Description Usage Arguments Value Author(s) See Also Examples
Does permutations to calculate the null distribution of get pi if there were no spatial dependence. Randomly reassigns coordinates to each observation permutations times
1 2 3 4 5 6 7 8 9 | 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.bootstrap()
,
get.pi.ci()
,
get.pi.permute()
,
get.pi.typed.bootstrap()
,
get.pi.typed()
,
get.pi()
1 2 3 4 5 6 7 8 9 10 11 | 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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