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観測ごとに最も近いクラスターを見つけるために、2 つのデータ フレームの行間の距離 (非類似度) を計算したいと考えています。因子と数値変数があるため、ガワー距離を使用しています。2 つのデータ フレームを比較したいので (1 つの行列の行間の非類似性ではなく)、 gower.dist が必要な関数になります。しかし、実装してみると、デイジーのガウアーを使用して行を結合し、非類似度行列の関心のある部分を調べた場合とは結果が異なることに気付きました。

ここではデータのサンプルのみを提供しますが、すべてのデータで非類似度を計算すると、対応する行が互いに等しくなくても、gower.dist の非類似度がゼロになることがよくありました。なんで？そして、異なる結果の理由は何でしょうか? 私の意見では、daisys の gower は正しく機能していますが、gower.dist はそうではありません (この例では)。

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)

次のデータで

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")

ありがとうございました！

編集:数値列に NA があり、それらの処理が異なるように見えるため、エラー/違いが発生したようです。daisy の NA の扱いを gower.dist に適応させるにはどうすればよいですか?

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1 に答える 1

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これは、データ フレームの数値列の NA 値が原因です。次のコードを検討して、2 つの関数が NA 値を持つ数値列でどのようにまったく異なる動作をするかを確認してください (daisy は gower.dist よりも堅牢です)。

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

gower.dist 関数のパラメーター rng でこれを修正します。

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

したがって、数値変数に NA が存在する場合に関数 gower.dist をデイジーのように機能させる方法は、次のようになります。

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
于 2016-10-27T11:10:34.613 に答える