File: donttestexamples.R

package info (click to toggle)
r-cran-prabclus 2.3-1-1
  • links: PTS, VCS
  • area: main
  • in suites: bullseye, sid
  • size: 1,396 kB
  • sloc: sh: 13; makefile: 2
file content (252 lines) | stat: -rw-r--r-- 11,531 bytes parent folder | download | duplicates (3)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
library(prabclus)

# example(prabclust)
data(kykladspecreg)
data(nb)
set.seed(1234)
x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb)
# If you want to use your own ASCII data files, use
# x <- prabinit(file="path/prabmatrixfile",
# neighborhood="path/neighborhoodfile")
print(prabclust(x))

# Here is an example for species delimitation with codominant markers;
# only 50 individuals were used in order to have a fast example. 
data(tetragonula)
ta <- alleleconvert(strmatrix=tetragonula[1:50,])
tai <- alleleinit(allelematrix=ta)
print(prabclust(tai))

# Here is an example for species delimitation with dominant markers;
# only 50 individuals were used in order to have a fast example.
# You may want to use stressvals to choose mdsdim.
data(veronica)
vei <- prabinit(prabmatrix=veronica[1:50,],distance="jaccard")
print(prabclust(vei,mdsmethod="kruskal",mdsdim=3))

# example(crmatrix)
  options(digits=3)
  data(kykladspecreg)
  data(nb)
  set.seed(1234)
  x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb)
  xc <- prabclust(x)

  crmatrix(x,xc)
  crmatrix(x,xc, percentages=TRUE)


# example(lociplots)
  options(digits=4)
  data(veronica)
  vei <- prabinit(prabmatrix=veronica[1:50,],distance="jaccard")
  ppv <- prabclust(vei)
  veloci <- prabinit(prabmatrix=veronica[1:50,],rows.are.species=FALSE)
  velociclust <- prabclust(veloci,nnk=0)
  lociplots(ppv,velociclust$clustering,veloci,lcluster=3)


