美好的一天
是否有可能根据PAM相异性聚类分析的输出生成一个图，其中多边形围绕聚类的外点绘制？
我目前使用函数clusplot实现了类似的功能，但是我更感兴趣的是看到使用直线划分的簇。
# Installing packages
library(cluster)
library(fpc)
library(ggplot2)
library(ggfortify)
#Importing Koeberg matrix into R
KoebergAllCSV <- read.csv("C:/R/Koeberg Cluster/KoebergAllCSV.csv", row.names=1, sep=";")
#Checking if data is in the correct format/Checking class/mode of each column
sapply(KoebergAllCSV, mode)
sapply(KoebergAllCSV, class)
#Creating gower dissimilarity matrix using function "daisy"
#specifying variable type(numerics all ratioscaled and log transformed)
#and weighting all columns as 1
Koeberg.Diss = daisy(KoebergAllCSV, metric = "gower", type = list(logratio = c("Mass", "EF")), weights = rep.int(1,5))
attributes(Koeberg.Diss)
#Determine k
pamk(Koeberg.Diss, krange=2:50, criterion="asw", usepam=TRUE, scaling=FALSE, diss=TRUE, critout=FALSE)
#Run cluster analysis using PAM (Partitioning around medoids)
pam_fit= pam(Koeberg.Diss, diss = TRUE, k = 28)
#Export cluster info
KoebergClusInfo = paste("KoebergClusInfo", ".txt")
write.table(pam_fit$clustering, file = KoebergClusInfo, sep=",")
## Default S3 method:
clusplot(Koeberg.Diss, pam_fit$clustering, diss = TRUE,
stand = FALSE,
lines = 0, labels= 4, xlim = c(-1,1), plotchar = TRUE, span = TRUE,
shade = TRUE, color = TRUE, col.p = "black",
main = "Koeberg gower/pam Clusterplot",
verbose = getOption("verbose"))
我知道ggplot2中的autoplot函数接受pam类的对象，但是在尝试将它用于我的数据并用
替换上面的clusplot函数时autoplot(pam(pam_fit), frame = TRUE)
或
autoplot(pam(Koeberg.Diss, diss = TRUE, k = 28), frame = TRUE)
我收到以下错误......
pam（pam_fit）出错：x不是数字数据框或矩阵。
和
as.data.frame.default（x [[i]]，可选= TRUE时出错， stringsAsFactors = stringsAsFactors）：不能强制类＆＃34;＆＃34;弃权＆＃34;＆＃34; 到data.frame分别......
我对R相对较新，并在这些论坛上发帖提问，所以任何帮助都会受到大力赞赏。
编辑：使用factoextra包中的fviz_cluster（）使其工作
# Installing packages
library(cluster)
library(fpc)
library(factoextra)
#Importing Koeberg matrix into R
KoebergAllCSV <- read.csv("C:/R/Koeberg Cluster/KoebergAllCSV.csv",
row.names=1, sep=";")
#creating gower dissimilarity matrix using daisy
Koeberg.Gower = as.matrix(daisy(KoebergAllCSV, metric = "gower", type =
list(logratio = c("Mass", "EF"))))
attributes(Koeberg.Gower)
pamk(Koeberg.Gower, krange=2:50, criterion="asw", usepam=TRUE,
scaling=FALSE, diss=TRUE, critout=FALSE)
Koeberg.Pam = pam(Koeberg.Gower, 28, diss = TRUE, keep.diss = TRUE)
fviz_cluster(object = list(data=Koeberg.Gower, cluster =
Koeberg.Pam$clustering), geom = c("point", "text"), ellipse.type =
"convex", stand = FALSE)
fviz_silhouette(silhouette(Koeberg.Pam))
答案 0 :(得分：0)
# Installing packages
library(cluster)
library(fpc)
library(factoextra)
#Importing Koeberg matrix into R
KoebergAllCSV <- read.csv("C:/R/Koeberg Cluster/KoebergAllCSV.csv",
row.names=1, sep=";")
#creating gower dissimilarity matrix using daisy
Koeberg.Gower = as.matrix(daisy(KoebergAllCSV, metric = "gower", type =
list(logratio = c("Mass", "EF"))))
attributes(Koeberg.Gower)
pamk(Koeberg.Gower, krange=2:50, criterion="asw", usepam=TRUE,
scaling=FALSE, diss=TRUE, critout=FALSE)
Koeberg.Pam = pam(Koeberg.Gower, 28, diss = TRUE, keep.diss = TRUE)
