similarity
Ecological similarity
Uses row imputation to identify "k" ecological similar observations
Usage
similarity(
x,
k = 4,
method = "mahalanobis",
frequency = TRUE,
scale = TRUE,
ID = NULL
)Arguments
- x
data.frame containing ecological measures
- k
Number of k nearest neighbors (kNN)
- method
Method to compute multivariate distances c("mahalanobis", "raw", "euclidean", "ica")
- frequency
Calculate frequency of each reference row (TRUE/FALSE)
- scale
Scale multivariate distances to standard range (TRUE/FALSE)
- ID
Unique ID vector to use as reference ID's (rownames). Must be unique and same length as number of rows in x
Value
data.frame with k similar targets and associated distances. If frequency = TRUE the freq column represents the number of times a row (ID) was selected as a neighbor.
Note
This function uses row-based imputation to identify k similar neighbors for each observation. Has been used to identify offsets based on ecological similarity.
References
Evans, J.S., S.R. Schill, G.T. Raber (2015) A Systematic Framework for Spatial Conservation Planning and Ecological Priority Design in St. Lucia, Eastern Caribbean. Chapter 26 in Central American Biodiversity : Conservation, Ecology and a Sustainable Future. F. Huettman (eds). Springer, NY.
Examples
# NOT RUN {
library(sp)
data(pu)
kNN <- similarity(pu@data[2:ncol(pu)], k = 4, frequency = FALSE,
ID = pu@data$UNIT_ID)
# }
# NOT RUN {
kNN <- similarity(pu@data[2:ncol(pu)], k = 4, frequency = TRUE,
ID = pu@data$UNIT_ID)
p <- kNN$freq
clr <- c("#3288BD", "#99D594", "#E6F598", "#FEE08B",
"#FC8D59", "#D53E4F")
p <- ifelse(p <= 0, clr[1],
ifelse(p > 0 & p < 10, clr[2],
ifelse(p >= 10 & p < 20, clr[3],
ifelse(p >= 20 & p < 50, clr[4],
ifelse(p >= 50 & p < 100, clr[5],
ifelse(p >= 100, clr[6], NA))))))
plot(pu, col=p, border=NA)
legend("topleft", legend=c("None","<10","10-20",
"20-50","50-100",">100"),
fill=clr, cex=0.6, bty="n")
box()
# }
# NOT RUN {
# }