abund: Sort variables by abundance
In pastecs: Package for Analysis of Space-Time Ecological Series
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
Sort variables (usually species in a species x stations matrix) in function of
their abundance, either in number of non-null values, or in number of
individuals (in log). The f coefficient allows adjusting weight given to each of these two criteria.
Usage
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
abund(x, f = 0.2)
## S3 method for class 'abund'
extract(e, n, left = TRUE, ...)
## S3 method for class 'abund'
identify(x, label.pts = FALSE, lvert = TRUE, lvars = TRUE, col = 2, lty = 2, ...)
## S3 method for class 'abund'
lines(x, n = x$n, lvert = TRUE, lvars = TRUE, col = 2, lty = 2, ...)
## S3 method for class 'abund'
plot(x, n = x$n, lvert = TRUE, lvars = TRUE, lcol = 2, llty = 2, all = TRUE,
dlab = c("cumsum", "% log(ind.)", "% non-zero"), dcol = c(1,2,4),
dlty = c(par("lty"), par("lty"), par("lty")), dpos = c(1.5, 20), type = "l",
xlab = "variables", ylab = "abundance",
main = paste("Abundance sorting for:",x$data, "with f =", round(x$f, 4)), ...)
## S3 method for class 'abund'
print(x, ...)
## S3 method for class 'summary.abund'
print(x, ...)
## S3 method for class 'abund'
summary(object, ...)
Arguments
x
A data frame containing the variables to sort according to their
abundance in columns for abund, or an 'abund' object for the methods
f
Weight given to the number of individuals criterium (strictly
included between 0 and 1; weight for the non-null values is 1-f. The
default value, f=0.2, gives enough weight to the number of non-null
values to get abundant species according to this criterium first, but
allowing to get at the other extreme rare, but locally abundant species
object
An 'abund' object returned by abund
e
An 'abund' object returned by abund
n
The number of variables selected at left
type
the type of graph to plot. By default, lines with 'l'
lvert
If TRUE then a vertical line separate the n variables at
left from the others
lvars
If TRUE then the x-axis labels of the n left variables
are printed in a different color to emphasize them
lcol
The color to use to draw the vertical line (lvert=TRUE)
and the variables labels (lvars=TRUE) at left af the nth variable.
By default, color 2 is used
llty
The style used to draw the vertical line (lvert=TRUE).
By default, a dashed line is used
xlab
the label of the x-axis
ylab
the label of the y-axis
main
the main title of the graph
all
If TRUE then all lines are drawn (cumsum, %log(ind.) and
%non-null). If FALSE, only the cumsum line is drawn
dlab
The legend labels
dcol
Colors to use for drawing the various curves on the graph
dlty
The line style to use for drawing the various curves on the graph
dpos
The position of the legend box on the graph (coordinates of its
top-left corner). A legend box is drawn only if all=TRUE
col
The color to use to draw lines
lty
The style used to draw lines
...
additional parameters
label.pts
Do we have to label points on the graph or to chose an
extraction level with the identify() method?
left
If TRUE, the n variables at left are extracted. Otherwise,
the total-n variables at right are extracted
Details
Successive sorts can be applied. For instance, a first sort with
f = 0.2, followed by an extraction of rare species and another sort
with f = 1 allows to collect only rare but locally abundant species.
Value
An object of type 'abund' is returned. It has methods print(),
summary(), plot(), lines(), identify(), extract().
Author(s)
Philippe Grosjean (phgrosjean@sciviews.org),
Frédéric Ibanez (ibanez@obs-vlfr.fr)
References
Ibanez, F., J.-C. Dauvin & M. Etienne, 1993. Comparaison des évolutions
à long terme (1977-1990) de deux peuplements macrobenthiques de la baie de
Morlaix (Manche occidentale): relations avec les facteurs hydroclimatiques.