fviz_cluster(object = list(data=Koeberg.Gower, cluster =
Koeberg.Pam$clustering), geom = c("point", "text"), ellipse.type =
"convex", stand = FALSE)
fviz_silhouette(silhouette(Koeberg.Pam))
使用的数据：
"KoebergAllCSV"
structure(list(Mass = c(157000, 775, 197, 15000, 3250, 628, 1815,
2070, 2000, 1218, 614, 536, 379, 235, 800, 672, 1960, 768, 1540,
1790, 3500, 7450, 4030, 2200, 830, 1180, 1310, 955, 590, 1168,
820, 790, 5000, 883, 824, 280, 184, 941, 293, 1250, 3900, 1700,
925, 220, 1040, 510, 690, 600, 539, 1018, 122, 1086, 118, 737,
370, 1236, 5820, 229, 226, 220, 305.5, 94.5, 390, 198, 445, 623,
1100, 377, 340, 418, 326, 202, 139, 47, 35.1, 46.1, 580, 1150,
66, 44, 50, 30, 34.2, 30, 91, 71, 59, 78.9, 110, 405, 19.5, 73,
64, 39, 54, 39, 37, 48, 21.2, 26.3, 24.2, 29, 15.2, 35, 16.1,
16.8, 29.7, 12.5, 55, 612, 630, 865, 22.4, 8.6, 47.3, 32.5, 28.8,
17.3, 38, 23.5, 22, 15.5, 18.1, 34, 23, 13.1, 13, 14.7, 19.1,
14, 18.6, 15.5, 37, 14.5, 24.6, 25, 28.5, 50.8, 52, 68.8, 76.1,
100, 85, 158, 113, 88, 25.6, 13, 10.2, 30.5, 38, 55, 45.5, 30,
52, 11, 17.8, 29, 13, 23.2, 38, 21, 25, 27.3, 427, 1572, 78.9,
15, 61, 212.9, 700, 11.1, 44, 29.6, 124, 3200, 5800, 5300, 950,
62.4, 205, 270, 93, 40.2, 102, 240, 90, 33, 16.6, 39.2, 47, 60.8,
13, 20.8, 8, 11, 165000, 180000, 63600, 11400, 21200, 41000,
11300, 840000, 240000, 320000, 900, 4090, 1250, 19000, 19000,
6400, 2610, 47, 4500, 1258, 238, 55, 113, 9990, 5360, 17800,
110.1973216, 238.1629085, 89.33169378, 245.0708356, 83.49190575,
7.323754897, 17.91558243, 2.259871723, 1.992123644, 78.63046291,
235.6804221, 413.5582987, 486.5966599, 7.418054089, 8.4510848,
8.4510848, 42.83324573, 8.4510848, 3.14445177, 2000, 496.2334891,
119.4158615, 805.4349144, 8.212468482, 25.0905618), Diet = structure(c(4L,
2L, 2L, 6L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 5L, 4L, 5L, 5L, 5L, 5L, 2L, 5L, 5L, 5L, 5L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
5L, 5L, 5L, 2L, 2L, 2L, 5L, 1L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 4L, 5L, 5L, 5L, 6L, 5L, 3L, 1L, 1L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 5L, 5L, 5L, 2L, 2L, 2L, 3L, 3L, 5L, 3L, 3L, 5L,
5L, 5L, 5L, 3L, 3L, 5L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 3L,
3L, 5L, 5L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 5L, 5L, 3L, 3L, 3L, 3L,
3L, 5L, 5L, 2L, 3L, 3L, 2L, 3L, 3L, 1L, 2L, 1L, 2L, 5L, 2L, 5L,
3L, 3L, 3L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 2L, 2L, 3L, 5L, 1L, 1L, 1L, 5L, 5L, 2L, 2L, 1L,
1L, 1L, 5L, 2L, 3L, 2L, 2L, 2L, 5L, 2L, 5L, 3L, 5L, 5L, 3L, 3L,
5L, 3L, 3L, 4L, 6L, 6L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L,
4L, 1L, 1L, 1L, 3L, 4L, 4L, 4L, 5L, 6L, 1L, 1L, 1L, 1L, 6L, 1L,
1L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
4L, 1L, 1L, 1L, 3L, 3L), .Label = c("A", "B", "C", "D", "E",
"F"), class = "factor"), Time = structure(c(3L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 1L, 2L, 3L, 3L, 3L, 1L, 4L, 3L, 3L, 3L, 3L, 3L, 3L,
1L, 2L, 1L, 3L, 3L, 4L, 2L, 3L, 1L, 3L, 2L, 3L, 2L, 3L, 3L, 1L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,
3L, 1L, 3L, 3L, 3L, 1L, 3L, 1L, 3L, 1L, 1L, 1L, 4L, 3L, 3L, 1L,
