| get.pi.typed.permute | R Documentation | 
Does permutations to calculate the null distribution of get pi if there were no spatial dependence. Randomly reassigns coordinates to each observation permutations times
get.pi.typed.permute(
  posmat,
  typeA = -1,
  typeB = -1,
  r = 1,
  r.low = rep(0, length(r)),
  permutations,
  data.frame = TRUE
)
posmat | 
 a matrix with columns type, x and y  | 
typeA | 
 the "from" type that we are interested in, -1 is wildcard  | 
typeB | 
 the "to" type that we are interested i, -1 is wildcard  | 
r | 
 the series of spatial distances we are interested in  | 
r.low | 
 the low end of each range....0 by default  | 
permutations | 
 the number of permute iterations  | 
data.frame | 
 logical indicating whether to return results as a data frame (default = TRUE)  | 
pi values for all the distances we looked at
Justin Lessler and Henrik Salje
Other get.pi: 
get.pi(),
get.pi.bootstrap(),
get.pi.ci(),
get.pi.permute(),
get.pi.typed(),
get.pi.typed.bootstrap()
data(DengueSimR02)
r.max<-seq(20,1000,20)
r.min<-seq(0,980,20)
#Lets see if there's a difference in spatial dependence by time case occurs
type<-2-(DengueSimR02[,"time"]<75)
tmp<-cbind(DengueSimR02,type=type)
typed.pi<-get.pi.typed(tmp,typeA=1,typeB=2,r=r.max,r.low=r.min)
typed.pi.type.null<-get.pi.typed.permute(tmp,typeA=1,typeB=2,r=r.max,r.low=r.min,permutations=100)
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