dissim: Calculate Dissimilarity Matrix Data
In RAM: R for Amplicon-Sequencing-Based Microbial-Ecology
Description
Usage
Arguments
Value
Author(s)
See Also
Examples
Description
These functions calculate different measures related to
dissimilarity matrices. All of these functions allow you to
specify one of many dissimilarity indices to be used.
Usage
1
2
3
4
5
6
7
8
9
10
11
12
dissim.clust(elem, is.OTU=TRUE, stand.method=NULL,
dist.method="morisita", clust.method="average")
dissim.eig(elem, is.OTU=TRUE, stand.method=NULL,
dist.method="morisita")
dissim.ord(elem, is.OTU=TRUE, stand.method=NULL,
dist.method="morisita", k=NULL)
dissim.GOF(elem, is.OTU=TRUE, stand.method=NULL,
dist.method="morisita")
dissim.tree(elem, is.OTU=TRUE, stand.method=NULL,
dist.method="morisita", clust.method="average")
dissim.pvar(elem, is.OTU=TRUE, stand.method=NULL,
dist.method="morisita")
Arguments
elem
an ecology data set that can be an OTU table or a taxonomy
abundance table. See RAM.input.formatting for
details.
is.OTU
logical, whether the ecology data sets are OTU tables or
taxonomy abundance matrices.
See RAM.input.formatting for details.
stand.method
optional, if is.null, the standardization method for data
transforamtion; must be one of the following: "total", "max",
"frequency", "normalize", "range", "standardize", "pa",
"chi.square", "hellinger", "log".
See also decostand.
dist.method
the dissimilarity index to be used; one of "manhattan",
"euclidean", "canberra", "bray",
"kulczynski", "jaccard", "gower",
"altGower","morisita", "horn",
"mountford", "raup", "binomial",
"chao", or "cao".
See also vegdist.
k
the number of dimensions desired. If NULL, the maximum
value will be calculated and used.
clust.method
the method used for clustering the data. Must be one of
"ward", "single", "complete",
"average", "mcquitty", "median",
or "centroid". See also hclust.
Value
dissim.clust
returns a hierarchical clustering
of the dissimilarity matrix.
dist.eigenval
returns the eigenvalues of the
dissimilarity matrix.
dissim.ord
returns a list: the first item is the
the ordination distances, the second is the dissimilarity matrix
distances.
dissim.GOF
returns the goodness of fit values
of the dissimilarity matrix, for various numbers of dimensions
used.
dissim.tree
returns a list: the first item is
the tree distances, the second is the dissimilarity matrix
distances.
dissim.pvar
returns a numeric vector containing
the percent variation explained by each axis (where each sample
corresponds to an axis).
Author(s)
Wen Chen and Joshua Simpson
See Also
decostand, vegdist,
hclust, dissim.plot
Examples
1
2
3
4
5
6
7
8
9
data(ITS1)
# calculate clustering, using default method
dissim.clust(ITS1)
# calculate tree distances, specifying a distance method
# (but use default clustering method)
dissim.tree(ITS1, dist.method="euclidean")
# calcualte ordination distances, specifying both distance
# and ordination methods
dissim.ord(ITS1, dist.method="bray", k=3)
