1))
#Return CPU (and other) times that expr used.
system.time(stkde.sig(d[,1],d[,2],d[,3],xgrids=20,ygrids=20,breaks=0.05,sim=3,alpha=0.05,nrowspar=1))
#determines how much real and CPU time (in seconds) the currently running R process has already taken
proc.time(stkde.sig(d[,1],d[,2],d[,3],xgrids=20,ygrids=20,breaks=0.05,sim=3,alpha=0.05,nrowspar=1))
#
#Example2-even number of years
#Dataset2
x12<-c(runif(100,0,1),runif(50,0.67,1))
y12<-c(runif(100,0,1),runif(50,0.67,1))
d12<-data.frame(x12,y12)
colnames(d12)<-c("x","y")
x22<-c(runif(100,0,1),runif(50,0.33,0.67))
y22<-c(runif(100,0,1),runif(50,0.33,0.67))
d22<-data.frame(x22,y22)
colnames(d22)<-c("x","y")
d2<-rbind(d12,d22)
d2$tf<-c(rep(1,150),rep(2,150))
colnames(d2)<-c("xlong","ylat","ztime")
#Running the function
stkde.sig(d2[,1],d2[,2],d2[,3],xgrids=20,ygrids=20,breaks=0.05,sim=3,alpha=0.05,nrowspar=2)
#reports the time spent in garbage collection so far in the R session while GC timing was enabled
gc.time(stkde.sig(d[,1],d[,2],d[,3],xgrids=20,ygrids=20,breaks=0.05,sim=3,alpha=0.05,nrowspar=2))
#Return CPU (and other) times that expr used.
system.time(stkde.sig(d[,1],d[,2],d[,3],xgrids=20,ygrids=20,breaks=0.05,sim=3,alpha=0.05,nrowspar=2))
#determines how much real and CPU time (in seconds) the currently running R process has already taken
proc.time(stkde.sig(d[,1],d[,2],d[,3],xgrids=20,ygrids=20,breaks=0.05,sim=3,alpha=0.05,nrowspar=2))

## End(Not run) 

Results