我想计算两个数据帧的行之间的距离（不相似性），以便为每个观察找到最接近的聚类。因为我有因子和数值变量，我正在使用Gower距离。由于我想比较两个数据帧（而不是一个矩阵的行之间的差异），gower.dist将是我需要的功能。然而，当我实现它时，我意识到结果与我使用daisy的gower时所得到的结果不同，将行绑定在一起并查看感兴趣的相异矩阵的部分。
我这里仅提供我的数据样本，但是当我计算出与所有数据的不相似之处时，gower.dist经常导致零的不相似，尽管相应的行彼此不相等。为什么？可能是造成不同结果的原因是什么？在我看来，daisys的gower工作正确，gower.dist不是（在这个例子中）。
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)
包含以下数据
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：似乎出现错误/差异是因为数字列中有NA，并且它们似乎被区别对待。我怎样才能使daisy对NAs的处理适应gower.dist？
答案 0 :(得分：2)
由于数据框的数字列中的NA值。请考虑以下代码，以查看两个函数在具有NA值的数字列中的行为方式完全不同（daisy比gower.dist更强大）：
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，当数字变量中存在NA时，使gower.dist函数像菊花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