3L, 3L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 1L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 4L, 1L, 4L, 4L,
1L, 4L, 1L, 3L, 1L, 4L, 1L, 1L, 1L, 4L, 4L, 1L, 4L, 4L, 3L, 3L,
4L, 4L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
1L, 3L), .Label = c("Cat", "Cr", "Di", "No"), class = "factor"),
Space = structure(c(5L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 5L, 2L,
2L, 2L, 5L, 2L, 2L, 2L, 5L, 2L, 5L, 2L, 2L, 5L, 5L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 5L, 1L, 1L, 1L, 5L, 1L, 1L, 1L,
3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 5L, 5L, 5L, 5L, 2L, 2L, 2L,
2L, 5L, 5L, 4L, 2L, 2L, 2L, 2L, 2L, 5L, 5L, 2L, 2L, 2L, 5L,
5L, 5L, 5L, 5L, 3L, 3L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 5L, 3L, 3L, 3L, 3L, 5L, 5L, 5L,
1L, 1L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 5L, 1L, 3L, 3L, 3L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 5L, 5L, 5L, 1L,
3L, 5L, 5L, 3L, 3L, 5L, 5L, 5L, 5L, 3L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 2L, 5L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 2L,
2L, 1L, 1L, 5L, 5L, 2L, 2L, 5L, 2L, 1L, 5L, 3L, 5L, 1L, 3L,
5L, 5L, 5L, 1L, 3L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 5L, 4L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L,
5L, 4L, 4L, 5L, 5L, 5L, 5L, 3L, 5L, 5L, 4L, 5L), .Label = c("Ae",
"Aq", "Ar", "Fo", "Te"), class = "factor"), EF = c(36274.12643,
974.5491757, 383.4606456, 15194.1663, 4179.125464, 1043.599331,
1739.739739, 1902.677158, 1858.620513, 1325.913225, 831.6334703,
758.1419376, 598.7459669, 432.4008244, 995.8492032, 1104.982804,
1833.224631, 968.5460848, 1555.574839, 2526.177199, 2720.81891,
4551.218864, 2995.035921, 1983.25768, 1021.131045, 1297.600326,
1393.320578, 1123.496167, 809.3558665, 1288.599252, 1012.736663,
987.3550419, 3468.868783, 1065.095472, 1016.098313, 487.1962293,
366.0414243, 1112.253632, 502.488525, 1349.53769, 2928.89833,
1663.891544, 1099.339465, 413.4082553, 1190.663398, 732.8997761,
900.420823, 818.6726853, 354.4240516, 652.168291, 85.2606497,
693.8895747, 270.483605, 941.7478954, 589.026282, 1339.226017,
3846.819879, 533.5361702, 528.1460593, 517.3161179, 666.1149499,
269.8814521, 803.9152978, 477.0047921, 889.8724349, 1153.045225,
1786.335023, 783.201274, 723.3180648, 640.0447608, 540.3690726,
390.0619447, 302.4004117, 144.5066856, 118.4522857, 142.6164524,
799.9885345, 1275.042111, 182.0952927, 543.7188737, 634.8358004,
341.8068213, 400.635478, 341.8068213, 1311.800923, 191.3798609,
168.7094925, 205.6358063, 257.8562828, 626.4208001, 79.37849663,
195.0348258, 178.3190991, 127.263627, 158.8361858, 127.263627,
122.7819915, 144.6363257, 82.97394225, 96.07341942, 90.78786186,
102.6748266, 66.17393783, 116.6808568, 68.813704, 70.83430724,
104.3536694, 57.93381766, 158.6645733, 816.6280747, 832.8847551,
1033.247332, 86.13942963, 44.9254345, 143.1986471, 110.9466069,
102.1927841, 72.26112014, 123.3917674, 88.99374555, 85.0904386,
67.05928121, 74.51689843, 114.4034188, 87.7017517, 59.81055334,
59.49970587, 64.68582581, 77.29226408, 62.57493448, 75.91055058,
67.05928121, 121.1742978, 64.08606129, 91.80560821, 92.81807245,