# Results:

# R version 3.1.2 (2014-10-31) -- "Pumpkin Helmet"
# Copyright (C) 2014 The R Foundation for Statistical Computing
# Platform: x86_64-pc-linux-gnu (64-bit)
# 
# R is free software and comes with ABSOLUTELY NO WARRANTY.
# You are welcome to redistribute it under certain conditions.
# Type 'license()' or 'licence()' for distribution details.
# 
# R is a collaborative project with many contributors.
# Type 'contributors()' for more information and
# 'citation()' on how to cite R or R packages in publications.
# 
# Type 'demo()' for some demos, 'help()' for on-line help, or
# 'help.start()' for an HTML browser interface to help.
# Type 'q()' to quit R.
# 
# > library(prabclus)
# Loading required package: MASS
# Loading required package: mclust
# Package 'mclust' version 4.4
# Type 'citation("mclust")' for citing this R package in publications.
# > 
# > # example(prabclust)
# > data(kykladspecreg)
# > data(nb)
# > set.seed(1234)
# > x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb)
# > # If you want to use your own ASCII data files, use
# > # x <- prabinit(file="path/prabmatrixfile",
# > # neighborhood="path/neighborhoodfile")
# > print(prabclust(x))
# * Clustered presence-absence matrix * 
# 
# Clustered:  4 -dim. MDS result from method  classical 
# 
# Noise-detector NNclean has been used with k= 2 
# NNclean is explained in S. Byers and A. E. Raftery, JASA 95 (1998), 781-794
# A Normal mixture model with noise component (mclust) has been used.
# Mixture component memberships:
#  [1] 0 1 0 2 2 8 6 0 7 0 2 0 0 4 1 6 6 8 4 0 0 0 4 1 4 0 6 5 3 1 3 5 0 6 1 0 0 1
# [39] 0 8 1 2 3 3 5 0 1 3 2 1 7 0 0 4 5 3 7 4 0 0 4 1 5 7 0 3 2 0 2 3 0 1 7 4 0 0
# [77] 2 5 0 6
# 
# Clustering (N denotes noise or one-point components):
#  [1] "N" "1" "N" "2" "2" "8" "6" "N" "7" "N" "2" "N" "N" "4" "1" "6" "6" "8" "4"
# [20] "N" "N" "N" "4" "1" "4" "N" "6" "5" "3" "1" "3" "5" "N" "6" "1" "N" "N" "1"
# [39] "N" "8" "1" "2" "3" "3" "5" "N" "1" "3" "2" "1" "7" "N" "N" "4" "5" "3" "7"
# [58] "4" "N" "N" "4" "1" "5" "7" "N" "3" "2" "N" "2" "3" "N" "1" "7" "4" "N" "N"
# [77] "2" "5" "N" "6"
# > 
# > # Here is an example for species delimitation with codominant markers;
# > # only 50 individuals were used in order to have a fast example. 
# > data(tetragonula)
# > ta <- alleleconvert(strmatrix=tetragonula[1:50,])
# > tai <- alleleinit(allelematrix=ta)
# > print(prabclust(tai))
# * Clustered presence-absence matrix * 
# 
# Clustered:  4 -dim. MDS result from method  classical 
# 
# Noise-detector NNclean has been used with k= 2 
# NNclean is explained in S. Byers and A. E. Raftery, JASA 95 (1998), 781-794
# A Normal mixture model with noise component (mclust) has been used.
# Mixture component memberships:
#  [1] 2 2 1 1 1 1 1 2 2 1 1 1 2 2 2 1 2 1 2 1 2 2 1 2 1 2 2 2 1 1 1 1 2 1 2 3 0 3
# [39] 0 0 0 3 3 3 3 0 3 3 3 3
# 
# Clustering (N denotes noise or one-point components):
#  [1] "2" "2" "1" "1" "1" "1" "1" "2" "2" "1" "1" "1" "2" "2" "2" "1" "2" "1" "2"
# [20] "1" "2" "2" "1" "2" "1" "2" "2" "2" "1" "1" "1" "1" "2" "1" "2" "3" "N" "3"
# [39] "N" "N" "N" "3" "3" "3" "3" "N" "3" "3" "3" "3"
# > 
# > # Here is an example for species delimitation with dominant markers;
# > # only 50 individuals were used in order to have a fast example.
# > # You may want to use stressvals to choose mdsdim.
# > data(veronica)
# > vei <- prabinit(prabmatrix=veronica[1:50,],distance="jaccard")
# > print(prabclust(vei,mdsmethod="kruskal",mdsdim=3))
# initial  value 28.163173 
# iter   5 value 20.897590
# iter  10 value 19.154545
# iter  15 value 18.814679
# iter  20 value 18.493361
# iter  20 value 18.475223
# final  value 18.228921 
# converged
# * Clustered presence-absence matrix * 
# 
# Clustered:  3 -dim. MDS result from method  kruskal 
# 
# Noise-detector NNclean has been used with k= 2 
# NNclean is explained in S. Byers and A. E. Raftery, JASA 95 (1998), 781-794
# A Normal mixture model with noise component (mclust) has been used.
# Mixture component memberships:
#  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1
# [39] 1 0 0 1 1 1 1 1 1 0 1 0
# 
# Clustering (N denotes noise or one-point components):
#  [1] "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1"
# [20] "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "N" "1" "1" "1" "1" "1" "1" "1" "1"
# [39] "1" "N" "N" "1" "1" "1" "1" "1" "1" "N" "1" "N"
# > 
# > # example(crmatrix)
# >   options(digits=3)
# >   data(kykladspecreg)
# >   data(nb)
# >   set.seed(1234)
# >   x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb)
# >   xc <- prabclust(x)
# > 
# >   crmatrix(x,xc)
#       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#  [1,]    0    0    0    1    0    0    0    0    0     0     0     0     0
#  [2,]    0    0    0    0    0    0    0    1    0     1     0     0     0
#  [3,]    0    0    0    0    0    0    0    0    0     1     2     2     2
#  [4,]    1    0    0    0    2    7    3    0    1     2     0     0     1
#  [5,]    0    0    0    0    0    0    