J. Exp. Mar. Biol. Ecol., 169:181-214.
See Also
Examples
1
2
3
4
5
6
7
8
9
Example output
Sorting of descriptors according to abundance for: bnr
Coefficient f: 0.2
163 variables sorted
Number of individuals (% of most abundant in log):
S8 S2 S3 S4 S6 S13 S1
72.273641 90.069317 88.739428 78.019800 76.302796 68.235798 100.000000
S5 S10 S14 S9 S15 S21 S17
77.095883 70.713663 67.746617 71.405852 65.019607 56.765809 61.013010
S22 S39 S12 S26 S25 S11 S38
56.217342 42.731127 68.629878 53.892117 55.223940 68.848694 43.220902
S19 S20 S29 S37 S41 S27 S16
60.065505 56.854955 50.145792 43.416565 42.561076 51.790497 62.798247
S45 S23 S43 S33 S36 S24 S47
41.106427 55.899523 42.254638 47.328915 44.929107 55.322688 39.646573
S49 S52 S31 S32 S46 S67 S58
38.667117 36.354344 49.292179 48.648509 39.888968 27.616484 32.237324
S50 S71 S54 S35 S61 S18 S34
36.889898 25.804639 33.768615 45.427018 31.764202 60.458819 47.328915
S68 S7 S69 S64 S72 S62 S48
27.056287 74.463292 26.760995 28.868131 25.459071 30.908713 39.396718
S70 S59 S78 S51 S42 S80 S53
26.454557 32.082659 23.004660 36.803057 42.431159 20.176537 35.976618
S73 S88 S100 S84 S77 S55 S56
25.459071 17.840663 14.548434 19.470036 23.468766 32.538086 32.538086
S30 S112 S135 S136 S137 S138 S139
49.694585 11.256205 0.000000 0.000000 0.000000 0.000000 0.000000
S140 S141 S142 S143 S144 S145 S146
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
S147 S148 S149 S150 S151 S152 S153
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
S154 S155 S156 S157 S158 S159 S160
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
S161 S162 S163 S92 S74 S114 S90
0.000000 0.000000 0.000000 15.800081 24.324255 8.920332 16.884308
S91 S107 S119 S120 S121 S125 S127
16.884308 13.068049 5.628103 5.628103 5.628103 5.628103 5.628103
S128 S131 S132 S133 S134 S93 S96
5.628103 5.628103 5.628103 5.628103 5.628103 14.548434 14.548434
S98 S85 S83 S113 S115 S117 S102
14.548434 18.696152 19.470036 8.920332 8.920332 8.920332 13.068049
S103 S108 S122 S123 S124 S126 S129
13.068049 13.068049 5.628103 5.628103 5.628103 5.628103 5.628103
S130 S89 S94 S95 S97 S101 S110
5.628103 17.840663 14.548434 14.548434 14.548434 14.548434 11.256205
S111 S81 S116 S118 S104 S105 S106
11.256205 20.176537 8.920332 8.920332 13.068049 13.068049 13.068049
S63 S99 S87 S109 S60 S75 S82
29.096868 14.548434 18.696152 11.256205 31.764202 24.324255 19.470036
S76 S79 S86 S57 S65 S66 S44
23.468766 20.826454 18.696152 32.538086 28.632763 28.390368 41.700812
S28 S40
50.964058 42.688947
Percent of non-zero values:
S8 S2 S3 S4 S6 S13 S1
95.1456311 95.1456311 93.2038835 89.3203883 86.4077670 82.5242718 89.3203883
S5 S10 S14 S9 S15 S21 S17
83.4951456 81.5533981 79.6116505 78.6407767 76.6990291 70.8737864 70.8737864
S22 S39 S12 S26 S25 S11 S38
68.9320388 64.0776699 67.9611650 64.0776699 64.0776699 66.0194175 59.2233010
S19 S20 S29 S37 S41 S27 S16
63.1067961 58.2524272 54.3689320 51.4563107 49.5145631 50.4854369 51.4563107
S45 S23 S43 S33 S36 S24 S47
43.6893204 44.6601942 39.8058252 38.8349515 35.9223301 37.8640777 33.0097087