Example output
Loading required package: vegan
Loading required package: permute
Loading required package: lattice
This is vegan 2.4-4
Loading required package: ggplot2
sh: 1: cannot create /dev/null: Permission denied
sh: 1: cannot create /dev/null: Permission denied
Call:
hclust(d = dist, method = clust.method)
Cluster method : average
Distance : morisita
Number of objects: 16
[[1]]
P1001.1M1 P1001.1M2 P1001.1M3 P1001.1M4 P1001.1M5 P1001.1M6
P1001.1M2 1851.811
P1001.1M3 2606.052 2606.052
P1001.1M4 3963.624 3963.624 3963.624
P1001.1M5 5013.779 5013.779 5013.779 5013.779
P1001.1M6 5013.779 5013.779 5013.779 5013.779 1889.346
P1001.1M7 5013.779 5013.779 5013.779 5013.779 2947.322 2947.322
P1001.1M8 5013.779 5013.779 5013.779 5013.779 2947.322 2947.322
P1001.1M9 6672.613 6672.613 6672.613 6672.613 6672.613 6672.613
P1001.1M10 3963.624 3963.624 3963.624 2505.439 5013.779 5013.779
P1001.1M11 6672.613 6672.613 6672.613 6672.613 6672.613 6672.613
P1001.1M12 6672.613 6672.613 6672.613 6672.613 6672.613 6672.613
P1001.1M13 12449.254 12449.254 12449.254 12449.254 12449.254 12449.254
P1001.1M14 12449.254 12449.254 12449.254 12449.254 12449.254 12449.254
P1001.1M15 22852.222 22852.222 22852.222 22852.222 22852.222 22852.222
P1001.1M16 22852.222 22852.222 22852.222 22852.222 22852.222 22852.222
P1001.1M7 P1001.1M8 P1001.1M9 P1001.1M10 P1001.1M11 P1001.1M12
P1001.1M2
P1001.1M3
P1001.1M4
P1001.1M5
P1001.1M6
P1001.1M7
P1001.1M8 1031.047
P1001.1M9 6672.613 6672.613
P1001.1M10 5013.779 5013.779 6672.613
P1001.1M11 6672.613 6672.613 4531.154 6672.613
P1001.1M12 6672.613 6672.613 4531.154 6672.613 3851.231
P1001.1M13 12449.254 12449.254 12449.254 12449.254 12449.254 12449.254
P1001.1M14 12449.254 12449.254 12449.254 12449.254 12449.254 12449.254
P1001.1M15 22852.222 22852.222 22852.222 22852.222 22852.222 22852.222
P1001.1M16 22852.222 22852.222 22852.222 22852.222 22852.222 22852.222
P1001.1M13 P1001.1M14 P1001.1M15
P1001.1M2
P1001.1M3
P1001.1M4
P1001.1M5
P1001.1M6
P1001.1M7
P1001.1M8
P1001.1M9
P1001.1M10
P1001.1M11
P1001.1M12
P1001.1M13
P1001.1M14 5510.646
P1001.1M15 22852.222 22852.222
P1001.1M16 22852.222 22852.222 2745.462
[[2]]
P1001.1M1 P1001.1M2 P1001.1M3 P1001.1M4 P1001.1M5 P1001.1M6
P1001.1M2 1851.811
P1001.1M3 1866.650 3345.453
P1001.1M4 3852.518 2193.254 5444.446
P1001.1M5 4768.865 4149.584 5769.415 4376.190
P1001.1M6 4123.574 2932.336 5512.916 2550.375 1889.346
P1001.1M7 5476.010 5129.181 6154.981 5738.036 2305.496 3539.500
P1001.1M8 5938.635 5557.648 6545.136 6063.782 2214.540 3729.752
P1001.1M9 5006.112 4857.036 5978.224 5159.390 5971.004 5241.537
P1001.1M10 3963.442 2896.047 5432.036 2505.439 4528.567 3111.638
P1001.1M11 6998.057 6797.269 7878.146 6764.666 7556.292 6941.974
P1001.1M12 6918.674 6775.531 7946.825 6691.987 7452.719 6782.516
P1001.1M13 15764.564 15515.023 16286.703 15386.464 15533.339 15337.395
P1001.1M14 11044.531 10681.834 11806.624 10469.793 10807.979 10440.200
