spatialEco (version 1.3-0)

similarity: Ecological similarity

Description

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.

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 {
 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 {
 
# }