101.4677065, 150.3213487, 152.7269268, 184.7538415, 197.8677001,
238.2502359, 213.3232585, 325.1772534, 258.896799, 218.4145451,
94.32710718, 59.49970587, 50.45235653, 106.2569217, 123.3917674,
158.6645733, 139.4700925, 105.0692888, 152.7269268, 53.11049696,
73.67479581, 102.6748266, 59.49970587, 88.21961721, 123.3917674,
82.44084978, 92.81807245, 98.54258242, 649.397604, 1577.514936,
202.7896107, 65.58060147, 170.2384385, 404.275036, 909.2871722,
53.43834074, 136.3267745, 104.1146163, 265.4017335, 2559.743414,
3837.812585, 3609.284914, 1119.48705, 196.0571475, 393.9976926,
605.6763891, 266.5768403, 139.7476799, 286.2283703, 438.6449711,
224.9201933, 112.1044413, 70.25978867, 126.0282381, 142.5804177,
169.8586911, 59.49970587, 81.90613014, 42.76953519, 53.11049696,
44893.11086, 48012.29543, 21505.57155, 5704.435068, 9209.019243,
15323.26221, 5665.766265, 157697.8254, 59952.20689, 74861.38869,
616.5285774, 2297.756619, 820.2217331, 23289.68486, 8728.776034,
3390.680499, 1555.167143, 82.25108625, 2783.313695, 2329.752262,
567.6985933, 163.9110073, 301.8499294, 4992.739194, 2906.435392,
8247.673366, 12.81581191, 25.42711978, 10.63408241, 26.08172622,
10.0137771, 1.178076499, 2.549050089, 0.353356528, 0.31350088,
9.49371787, 25.19136319, 41.52955076, 47.98985328, 1.091673606,
1.235456699, 1.235456699, 5.76431571, 1.235456699, 0.483456886,
112.0018952, 48.83385255, 13.76461928, 75.11335195, 1.274157763,
3.438909954)), .Names = c("Mass", "Diet", "Time", "Space",
"EF"), class = "data.frame", row.names = c("CommonOstrich", "GreatCrestedGrebe",
"LittleGrebe", "GreatWhitePelican", "White-breastedCormorant",
"ReedCormorant", "AfricanDarter", "GreyHeron", "Black-headedHeron",
"PurpleHeron", "LittleEgret", "Yellow-billedEgret", "CattleEgret",
"Green-backedHeron", "Black-crownedNight-Heron", "HamerkopHamerkop",
"AfricanSacredIbis", "GlossyIbis", "HadedaIbis", "AfricanSpoonbill",
"GreaterFlamingo", "Spur-wingedGoose", "EgyptianGoose", "SouthAfricanShelduck",
"CapeShoveler", "AfricanBlackDuck", "Yellow-billedDuck", "Red-billedTeal",
"CapeTeal", "SouthernPochard", "MaccoaDuck", "White-backedDuck",
"Secretarybird", "PeregrineFalcon", "LannerFalcon", "RockKestrel",
"LesserKestrel", "Yellow-billedKite", "Black-shoulderedKite",
"BootedEagle", "AfricanFish-eagle", "JackalBuzzard", "SteppeBuzzard",
"Rufous-chestedSparrowhawk", "BlackSparrowhawk", "AfricanGoshawk",
"AfricanMarsh-harrier", "BlackHarrier", "Grey-wingedFrancolin",
"CapeSpurfowl", "CommonQuail", "HelmetedGuineafowl", "BlackCrake",
"AfricanPurpleSwamphen", "CommonMoorhen", "Red-knobbedCoot",
"BlueCrane", "CrownedLapwing", "BlacksmithLapwing", "RuffRuff",
"CommonGreenshank", "WoodSandpiper", "PiedAvocet", "Black-wingedStilt",
"WaterThick-knee", "SpottedThick-knee", "KelpGull", "Grey-headedGull",
"Hartlaub'sGull", "SpeckledPigeon", "Red-eyedDove", "CapeTurtle-dove",
"LaughingDove", "NamaquaDove", "Klaas'sCuckoo", "DiderickCuckoo",
"BarnOwl", "SpottedEagle-owl", "Fiery-neckedNightjar", "CommonSwift",
"AfricanBlackSwift", "White-rumpedSwift", "HorusSwift", "LittleSwift",
"AlpineSwift", "SpeckledMousebird", "White-backedMousebird",