P1001.1M15 26410.310 26218.344 26716.357 26121.331 26194.577 26110.720
P1001.1M16 24259.200 24076.561 24596.765 23988.474 24019.932 23939.297
P1001.1M7 P1001.1M8 P1001.1M9 P1001.1M10 P1001.1M11 P1001.1M12
P1001.1M2
P1001.1M3
P1001.1M4
P1001.1M5
P1001.1M6
P1001.1M7
P1001.1M8 1031.047
P1001.1M9 6842.739 7233.432
P1001.1M10 5722.094 6126.613 2908.944
P1001.1M11 8518.836 8714.305 4869.734 5676.274
P1001.1M12 8626.381 8883.616 4192.574 5038.053 3851.231
P1001.1M13 16125.383 16082.759 14352.252 14808.014 10136.219 12626.458
P1001.1M14 11628.057 11665.615 9360.918 9656.363 5655.493 7610.122
P1001.1M15 26561.933 26449.341 25440.410 25859.991 21111.903 23877.201
P1001.1M16 24423.237 24322.010 23142.679 23610.164 18839.905 21447.304
P1001.1M13 P1001.1M14 P1001.1M15
P1001.1M2
P1001.1M3
P1001.1M4
P1001.1M5
P1001.1M6
P1001.1M7
P1001.1M8
P1001.1M9
P1001.1M10
P1001.1M11
P1001.1M12
P1001.1M13
P1001.1M14 5510.646
P1001.1M15 11544.959 16977.518
P1001.1M16 9079.412 14522.388 2745.462
[[1]]
P1001.1M1 P1001.1M2 P1001.1M3 P1001.1M4 P1001.1M5 P1001.1M6
P1001.1M2 0.18401744
P1001.1M3 0.06176749 0.23883533
P1001.1M4 0.67279774 0.49794212 0.72864830
P1001.1M5 0.43931536 0.41932347 0.43072855 0.71220274
P1001.1M6 0.43579850 0.36441237 0.44501566 0.58995955 0.12657835
P1001.1M7 0.47081295 0.46442189 0.45585234 0.77021655 0.06420233 0.18338222
P1001.1M8 0.48436705 0.48392944 0.46710721 0.79058631 0.08036738 0.20234967
P1001.1M9 0.35853525 0.45671782 0.35909607 0.78959530 0.63781624 0.63696832
P1001.1M10 0.30173652 0.30831234 0.33738878 0.57781394 0.57761754 0.53622650
P1001.1M11 0.52259198 0.57733286 0.53121646 0.79677431 0.72916578 0.71376874
P1001.1M12 0.52612882 0.57158421 0.54046443 0.77300869 0.75611051 0.73297211
P1001.1M13 0.80030024 0.81181764 0.80591943 0.89084569 0.80263919 0.78426825
P1001.1M14 0.75903076 0.77298995 0.76620617 0.86261001 0.79104925 0.77050271
P1001.1M15 0.87048901 0.86270832 0.87752808 0.89318591 0.80677065 0.78319917
P1001.1M16 0.85799442 0.85603364 0.86414184 0.90034227 0.80822151 0.78709591
P1001.1M7 P1001.1M8 P1001.1M9 P1001.1M10 P1001.1M11 P1001.1M12
P1001.1M2
P1001.1M3
P1001.1M4
P1001.1M5
P1001.1M6
P1001.1M7
P1001.1M8 0.02436488
P1001.1M9 0.67774507 0.68258591
P1001.1M10 0.62968907 0.64109640 0.21482353
P1001.1M11 0.77584975 0.77930514 0.19218783 0.28393579
P1001.1M12 0.80481001 0.80983830 0.20577594 0.26745986 0.05986338
P1001.1M13 0.84901227 0.84643114 0.54436555 0.57809732 0.38720324 0.41992339
P1001.1M14 0.83885386 0.83746572 0.49295826 0.52746734 0.32984265 0.36054141
P1001.1M15 0.85264587 0.84920217 0.65425489 0.66219651 0.50983452 0.54085540
P1001.1M16 0.85384911 0.85034751 0.63128976 0.64739300 0.48409444 0.51614646
P1001.1M13 P1001.1M14 P1001.1M15
P1001.1M2
P1001.1M3
P1001.1M4
P1001.1M5
P1001.1M6
P1001.1M7
P1001.1M8
P1001.1M9
P1001.1M10
P1001.1M11
P1001.1M12
P1001.1M13
P1001.1M14 0.06055481
P1001.1M15 0.13876395 0.19211785
P1001.1M16 0.10689195 0.16274404 0.03381889
[[2]]
P1001.1M1 P1001.1M2 P1001.1M3 P1001.1M4 P1001.1M5 P1001.1M6
