stkde.base: Spatio-Temporal Kernel Density Estimation

In stam: Spatio-Temporal Analysis and Modelling

Description

stkde.base calculates the three dimensional kernel density estimation of spatio-temporal mixed data,continous space and discrete time.

Usage

stkde.base(xlong,ylat,ztime,xgrids,ygrids,breaks,...)

Arguments

xlong	Projected planar coordinates of longitude.
ylat	Projected planar coordinates of latitude.
ztime	The integer variable,such as YEAR,1990,1991 or 1,2.
xgrids	Number of grids to evaluate the density in the x direction.
ygrids	Number of grids to evaluate the density in the y direction.
breaks	breaks is to be used to specify the interval size for a numeric vector of probabilities with values in [0,1]. Defaults to the 0.05.
...	additional arguments supplied to control various aspects of stkde.These arguments are the same as npudensbw in the np package, see details there.

Details

stkde.base is a method to conduct the spatio-temporal kernel density estimation, when the time variable is discrete or categorial variable,not continuous variable.

Value

stkde.base returns a stkde object, with the following components:
#bw: bandwidth(s), scale factor(s) or nearest neighbours for the data.
#dens: kernel estimation of the density (cumulative distribution) at the evaluation points.

Note

If you are using data of mixed types, then it is advisable to use the data.frame function to construct your input data and not cbind, since cbind will typically not work as intended on mixed data types and will coerce the data to the same type.

Author(s)

Zhijie Zhang, [e-mail]

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/.

See Also

npudensbw(np), npudens(np)

Examples

## Not run: 
# EXAMPLE:Simulated dataset
# We will generate a 3 different stages' case points.
# The higher density are in the off-diagonal direction.
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$tf<-c(rep(1,150),rep(2,150),rep(3,150))
#d is the simulated data
#d[1,]
#plot(d1);points(d2,col="red");points(d3,col="green")
#Key Code
#attach(d)
samkde<-stkde.base(xlong=d$x,ylat=d$y,ztime=d$tf,xgrids=20,ygrids=20,
        breaks=0.05,bwmethod="cv.ml")

## End(Not run) 

stam documentation built on May 2, 2019, 3:28 p.m.