0    0    0     0     0     0     0
#  [6,]    0    0    0    0    0    0    0    0    0     0     0     0     0
#  [7,]    5    4    1    0    0    0    0    0    2     0     0     0     0
#  [8,]    0    0    0    0    0    0    0    0    0     0     0     0     0
#  [9,]    9   10    3    3    4    4    4    9    7     9     3     1     4
#       [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
#  [1,]     0     0     0     0     0     0     0     0     0     0     0     0
#  [2,]     2     3     2     2     0     1     6     1     3     6     2     2
#  [3,]     1     1     0     1     0     0     0     0     0     0     2     0
#  [4,]     0     0     0     0     0     1     0     0     0     1     0     0
#  [5,]     0     0     0     0     0     0     0     0     0     0     0     0
#  [6,]     0     0     0     0     0     0     0     0     0     0     0     0
#  [7,]     0     0     0     0     0     0     0     0     0     0     0     0
#  [8,]     0     0     0     0     0     0     0     0     0     0     0     0
#  [9,]     8     7     6     5     6     8    10     3     4     7     6     8
#       [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34]
#  [1,]     0     0     7     8     3     0     0     0     0
#  [2,]     2     0     0     0     0     0     0     0     0
#  [3,]     2     0     0     0     0     0     0     0     0
#  [4,]     0     0     0     0     0     0     0     0     0
#  [5,]     0     6     0     0     0     0     0     0     0
#  [6,]     0     0     0     0     3     4     2     6     0
#  [7,]     0     0     0     0     0     0     0     0     0
#  [8,]     0     3     3     3     0     0     0     0     0
#  [9,]     5    10     6    10     5     6     2     7     4
# >   crmatrix(x,xc, percentages=TRUE)
#        [,1] [,2] [,3]   [,4] [,5]  [,6]  [,7]  [,8]  [,9] [,10] [,11] [,12]
#  [1,] 0.000  0.0 0.00 0.0909 0.00 0.000 0.000 0.000 0.000 0.000  0.00  0.00
#  [2,] 0.000  0.0 0.00 0.0000 0.00 0.000 0.000 0.125 0.000 0.125  0.00  0.00
#  [3,] 0.000  0.0 0.00 0.0000 0.00 0.000 0.000 0.000 0.000 0.125  0.25  0.25
#  [4,] 0.125  0.0 0.00 0.0000 0.25 0.875 0.375 0.000 0.125 0.250  0.00  0.00
#  [5,] 0.000  0.0 0.00 0.0000 0.00 0.000 0.000 0.000 0.000 0.000  0.00  0.00
#  [6,] 0.000  0.0 0.00 0.0000 0.00 0.000 0.000 0.000 0.000 0.000  0.00  0.00
#  [7,] 1.000  0.8 0.20 0.0000 0.00 0.000 0.000 0.000 0.400 0.000  0.00  0.00
#  [8,] 0.000  0.0 0.00 0.0000 0.00 0.000 0.000 0.000 0.000 0.000  0.00  0.00
#  [9,] 0.360  0.4 0.12 0.1200 0.16 0.160 0.160 0.360 0.280 0.360  0.12  0.04
#       [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
#  [1,] 0.000 0.000 0.000  0.00 0.000  0.00 0.000  0.00 0.000 0.000 0.000  0.00
#  [2,] 0.000 0.250 0.375  0.25 0.250  0.00 0.125  0.75 0.125 0.375 0.750  0.25
#  [3,] 0.250 0.125 0.125  0.00 0.125  0.00 0.000  0.00 0.000 0.000 0.000  0.25
#  [4,] 0.125 0.000 0.000  0.00 0.000  0.00 0.125  0.00 0.000 0.000 0.125  0.00
#  [5,] 0.000 0.000 0.000  0.00 0.000  0.00 0.000  0.00 0.000 0.000 0.000  0.00
#  [6,] 0.000 0.000 0.000  0.00 0.000  0.00 0.000  0.00 0.000 0.000 0.000  0.00
#  [7,] 0.000 0.000 0.000  0.00 0.000  0.00 0.000  0.00 0.000 0.000 0.000  0.00
#  [8,] 0.000 0.000 0.000  0.00 0.000  0.00 0.000  0.00 0.000 0.000 0.000  0.00
#  [9,] 0.160 0.320 0.280  0.24 0.200  0.24 0.320  0.40 0.120 0.160 0.280  0.24
#       [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34]
#  [1,]  0.00  0.00   0.0 0.636 0.727 0.273 0.000 0.000  0.00  0.00
#  [2,]  0.25  0.25   0.0 0.000 0.000 0.000 0.000 0.000  0.00  0.00
#  [3,]  0.00  0.25   0.0 0.000 0.000 0.000 0.000 0.000  0.00  0.00
#  [4,]  0.00  0.00   0.0 0.000 0.000 0.000 0.000 0.000  0.00  0.00
#  [5,]  0.00  0.00   1.0 0.000 0.000 0.000 0.000 0.000  0.00  0.00
#  [6,]  0.00  0.00   0.0 0.000 0.000 0.500 0.667 0.333  1.00  0.00
#  [7,]  0.00  0.00   0.0 0.000 0.000 0.000 0.000 0.000  0.00  0.00
#  [8,]  0.00  0.00   1.0 1.000 1.000 0.000 0.000 0.000  0.00  0.00
#  [9,]  0.32  0.20   0.4 0.240 0.400 0.200 0.240 0.080  0.28  0.16
# > 
# > 
# > # example(lociplots)
# >   options(digits=4)
# >   data(veronica)
# >   vei <- prabinit(prabmatrix=veronica[1:50,],distance="jaccard")
# >   ppv <- prabclust(vei)
# >   veloci <- prabinit(prabmatrix=veronica[1:50,],rows.are.species=FALSE)
# >   velociclust <- prabclust(veloci,nnk=0)
# >   lociplots(ppv,velociclust$clustering,veloci,lcluster=3)
# $locfreq
#  [1] 0.4737 0.3684 0.4737 0.5263 0.3684 0.4211 0.3684 0.5263 0.5263 0.5263
# [11] 0.6842 0.4211 0.4211 0.5263 0.4211 0.2632 0.4211 0.4737 0.5263 0.4211
# [21] 0.4737 0.4211 0.5263 0.4211 0.6316 0.5263 0.4211 0.4737 0.4211 0.3684
# [31] 0.5263 0.4737 0.4737 0.4211 0.4737 0.5789 0.5263 0.5263 0.4211 0.3684
# [41] 0.2632 0.4211 0.3684 0.4737 0.5263 0.4211 0.4211 0.2632 0.5263 0.5263
# 
# $locfreqmin
# [1] 0.2632 0.3684 0.2632 0.4211
# 
# $locfreqmax
# [1] 0.5263 0.5263 0.5789 0.6842
# 
# $locfreqmean
# [1] 0.3947 0.4520 0.4575 0.5044
# 
# > 
# > proc.time()
#    user  system elapsed 
#   1.708   0.020   1.729