P1001.1M2 0.28716680
P1001.1M3 0.19465973 0.39894422
P1001.1M4 0.74063369 0.62462632 0.78892982
P1001.1M5 0.57360439 0.57899372 0.59235888 0.74331429
P1001.1M6 0.58963799 0.58667238 0.64047778 0.67706750 0.30057585
P1001.1M7 0.57473277 0.58567533 0.57165784 0.77734492 0.22165012 0.38866720
P1001.1M8 0.60025089 0.61159753 0.59410845 0.79877361 0.23967143 0.40178438
P1001.1M9 0.54490985 0.62689131 0.57296207 0.81928292 0.69146441 0.73051792
P1001.1M10 0.57524021 0.57175701 0.63943777 0.70736146 0.65387107 0.66030140
P1001.1M11 0.65844828 0.72782403 0.67292925 0.82381128 0.76152725 0.76460082
P1001.1M12 0.71029225 0.78281208 0.72792884 0.82147520 0.80651823 0.80733536
P1001.1M13 0.82956619 0.85163636 0.85308259 0.91066730 0.83549517 0.83920073
P1001.1M14 0.83720930 0.84331855 0.86347713 0.89429958 0.82724810 0.83015847
P1001.1M15 0.88642648 0.90176953 0.89830128 0.93942585 0.87813694 0.88716109
P1001.1M16 0.87011569 0.88660993 0.88646857 0.92907450 0.85929648 0.86749220
P1001.1M7 P1001.1M8 P1001.1M9 P1001.1M10 P1001.1M11 P1001.1M12
P1001.1M2
P1001.1M3
P1001.1M4
P1001.1M5
P1001.1M6
P1001.1M7
P1001.1M8 0.11964633
P1001.1M9 0.70845312 0.71111343
P1001.1M10 0.72731281 0.74134368 0.38084915
P1001.1M11 0.80989357 0.81244791 0.37141703 0.51730104
P1001.1M12 0.85682741 0.85907011 0.45568818 0.57904613 0.27970677
P1001.1M13 0.86977887 0.87273170 0.60702640 0.68630219 0.48290709 0.55847167
P1001.1M14 0.86587901 0.86891094 0.57111380 0.65864805 0.44412022 0.49873029
P1001.1M15 0.90581068 0.90802458 0.75287601 0.78463173 0.62992930 0.72810521
P1001.1M16 0.88724843 0.89035282 0.70210726 0.76267683 0.59385229 0.66274730
P1001.1M13 P1001.1M14 P1001.1M15
P1001.1M2
P1001.1M3
P1001.1M4
P1001.1M5
P1001.1M6
P1001.1M7
P1001.1M8
P1001.1M9
P1001.1M10
P1001.1M11
P1001.1M12
P1001.1M13
P1001.1M14 0.18857772
P1001.1M15 0.29521994 0.44000865
P1001.1M16 0.22024355 0.36049347 0.09472624
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Description Usage Arguments Value Author(s) See Also Examples
Description
These functions calculate different measures related to dissimilarity matrices. All of these functions allow you to specify one of many dissimilarity indices to be used.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 | dissim.clust(elem, is.OTU=TRUE, stand.method=NULL,
dist.method="morisita", clust.method="average")
dissim.eig(elem, is.OTU=TRUE, stand.method=NULL,
dist.method="morisita")
dissim.ord(elem, is.OTU=TRUE, stand.method=NULL,
dist.method="morisita", k=NULL)
dissim.GOF(elem, is.OTU=TRUE, stand.method=NULL,
dist.method="morisita")
dissim.tree(elem, is.OTU=TRUE, stand.method=NULL,
dist.method="morisita", clust.method="average")
dissim.pvar(elem, is.OTU=TRUE, stand.method=NULL,
dist.method="morisita")
|
Arguments
elem |
an ecology data set that can be an OTU table or a taxonomy
abundance table. See |
is.OTU |
logical, whether the ecology data sets are OTU tables or
taxonomy abundance matrices.
