joincount.mc: Permutation test for same colour join count statistics

In spdep: Spatial Dependence: Weighting Schemes, Statistics and Models

Description

A permutation test for same colour join count statistics calculated by using nsim random permutations of fx for the given spatial weighting scheme, to establish the ranks of the observed statistics (for each colour) in relation to the nsim simulated values.

Usage

joincount.mc(fx, listw, nsim, zero.policy=FALSE, alternative="greater",
 spChk=NULL)

Arguments

fx	a factor of the same length as the neighbours and weights objects in listw
listw	a listw object created for example by nb2listw
nsim	number of permutations
zero.policy	if TRUE assign zero to the lagged value of zones without neighbours, if FALSE assign NA
alternative	a character string specifying the alternative hypothesis, must be one of "greater" (default), or "less".
spChk	should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use get.spChkOption()

Value

A list with class jclist of lists with class htest and mc.sim for each of the k colours containing the following components:

statistic	the value of the observed statistic.
parameter	the rank of the observed statistic.
method	a character string giving the method used.
data.name	a character string giving the name(s) of the data.
p.value	the pseudo p-value of the test.
alternative	a character string describing the alternative hypothesis.
estimate	the mean and variance of the simulated distribution.
res	nsim simulated values of statistic, the final element is the observed statistic

Author(s)

Roger Bivand [e-mail]

References

Cliff, A. D., Ord, J. K. 1981 Spatial processes, Pion, p. 63-5.

See Also

joincount.test

Examples

data(oldcol)
HICRIME <- cut(COL.OLD$CRIME, breaks=c(0,35,80), labels=c("low","high"))
names(HICRIME) <- rownames(COL.OLD)
joincount.mc(HICRIME, nb2listw(COL.nb, style="B"), nsim=99)
joincount.test(HICRIME, nb2listw(COL.nb, style="B"))

Example output

Loading required package: sp
Loading required package: Matrix

	Monte-Carlo simulation of join-count statistic

data:  HICRIME 
weights: nb2listw(COL.nb, style = "B") 
number of simulations + 1: 100 

Join-count statistic for low = 34, rank of observed statistic = 85,
p-value = 0.15
alternative hypothesis: greater
sample estimates:
    mean of simulation variance of simulation 
              30.32323               18.58833 


	Monte-Carlo simulation of join-count statistic

data:  HICRIME 
weights: nb2listw(COL.nb, style = "B") 
number of simulations + 1: 100 

Join-count statistic for high = 54, rank of observed statistic = 100,
p-value = 0.01
alternative hypothesis: greater
sample estimates:
    mean of simulation variance of simulation 
              27.02020               15.73428 


	Join count test under nonfree sampling

data:  HICRIME 
weights: nb2listw(COL.nb, style = "B") 

Std. deviate for low = 1.0141, p-value = 0.1553
alternative hypothesis: greater
sample estimates:
Same colour statistic           Expectation              Variance 
             34.00000              29.59184              18.89550 


	Join count test under nonfree sampling

data:  HICRIME 
weights: nb2listw(COL.nb, style = "B") 

Std. deviate for high = 6.3307, p-value = 1.22e-10
alternative hypothesis: greater
sample estimates:
Same colour statistic           Expectation              Variance 
             54.00000              27.22449              17.88838 

spdep documentation built on Aug. 19, 2017, 3:01 a.m.