"Red-facedMousebird", "PiedKingfisher", "GiantKingfisher", "MalachiteKingfisher",
"EuropeanBee-eater", "AfricanHoopoe", "AcaciaPiedBarbet", "GreaterHoneyguide",
"LesserHoneyguide", "CardinalWoodpecker", "Large-billedLark",
"Grey-backedSparrowlark", "Red-cappedLark", "BarnSwallow", "White-throatedSwallow",
"Pearl-breastedSwallow", "GreaterStripedSwallow", "RockMartin",
"Brown-throatedMartin", "BandedMartin", "Black(Southernrace)Saw-wing",
"Fork-tailedDrongo", "PiedCrow", "CapeCrow", "White-neckedRaven",
"GreyTit", "CapePenduline-tit", "CapeBulbul", "CappedWheatear",
"FamiliarChat", "AfricanStonechat", "CapeRobin-chat", "KarooScrub-robin",
"LesserSwamp-warbler", "AfricanReed-warbler", "LittleRush-warbler",
"CapeGrassbird", "Long-billedCrombec", "Bar-throatedApalis",
"CloudCisticola", "Grey-backedCisticola", "Levaillant'sCisticola",
"AfricanDuskyFlycatcher", "Chestnut-ventedTit-babbler", "Layard'sTit-babbler",
"FiscalFlycatcher", "CapeBatis", "AfricanParadise-flycatcher",
"CapeWagtail", "AfricanPipit", "CapeLongclaw", "Common(Southern)Fiscal",
"SouthernBoubou", "Bokmakierie", "CommonStarling", "WattledStarling",
"Red-wingedStarling", "PiedStarling", "CapeSugarbird", "MalachiteSunbird",
"Orange-breastedSunbird", "SouthernDouble-collaredSunbird", "HouseSparrow",
"CapeSparrow", "CapeWeaver", "SouthernMasked-weaver", "SouthernRedBishop",
"YellowBishop", "CommonWaxbill", "Pin-tailedWhydah", "CapeCanary",
"Black-headedCanary", "BrimstoneCanary", "White-throatedCanary",
"YellowCanary", "Streaky-headedSeedeater", "CapeBunting", "RockDove",
"MallardDuck", "OliveThrush", "CapeWhite-eye", "CapeLong-billedLark",
"Burchell'sCoucal", "SouthernBlackKorhaan", "KarooPrinia", "CapeClapperLark",
"SouthernGrey-headedSparrow", "LittleBittern", "BlackStork",
"Verreaux'sEagle", "MartialEagle", "AfricanHarrier-Hawk", "BlackrumpedButtonquail",
"AfricanRail", "AfricanJacana", "CommonSandpiper", "LittleStint",
"White-wingedTern", "NamaquaSandgrouse", "Red-chestedCuckoo",
"KarooLark", "SandMartin", "SombreGreenbul", "MountainChat",
"Ant-eatingChat", "ZittingCisticola", "SpottedFlycatcher", "FairyFlycatcher",
"DuskySunbird", "RedHartebeest", "BlueWildebeest", "Bontebok",
"CapeGrysbok", "CommonDuiker", "Springbok", "Steenbok", "CommonEland",
"Gemsbok", "PlainsZebra", "YellowMongoose", "LargeGreyMongoose",
"SmallGreyMongoose", "CapePorcupine", "Caracal", "AfricanWildCat",
"Small-spottedGenet", "CapeGoldenMole", "ScrubHare", "CapeDuneMole-Rat",
"VleiRat", "FourStripedGrassMouse", "CapeGerbil", "Black-BackedJackal",
"Bat-earedFox", "AfricanClawlessOtter", "HeraldSnake", "RhombicEgg-eater",
"SpottedHarlequinSnake", "OliveHouseSnake", "SpottedHouseSnake",
"Knox'sDesertLizard", "NamaquaDwarfChameleon", "Austen'sThick-toedGecko",
"OcelatedThick-toedGecko", "CrossedWhipSnake", "CapeWhipSnake",
"Spotted/RhombicSkaapsteker", "MoleSnake", "Short-leggedseps",
"SilveryDwarfBurrowingSkink", "BloubergDwarfBurrowingSkink",
"CapeSkink", "Red-SidedSkink", "VariegatedSkink", "AngulateTortoise",
"Boomslang", "KarooWhipSnake", "CapeCobra", "Delalande'sBeakedBlindSnake",
"CapeGirdledLizard"))