S49 S52 S31 S32 S46 S67 S58
30.0970874 28.1553398 31.0679612 30.0970874 23.3009709 19.4174757 20.3883495
S50 S71 S54 S35 S61 S18 S34
21.3592233 18.4466019 20.3883495 23.3009709 19.4174757 26.2135922 22.3300971
S68 S7 S69 S64 S72 S62 S48
16.5048544 28.1553398 15.5339806 15.5339806 14.5631068 15.5339806 16.5048544
S70 S59 S78 S51 S42 S80 S53
12.6213592 12.6213592 9.7087379 12.6213592 13.5922330 7.7669903 11.6504854
S73 S88 S100 S84 S77 S55 S56
8.7378641 6.7961165 5.8252427 6.7961165 7.7669903 9.7087379 9.7087379
S30 S112 S135 S136 S137 S138 S139
13.5922330 3.8834951 0.9708738 0.9708738 0.9708738 0.9708738 0.9708738
S140 S141 S142 S143 S144 S145 S146
0.9708738 0.9708738 0.9708738 0.9708738 0.9708738 0.9708738 0.9708738
S147 S148 S149 S150 S151 S152 S153
0.9708738 0.9708738 0.9708738 0.9708738 0.9708738 0.9708738 0.9708738
S154 S155 S156 S157 S158 S159 S160
0.9708738 0.9708738 0.9708738 0.9708738 0.9708738 0.9708738 0.9708738
S161 S162 S163 S92 S74 S114 S90
0.9708738 0.9708738 0.9708738 4.8543689 6.7961165 2.9126214 4.8543689
S91 S107 S119 S120 S121 S125 S127
4.8543689 3.8834951 1.9417476 1.9417476 1.9417476 1.9417476 1.9417476
S128 S131 S132 S133 S134 S93 S96
1.9417476 1.9417476 1.9417476 1.9417476 1.9417476 3.8834951 3.8834951
S98 S85 S83 S113 S115 S117 S102
3.8834951 4.8543689 4.8543689 1.9417476 1.9417476 1.9417476 2.9126214
S103 S108 S122 S123 S124 S126 S129
2.9126214 2.9126214 0.9708738 0.9708738 0.9708738 0.9708738 0.9708738
S130 S89 S94 S95 S97 S101 S110
0.9708738 3.8834951 2.9126214 2.9126214 2.9126214 2.9126214 1.9417476
S111 S81 S116 S118 S104 S105 S106
1.9417476 3.8834951 0.9708738 0.9708738 1.9417476 1.9417476 1.9417476
S63 S99 S87 S109 S60 S75 S82
5.8252427 1.9417476 2.9126214 0.9708738 5.8252427 3.8834951 1.9417476
S76 S79 S86 S57 S65 S66 S44
2.9126214 1.9417476 0.9708738 3.8834951 1.9417476 0.9708738 3.8834951
S28 S40
4.8543689 0.9708738
[1] "S8" "S2" "S3" "S4" "S6" "S13" "S1" "S5" "S10" "S14"
[11] "S9" "S15" "S21" "S17" "S22" "S39" "S12" "S26" "S25" "S11"
[21] "S38" "S19" "S20" "S29" "S37" "S41" "S27" "S16" "S45" "S23"
[31] "S43" "S33" "S36" "S24" "S47" "S49" "S52" "S31" "S32" "S46"
[41] "S67" "S58" "S50" "S71" "S54" "S35" "S61" "S18" "S34" "S68"
[51] "S7" "S69" "S64" "S72" "S62" "S48" "S70" "S59" "S78" "S51"
[61] "S42" "S80" "S53" "S73" "S88" "S100" "S84" "S77" "S55" "S56"
[71] "S30" "S112" "S135" "S136" "S137" "S138" "S139" "S140" "S141" "S142"
[81] "S143" "S144" "S145" "S146" "S147" "S148" "S149" "S150" "S151" "S152"
[91] "S153" "S154" "S155" "S156" "S157" "S158" "S159" "S160" "S161" "S162"
[101] "S163" "S92" "S74" "S114" "S90" "S91" "S107" "S119" "S120" "S121"
[111] "S125" "S127" "S128" "S131" "S132" "S133" "S134" "S93" "S96" "S98"
[121] "S85" "S83" "S113" "S115" "S117" "S102" "S103" "S108" "S122" "S123"
[131] "S124" "S126" "S129" "S130" "S89" "S94" "S95" "S97" "S101" "S110"
[141] "S111" "S81" "S116" "S118" "S104" "S105" "S106" "S63" "S99" "S87"
[151] "S109" "S60" "S75" "S82" "S76" "S79" "S86" "S57" "S65" "S66"
[161] "S44" "S28" "S40"
pastecs documentation built on May 2, 2019, 3:36 p.m.