See |
stand.method |
optional, if |
dist.method |
the dissimilarity index to be used; one of " |
k |
the number of dimensions desired. If |
clust.method |
the method used for clustering the data. Must be one of
" |
Value
|
returns a hierarchical clustering of the dissimilarity matrix. |
|
returns the eigenvalues of the dissimilarity matrix. |
|
returns a list: the first item is the the ordination distances, the second is the dissimilarity matrix distances. |
|
returns the goodness of fit values of the dissimilarity matrix, for various numbers of dimensions used. |
|
returns a list: the first item is the tree distances, the second is the dissimilarity matrix distances. |
|
returns a numeric vector containing the percent variation explained by each axis (where each sample corresponds to an axis). |
Author(s)
Wen Chen and Joshua Simpson
See Also
decostand, vegdist,
hclust, dissim.plot
Examples
1 2 3 4 5 6 7 8 9 | data(ITS1)
# calculate clustering, using default method
dissim.clust(ITS1)
# calculate tree distances, specifying a distance method
# (but use default clustering method)
dissim.tree(ITS1, dist.method="euclidean")
# calcualte ordination distances, specifying both distance
# and ordination methods
dissim.ord(ITS1, dist.method="bray", k=3)
|
Example output
Loading required package: vegan
Loading required package: permute
Loading required package: lattice
This is vegan 2.4-4
Loading required package: ggplot2
sh: 1: cannot create /dev/null: Permission denied
sh: 1: cannot create /dev/null: Permission denied
Call:
hclust(d = dist, method = clust.method)
Cluster method : average
Distance : morisita
Number of objects: 16
[[1]]
P1001.1M1 P1001.1M2 P1001.1M3 P1001.1M4 P1001.1M5 P1001.1M6
P1001.1M2 1851.811
P1001.1M3 2606.052 2606.052
P1001.1M4 3963.624 3963.624 3963.624
P1001.1M5 5013.779 5013.779 5013.779 5013.779
P1001.1M6 5013.779 5013.779 5013.779 5013.779 1889.346
P1001.1M7 5013.779 5013.779 5013.779 5013.779 2947.322 2947.322
P1001.1M8 5013.779 5013.779 5013.779 5013.779 2947.322 2947.322
P1001.1M9 6672.613 6672.613 6672.613 6672.613 6672.613 6672.613
P1001.1M10 3963.624 3963.624 3963.624 2505.439 5013.779 5013.779
P1001.1M11 6672.613 6672.613 6672.613 6672.613 6672.613 6672.613
P1001.1M12 6672.613 6672.613 6672.613 6672.613 6672.613 6672.613
P1001.1M13 12449.254 12449.254 12449.254 12449.254 12449.254 12449.254
P1001.1M14 12449.254 12449.254 12449.254 12449.254 12449.254 12449.254
P1001.1M15 22852.222 22852.222 22852.222 22852.222 22852.222 22852.222
P1001.1M16 22852.222 22852.222 22852.222 22852.222 22852.222 22852.222
P1001.1M7 P1001.1M8 P1001.1M9 P1001.1M10 P1001.1M11 P1001.1M12
P1001.1M2
P1001.1M3
P1001.1M4
P1001.1M5
P1001.1M6
P1001.1M7
P1001.1M8 1031.047
P1001.1M9 6672.613 6672.613
P1001.1M10 5013.779 5013.779 6672.613
P1001.1M11 6672.613 6672.613 4531.154 6672.613
P1001.1M12 6672.613 6672.613 4531.154 6672.613 3851.231
P1001.1M13 12449.254 12449.254 12449.254 12449.254 12449.254 12449.254
P1001.1M14 12449.254 12449.254 12449.254 12449.254 12449.254 12449.254
