我想计算两个数据帧的行之间的距离（差异），以便为每个观察值找到最接近的聚类。因为我有因子和数值变量，所以我使用高尔距离。因为我想比较两个数据帧（而不是一个矩阵的行之间的差异），所以gower.dist将是我需要的功能。但是，当我实现它时，我意识到结果与使用雏菊的方法时得到的结果不同，将行绑定在一起并查看感兴趣的相异矩阵的一部分。
我仅在此处提供数据样本，但是当我计算所有数据的差异时，尽管对应的行彼此不相等，但gower.dist通常会导致差异为零。为什么？产生不同结果的原因可能是什么？在我看来，daisys的gower正常工作，而gower.dist不是（在此示例中）。
library(cluster)
library(StatMatch)
# Calculate distance using daisy's gower
daisyDist <- daisy(rbind(df,cent),metric="gower")
daisyDist <- as.matrix(daisyDist)
daisyDist <- daisyDist[(nrow(df)+1):nrow(daisyDist),1:nrow(df)] #only look at part where rows from df are compared to (rows of) cent
# Calculate distance using dist.gower
gowerDist <- gower.dist(cent,df)
具有以下数据
df <- structure(list(searchType = structure(c(NA, 1L, 1L, 1L, 1L), .Label = c("1", "2"), class = "factor"), roomMin = structure(c(4L, 1L, 1L, 6L, 6L), .Label = c("10", "100", "150", "20", "255", "30", "40", "50", "60", "70", "Missing[NoInput]"), class = "factor"), roomMax = structure(c(8L, 8L, NA, 10L, 9L), .Label = c("10", "100", "120", "150", "160", "20", "255", "30", "40", "50", "60", "70", "80", "90", "Missing[NoInput]"), class = "factor"), priceMin = c(NA, 73, 60, 29, 11), priceMax = c(35, 11, 1, 62, 23), sizeMin = structure(c(5L, 5L, 5L, 6L, 6L), .Label = c("100", "125", "150", "250", "50", "75", "Missing[NoInput]"), class = "factor"), sizeMax = structure(c(1L, 6L, 5L, 3L, 1L), .Label = c("100", "125", "150", "250", "50", "75", "Missing[NoInput]"), class = "factor"), longitude = c(6.6306, 7.47195, 8.5562, NA, 8.569), latitude = c(46.52425, 46.9512, 47.37515, NA, 47.3929), specificSearch = structure(c(1L, 1L, 1L, 1L, 1L), .Label = c("0", "1"), class = "factor"), objectType = structure(c(NA, 2L, 2L, 2L, 2L), .Label = c("1", "2", "3", "Missing[]"), class = "factor")), .Names = c("searchType", "roomMin", "roomMax", "priceMin", "priceMax", "sizeMin", "sizeMax", "longitude", "latitude", "specificSearch", "objectType"), row.names = c(112457L, 94601L, 78273L, 59172L, 117425L), class = "data.frame")
cent <- structure(list(searchType = structure(c(1L, 1L, 1L), .Label = c("1", "2"), class = "factor"), roomMin = structure(c(1L, 4L, 4L), .Label = c("10", "100", "150", "20", "255", "30", "40", "50", "60", "70", "Missing[NoInput]"), class = "factor"), roomMax = structure(c(6L, 9L, 8L), .Label = c("10", "100", "120", "150", "160", "20", "255", "30", "40", "50", "60", "70", "80", "90", "Missing[NoInput]"), class = "factor"), priceMin = c(60, 33, 73), priceMax = c(103, 46, 23), sizeMin = structure(c(1L, 5L, 5L), .Label = c("100", "125", "150", "250", "50", "75", "Missing[NoInput]"), class = "factor"), sizeMax = structure(c(1L, 2L, 1L), .Label = c("100", "125", "150", "250", "50", "75", "Missing[NoInput]"), class = "factor"), longitude = c(8.3015, 7.42765, 7.6104), latitude = c(47.05485, 46.9469, 46.75125), specificSearch = structure(c(1L, 1L, 1L), .Label = c("0", "1"), class = "factor"), objectType = structure(c(2L, 2L, 2L), .Label = c("1", "2", "3", "Missing[]"), class = "factor")), .Names = c("searchType", "roomMin", "roomMax", "priceMin", "priceMax", "sizeMin", "sizeMax", "longitude", "latitude", "specificSearch", "objectType"), row.names = c(60656L, 66897L, 130650L), class = "data.frame")
谢谢！
编辑：似乎发生错误/差异是因为数字列中有NA，并且它们似乎被区别对待。如何使雏菊对NA的治疗适应gower.dist？
最佳答案
这是由于数据框的数字列中的NA值所致。考虑下面的代码，以了解两个函数如何在具有NA值的数字列上表现完全不同（雏菊比gower.dist更健壮）：
df1 <- rbind(df,cent)
head(df1)
searchType roomMin roomMax priceMin priceMax sizeMin sizeMax longitude latitude specificSearch objectType
112457 <NA> 20 30 NA 35 50 100 6.63060 46.52425 0 <NA>
94601 1 10 30 73 11 50 75 7.47195 46.95120 0 2
78273 1 10 <NA> 60 1 50 50 8.55620 47.37515 0 2
59172 1 30 50 29 62 75 150 NA NA 0 2
117425 1 30 40 11 23 75 100 8.56900 47.39290 0 2
60656 1 10 20 60 103 100 100 8.30150 47.05485 0 2
