# lee

0th

Percentile

##### Compute Lee's statistic

A simple function to compute Lee's L statistic for bivariate spatial data;

$$L(x,y) = \frac{n}{\sum_{i=1}^{n}(\sum_{j=1}^{n}w_{ij})^2} \frac{\sum_{i=1}^{n}(\sum_{j=1}^{n}w_{ij}(x_i-\bar{x})) ((\sum_{j=1}^{n}w_{ij}(y_j-\bar{y}))}{\sqrt{\sum_{i=1}^{n}(x_i - \bar{x})^2} \sqrt{\sum_{i=1}^{n}(y_i - \bar{y})^2}}$$

Keywords
spatial
##### Usage
lee(x, y, listw, n, S2, zero.policy=NULL, NAOK=FALSE)
##### Arguments
x
a numeric vector the same length as the neighbours list in listw
y
a numeric vector the same length as the neighbours list in listw
listw
a listw object created for example by nb2listw
n
number of zones
S2
Sum of squared sum of weights by rows.
zero.policy
default NULL, use global option value; if TRUE assign zero to the lagged value of zones without neighbours, if FALSE assign NA
NAOK
if 'TRUE' then any 'NA' or 'NaN' or 'Inf' values in x are passed on to the foreign function. If 'FALSE', the presence of 'NA' or 'NaN' or 'Inf' values is regarded as an error.

a list of

##### References

Lee (2001). Developing a bivariate spatial association measure: An integration of Pearson's r and Moran's I. J Geograph Syst 3: 369-385

lee.mc

• lee
##### Examples
data(boston)
lw<-nb2listw(boston.soi)

x<-boston.c$CMEDV y<-boston.c$CRIM