P1001.1M15 22852.222 22852.222 22852.222 22852.222 22852.222 22852.222
P1001.1M16 22852.222 22852.222 22852.222 22852.222 22852.222 22852.222
P1001.1M13 P1001.1M14 P1001.1M15
P1001.1M2
P1001.1M3
P1001.1M4
P1001.1M5
P1001.1M6
P1001.1M7
P1001.1M8
P1001.1M9
P1001.1M10
P1001.1M11
P1001.1M12
P1001.1M13
P1001.1M14 5510.646
P1001.1M15 22852.222 22852.222
P1001.1M16 22852.222 22852.222 2745.462
[[2]]
P1001.1M1 P1001.1M2 P1001.1M3 P1001.1M4 P1001.1M5 P1001.1M6
P1001.1M2 1851.811
P1001.1M3 1866.650 3345.453
P1001.1M4 3852.518 2193.254 5444.446
P1001.1M5 4768.865 4149.584 5769.415 4376.190
P1001.1M6 4123.574 2932.336 5512.916 2550.375 1889.346
P1001.1M7 5476.010 5129.181 6154.981 5738.036 2305.496 3539.500
P1001.1M8 5938.635 5557.648 6545.136 6063.782 2214.540 3729.752
P1001.1M9 5006.112 4857.036 5978.224 5159.390 5971.004 5241.537
P1001.1M10 3963.442 2896.047 5432.036 2505.439 4528.567 3111.638
P1001.1M11 6998.057 6797.269 7878.146 6764.666 7556.292 6941.974
P1001.1M12 6918.674 6775.531 7946.825 6691.987 7452.719 6782.516
P1001.1M13 15764.564 15515.023 16286.703 15386.464 15533.339 15337.395
P1001.1M14 11044.531 10681.834 11806.624 10469.793 10807.979 10440.200
P1001.1M15 26410.310 26218.344 26716.357 26121.331 26194.577 26110.720
P1001.1M16 24259.200 24076.561 24596.765 23988.474 24019.932 23939.297
P1001.1M7 P1001.1M8 P1001.1M9 P1001.1M10 P1001.1M11 P1001.1M12
P1001.1M2
P1001.1M3
P1001.1M4
P1001.1M5
P1001.1M6
P1001.1M7
P1001.1M8 1031.047
P1001.1M9 6842.739 7233.432
P1001.1M10 5722.094 6126.613 2908.944
P1001.1M11 8518.836 8714.305 4869.734 5676.274
P1001.1M12 8626.381 8883.616 4192.574 5038.053 3851.231
P1001.1M13 16125.383 16082.759 14352.252 14808.014 10136.219 12626.458
P1001.1M14 11628.057 11665.615 9360.918 9656.363 5655.493 7610.122
P1001.1M15 26561.933 26449.341 25440.410 25859.991 21111.903 23877.201
P1001.1M16 24423.237 24322.010 23142.679 23610.164 18839.905 21447.304
P1001.1M13 P1001.1M14 P1001.1M15
P1001.1M2
P1001.1M3
P1001.1M4
P1001.1M5
P1001.1M6
P1001.1M7
P1001.1M8
P1001.1M9
P1001.1M10
P1001.1M11
P1001.1M12
P1001.1M13
P1001.1M14 5510.646
P1001.1M15 11544.959 16977.518
P1001.1M16 9079.412 14522.388 2745.462
[[1]]
P1001.1M1 P1001.1M2 P1001.1M3 P1001.1M4 P1001.1M5 P1001.1M6
P1001.1M2 0.18401744
P1001.1M3 0.06176749 0.23883533
P1001.1M4 0.67279774 0.49794212 0.72864830
P1001.1M5 0.43931536 0.41932347 0.43072855 0.71220274
P1001.1M6 0.43579850 0.36441237 0.44501566 0.58995955 0.12657835
P1001.1M7 0.47081295 0.46442189 0.45585234 0.77021655 0.06420233 0.18338222
P1001.1M8 0.48436705 0.48392944 0.46710721 0.79058631 0.08036738 0.20234967
P1001.1M9 0.35853525 0.45671782 0.35909607 0.78959530 0.63781624 0.63696832
P1001.1M10 0.30173652 0.30831234 0.33738878 0.57781394 0.57761754 0.53622650
P1001.1M11 0.52259198 0.57733286 0.53121646 0.79677431 0.72916578 0.71376874
P1001.1M12 0.52612882 0.57158421 0.54046443 0.77300869 0.75611051 0.73297211
P1001.1M13 0.80030024 0.81181764 0.80591943 0.89084569 0.80263919 0.78426825
P1001.1M14 0.75903076 0.77298995 0.76620617 0.86261001 0.79104925 0.77050271