# only use the numeric column priceMin (4th column) to compute the distance
class(df1[,4])
# [1] "numeric"
df2 <- df1[4]
# daisy output
as.matrix(daisy(df2,metric="gower"))
112457 94601 78273 59172 117425 60656 66897 130650
112457 0 NA NA NA NA NA NA NA
94601 NA 0.0000000 0.2096774 0.70967742 1.0000000 0.2096774 0.64516129 0.0000000
78273 NA 0.2096774 0.0000000 0.50000000 0.7903226 0.0000000 0.43548387 0.2096774
59172 NA 0.7096774 0.5000000 0.00000000 0.2903226 0.5000000 0.06451613 0.7096774
117425 NA 1.0000000 0.7903226 0.29032258 0.0000000 0.7903226 0.35483871 1.0000000
60656 NA 0.2096774 0.0000000 0.50000000 0.7903226 0.0000000 0.43548387 0.2096774
66897 NA 0.6451613 0.4354839 0.06451613 0.3548387 0.4354839 0.00000000 0.6451613
130650 NA 0.0000000 0.2096774 0.70967742 1.0000000 0.2096774 0.64516129 0.0000000
# gower.dist output
gower.dist(df2)
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] NaN NaN NaN NaN NaN NaN NaN NaN
[2,] NaN 0 0 0 0 0 0 0
[3,] NaN 0 0 0 0 0 0 0
[4,] NaN 0 0 0 0 0 0 0
[5,] NaN 0 0 0 0 0 0 0
[6,] NaN 0 0 0 0 0 0 0
[7,] NaN 0 0 0 0 0 0 0
[8,] NaN 0 0 0 0 0 0 0
使用gower.dist函数中的参数rngs修复此问题：
gower.dist(df2, rngs=max(df2, na.rm=TRUE) - min(df2, na.rm=TRUE))
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] NaN NaN NaN NaN NaN NaN NaN NaN
[2,] NaN 0.0000000 0.2096774 0.70967742 1.0000000 0.2096774 0.64516129 0.0000000
[3,] NaN 0.2096774 0.0000000 0.50000000 0.7903226 0.0000000 0.43548387 0.2096774
[4,] NaN 0.7096774 0.5000000 0.00000000 0.2903226 0.5000000 0.06451613 0.7096774
[5,] NaN 1.0000000 0.7903226 0.29032258 0.0000000 0.7903226 0.35483871 1.0000000
[6,] NaN 0.2096774 0.0000000 0.50000000 0.7903226 0.0000000 0.43548387 0.2096774
[7,] NaN 0.6451613 0.4354839 0.06451613 0.3548387 0.4354839 0.00000000 0.6451613
[8,] NaN 0.0000000 0.2096774 0.70967742 1.0000000 0.2096774 0.64516129 0.0000000
因此，当数值变量中存在NA时，使函数gower.dist像雏菊一样工作的方法可以类似于以下方法：
df1 <- rbind(df,cent)
# compute the ranges of the numeric variables correctly
cols <- which(sapply(df1, is.numeric))
rngs <- rep(1, ncol(df1))
rngs[cols] <- sapply(df1[cols], function(x) max(x, na.rm=TRUE) - min(x, na.rm=TRUE))
daisyDist <- as.matrix(daisy(df1,metric="gower"))
gowerDist <- gower.dist(df1)
daisyDist
112457 94601 78273 59172 117425 60656 66897 130650
112457 0.0000000 0.3951059 0.6151851 0.7107843 0.6397059 0.6424374 0.3756990 0.1105551
94601 0.3951059 0.0000000 0.2355126 0.5788530 0.5629176 0.4235379 0.3651002 0.2199324
78273 0.6151851 0.2355126 0.0000000 0.5122549 0.4033046 0.3500130 0.3951874 0.3631533
59172 0.7107843 0.5788530 0.5122549 0.0000000 0.2969639 0.5446623 0.4690421 0.5657812
117425 0.6397059 0.5629176 0.4033046 0.2969639 0.0000000 0.4638003 0.4256891 0.4757460
60656 0.6424374 0.4235379 0.3500130 0.5446623 0.4638003 0.0000000 0.5063082 0.4272755
66897 0.3756990 0.3651002 0.3951874 0.4690421 0.4256891 0.5063082 0.0000000 0.2900150
130650 0.1105551 0.2199324 0.3631533 0.5657812 0.4757460 0.4272755 0.2900150 0.0000000
gowerDist
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 0.0000000 0.3951059 0.6151851 0.7107843 0.6397059 0.6424374 0.3756990 0.1105551
[2,] 0.3951059 0.0000000 0.2355126 0.5788530 0.5629176 0.4235379 0.3651002 0.2199324
[3,] 0.6151851 0.2355126 0.0000000 0.5122549 0.4033046 0.3500130 0.3951874 0.3631533
[4,] 0.7107843 0.5788530 0.5122549 0.0000000 0.2969639 0.5446623 0.4690421 0.5657812
[5,] 0.6397059 0.5629176 0.4033046 0.2969639 0.0000000 0.4638003 0.4256891 0.4757460
[6,] 0.6424374 0.4235379 0.3500130 0.5446623 0.4638003 0.0000000 0.5063082 0.4272755
[7,] 0.3756990 0.3651002 0.3951874 0.4690421 0.4256891 0.5063082 0.0000000 0.2900150
[8,] 0.1105551 0.2199324 0.3631533 0.5657812 0.4757460 0.4272755 0.2900150 0.0000000
关于r - R-结果不同gower.dist和daisy(…，metric =“gower”)，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/40264815/