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
Sort variables (usually species in a species x stations matrix) in function of
their abundance, either in number of non-null values, or in number of
individuals (in log). The f coefficient allows adjusting weight given to each of these two criteria.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | abund(x, f = 0.2)
## S3 method for class 'abund'
extract(e, n, left = TRUE, ...)
## S3 method for class 'abund'
identify(x, label.pts = FALSE, lvert = TRUE, lvars = TRUE, col = 2, lty = 2, ...)
## S3 method for class 'abund'
lines(x, n = x$n, lvert = TRUE, lvars = TRUE, col = 2, lty = 2, ...)
## S3 method for class 'abund'
plot(x, n = x$n, lvert = TRUE, lvars = TRUE, lcol = 2, llty = 2, all = TRUE,
dlab = c("cumsum", "% log(ind.)", "% non-zero"), dcol = c(1,2,4),
dlty = c(par("lty"), par("lty"), par("lty")), dpos = c(1.5, 20), type = "l",
xlab = "variables", ylab = "abundance",
main = paste("Abundance sorting for:",x$data, "with f =", round(x$f, 4)), ...)
## S3 method for class 'abund'
print(x, ...)
## S3 method for class 'summary.abund'
print(x, ...)
## S3 method for class 'abund'
summary(object, ...)
|
Arguments
x |
A data frame containing the variables to sort according to their
abundance in columns for |
f |
Weight given to the number of individuals criterium (strictly
included between 0 and 1; weight for the non-null values is |
object |
An 'abund' object returned by |
e |
An 'abund' object returned by |
n |
The number of variables selected at left |
type |
the type of graph to plot. By default, lines with 'l' |
lvert |
If |
lvars |
If |
lcol |
The color to use to draw the vertical line ( |
llty |
The style used to draw the vertical line ( |
xlab |
the label of the x-axis |
ylab |
the label of the y-axis |
main |
the main title of the graph |
all |
If |
dlab |
The legend labels |
dcol |
Colors to use for drawing the various curves on the graph |
dlty |
The line style to use for drawing the various curves on the graph |
dpos |
The position of the legend box on the graph (coordinates of its
top-left corner). A legend box is drawn only if |
col |
The color to use to draw lines |
lty |
The style used to draw lines |
... |
additional parameters |
label.pts |
Do we have to label points on the graph or to chose an
extraction level with the |
left |
If |
Details
Successive sorts can be applied. For instance, a first sort with
f = 0.2, followed by an extraction of rare species and another sort
with f = 1 allows to collect only rare but locally abundant species.
Value
An object of type 'abund' is returned. It has methods print(),
summary(), plot(), lines(), identify(), extract().
Author(s)
Philippe Grosjean (phgrosjean@sciviews.org), Frédéric Ibanez (ibanez@obs-vlfr.fr)
References
Ibanez, F., J.-C. Dauvin & M. Etienne, 1993. Comparaison des évolutions à long terme (1977-1990) de deux peuplements macrobenthiques de la baie de Morlaix (Manche occidentale): relations avec les facteurs hydroclimatiques. J. Exp. Mar. Biol. Ecol., 169:181-214.
