R中Jeffries-Matusita距离法的谱可分性
R中Jeffries-Matusita距离法的谱可分性
提问于 2014-07-15 15:36:03
在R中，我用j-m (jeffries matusita)距离法分析数据的可分性，主要目的是计算变量之间的j-m距离，即大于2的变量之间的距离。假设我有以下关于反射率的数据，主要任务是显示四个果树在选定波长处的可分性。
orange <- c(37, 27, 45, 30, 57, 48, 34, 50, 20, 53, 33, 25, 51),
lemon <- c(12, 17, 20, 32, 16, 30, 30, 37, 25, 42, 13, 56, 13),
pear <- c(41, 19, 15, 12, 15, 55, 33, 37, 40, 40, 43, 46, 54),
apple <- c(38, 39, 12, 60, 34, 47, 13, 24, 30, 19, 57, 54, 55)
Wavelength <- c(354, 576, 842, 853, 918, 948, 1142, 1221, 1253, 1322, 1545, 1684, 2407)回答 1
Stack Overflow用户
回答已采纳
发布于 2014-07-15 16:57:39
因此，您需要一个接受任意距离函数的距离方法，并且需要定义JM距离。后者可在this post中使用。对于前者，我们在包dist(...)中使用proxy函数，它允许指定任意函数来计算成对距离。
jm.dist <- function ( Vector.1 , Vector.2 ) {
# this function adapted from:
# https://stats.stackexchange.com/questions/78849/measure-for-separability
Matrix.1 <- as.matrix (Vector.1)
Matrix.2 <- as.matrix (Vector.2)
mean.Matrix.1 <- mean ( Matrix.1 )
mean.Matrix.2 <- mean ( Matrix.2 )
mean.difference <- mean.Matrix.1 - mean.Matrix.2
cv.Matrix.1 <- cov ( Matrix.1 )
cv.Matrix.2 <- cov ( Matrix.2 )
p <- ( cv.Matrix.1 + cv.Matrix.2 ) / 2
# calculate the Bhattacharryya index
bh.distance <- 0.125 *t ( mean.difference ) * p^ ( -1 ) * mean.difference +
0.5 * log (det ( p ) / sqrt (det ( cv.Matrix.1 ) * det ( cv.Matrix.2 )))
# calculate Jeffries-Matusita
# following formula is bound between 0 and 2.0
jm.distance <- 2 * ( 1 - exp ( -bh.distance ) )
# also found in the bibliography:
# jm.distance <- 1000 * sqrt ( 2 * ( 1 - exp ( -bh.distance ) ) )
# the latter formula is bound between 0 and 1414.0
return(jm.distance)
}
df <- data.frame(orange,lemon,pear,apple)
library(proxy)
dist(df,method=jm.dist,by_rows=FALSE)
# orange lemon pear
# lemon 0.24530946
# pear 0.04906073 0.09034789
# apple 0.05878462 0.14807198 0.01435419注意，一旦加载了proxy库，您就屏蔽了默认的dist(...)函数。
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https://stackoverflow.com/questions/24762383
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