P1001.1M15 0.87048901 0.86270832 0.87752808 0.89318591 0.80677065 0.78319917
P1001.1M16 0.85799442 0.85603364 0.86414184 0.90034227 0.80822151 0.78709591
P1001.1M7 P1001.1M8 P1001.1M9 P1001.1M10 P1001.1M11 P1001.1M12
P1001.1M2
P1001.1M3
P1001.1M4
P1001.1M5
P1001.1M6
P1001.1M7
P1001.1M8 0.02436488
P1001.1M9 0.67774507 0.68258591
P1001.1M10 0.62968907 0.64109640 0.21482353
P1001.1M11 0.77584975 0.77930514 0.19218783 0.28393579
P1001.1M12 0.80481001 0.80983830 0.20577594 0.26745986 0.05986338
P1001.1M13 0.84901227 0.84643114 0.54436555 0.57809732 0.38720324 0.41992339
P1001.1M14 0.83885386 0.83746572 0.49295826 0.52746734 0.32984265 0.36054141
P1001.1M15 0.85264587 0.84920217 0.65425489 0.66219651 0.50983452 0.54085540
P1001.1M16 0.85384911 0.85034751 0.63128976 0.64739300 0.48409444 0.51614646
P1001.1M13 P1001.1M14 P1001.1M15
P1001.1M2
P1001.1M3
P1001.1M4
P1001.1M5
P1001.1M6
P1001.1M7
P1001.1M8
P1001.1M9
P1001.1M10
P1001.1M11
P1001.1M12
P1001.1M13
P1001.1M14 0.06055481
P1001.1M15 0.13876395 0.19211785
P1001.1M16 0.10689195 0.16274404 0.03381889
[[2]]
P1001.1M1 P1001.1M2 P1001.1M3 P1001.1M4 P1001.1M5 P1001.1M6
P1001.1M2 0.28716680
P1001.1M3 0.19465973 0.39894422
P1001.1M4 0.74063369 0.62462632 0.78892982
P1001.1M5 0.57360439 0.57899372 0.59235888 0.74331429
P1001.1M6 0.58963799 0.58667238 0.64047778 0.67706750 0.30057585
P1001.1M7 0.57473277 0.58567533 0.57165784 0.77734492 0.22165012 0.38866720
P1001.1M8 0.60025089 0.61159753 0.59410845 0.79877361 0.23967143 0.40178438
P1001.1M9 0.54490985 0.62689131 0.57296207 0.81928292 0.69146441 0.73051792
P1001.1M10 0.57524021 0.57175701 0.63943777 0.70736146 0.65387107 0.66030140
P1001.1M11 0.65844828 0.72782403 0.67292925 0.82381128 0.76152725 0.76460082
P1001.1M12 0.71029225 0.78281208 0.72792884 0.82147520 0.80651823 0.80733536
P1001.1M13 0.82956619 0.85163636 0.85308259 0.91066730 0.83549517 0.83920073
P1001.1M14 0.83720930 0.84331855 0.86347713 0.89429958 0.82724810 0.83015847
P1001.1M15 0.88642648 0.90176953 0.89830128 0.93942585 0.87813694 0.88716109
P1001.1M16 0.87011569 0.88660993 0.88646857 0.92907450 0.85929648 0.86749220
P1001.1M7 P1001.1M8 P1001.1M9 P1001.1M10 P1001.1M11 P1001.1M12
P1001.1M2
P1001.1M3
P1001.1M4
P1001.1M5
P1001.1M6
P1001.1M7
P1001.1M8 0.11964633
P1001.1M9 0.70845312 0.71111343
P1001.1M10 0.72731281 0.74134368 0.38084915
P1001.1M11 0.80989357 0.81244791 0.37141703 0.51730104
P1001.1M12 0.85682741 0.85907011 0.45568818 0.57904613 0.27970677
P1001.1M13 0.86977887 0.87273170 0.60702640 0.68630219 0.48290709 0.55847167
P1001.1M14 0.86587901 0.86891094 0.57111380 0.65864805 0.44412022 0.49873029
P1001.1M15 0.90581068 0.90802458 0.75287601 0.78463173 0.62992930 0.72810521
P1001.1M16 0.88724843 0.89035282 0.70210726 0.76267683 0.59385229 0.66274730
P1001.1M13 P1001.1M14 P1001.1M15
P1001.1M2
P1001.1M3
P1001.1M4
P1001.1M5
P1001.1M6
P1001.1M7
P1001.1M8
P1001.1M9
P1001.1M10
P1001.1M11
P1001.1M12
P1001.1M13
P1001.1M14 0.18857772
P1001.1M15 0.29521994 0.44000865
P1001.1M16 0.22024355 0.36049347 0.09472624
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