See Also
Examples
1 2 3 4 5 6 7 8 9 |
Example output
Sorting of descriptors according to abundance for: bnr
Coefficient f: 0.2
163 variables sorted
Number of individuals (% of most abundant in log):
S8 S2 S3 S4 S6 S13 S1
72.273641 90.069317 88.739428 78.019800 76.302796 68.235798 100.000000
S5 S10 S14 S9 S15 S21 S17
77.095883 70.713663 67.746617 71.405852 65.019607 56.765809 61.013010
S22 S39 S12 S26 S25 S11 S38
56.217342 42.731127 68.629878 53.892117 55.223940 68.848694 43.220902
S19 S20 S29 S37 S41 S27 S16
60.065505 56.854955 50.145792 43.416565 42.561076 51.790497 62.798247
S45 S23 S43 S33 S36 S24 S47
41.106427 55.899523 42.254638 47.328915 44.929107 55.322688 39.646573
S49 S52 S31 S32 S46 S67 S58
38.667117 36.354344 49.292179 48.648509 39.888968 27.616484 32.237324
S50 S71 S54 S35 S61 S18 S34
36.889898 25.804639 33.768615 45.427018 31.764202 60.458819 47.328915
S68 S7 S69 S64 S72 S62 S48
27.056287 74.463292 26.760995 28.868131 25.459071 30.908713 39.396718
S70 S59 S78 S51 S42 S80 S53
26.454557 32.082659 23.004660 36.803057 42.431159 20.176537 35.976618
S73 S88 S100 S84 S77 S55 S56
25.459071 17.840663 14.548434 19.470036 23.468766 32.538086 32.538086
S30 S112 S135 S136 S137 S138 S139
49.694585 11.256205 0.000000 0.000000 0.000000 0.000000 0.000000
S140 S141 S142 S143 S144 S145 S146
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
S147 S148 S149 S150 S151 S152 S153
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
S154 S155 S156 S157 S158 S159 S160
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
S161 S162 S163 S92 S74 S114 S90
0.000000 0.000000 0.000000 15.800081 24.324255 8.920332 16.884308
S91 S107 S119 S120 S121 S125 S127
16.884308 13.068049 5.628103 5.628103 5.628103 5.628103 5.628103
S128 S131 S132 S133 S134 S93 S96
5.628103 5.628103 5.628103 5.628103 5.628103 14.548434 14.548434
S98 S85 S83 S113 S115 S117 S102
14.548434 18.696152 19.470036 8.920332 8.920332 8.920332 13.068049
S103 S108 S122 S123 S124 S126 S129
13.068049 13.068049 5.628103 5.628103 5.628103 5.628103 5.628103
S130 S89 S94 S95 S97 S101 S110
5.628103 17.840663 14.548434 14.548434 14.548434 14.548434 11.256205
S111 S81 S116 S118 S104 S105 S106
11.256205 20.176537 8.920332 8.920332 13.068049 13.068049 13.068049
S63 S99 S87 S109 S60 S75 S82
29.096868 14.548434 18.696152 11.256205 31.764202 24.324255 19.470036
S76 S79 S86 S57 S65 S66 S44
23.468766 20.826454 18.696152 32.538086 28.632763 28.390368 41.700812
S28 S40
50.964058 42.688947
Percent of non-zero values:
S8 S2 S3 S4 S6 S13 S1
95.1456311 95.1456311 93.2038835 89.3203883 86.4077670 82.5242718 89.3203883
S5 S10 S14 S9 S15 S21 S17
83.4951456 81.5533981 79.6116505 78.6407767 76.6990291 70.8737864 70.8737864
S22 S39 S12 S26 S25 S11 S38
68.9320388 64.0776699 67.9611650 64.0776699 64.0776699 66.0194175 59.2233010
S19 S20 S29 S37 S41 S27 S16
63.1067961 58.2524272 54.3689320 51.4563107 49.5145631 50.4854369 51.4563107
S45 S23 S43 S33 S36 S24 S47
43.6893204 44.6601942 39.8058252 38.8349515 35.9223301 37.8640777 33.0097087
S49 S52 S31 S32 S46 S67 S58
30.0970874 28.1553398 31.0679612 30.0970874 23.3009709 19.4174757 20.3883495
S50 S71 S54 S35 S61 S18 S34
21.3592233 18.4466019 20.3883495 23.3009709 19.4174757 26.2135922 22.3300971
S68 S7 S69 S64 S72 S62 S48
16.5048544 28.1553398 15.5339806 15.5339806 14.5631068 15.5339806 16.5048544
S70 S59 S78 S51 S42 S80 S53
12.6213592 12.6213592 9.7087379 12.6213592 13.5922330 7.7669903 11.6504854
S73 S88 S100 S84 S77 S55 S56
8.7378641 6.7961165 5.8252427 6.7961165 7.7669903 9.7087379 9.7087379
S30 S112 S135 S136 S137 S138 S139
13.5922330 3.8834951 0.9708738 0.9708738 0.9708738 0.9708738 0.9708738
S140 S141 S142 S143 S144 S145 S146
0.9708738 0.9708738 0.9708738 0.9708738 0.9708738 0.9708738 0.9708738
S147 S148 S149 S150 S151 S152 S153
0.9708738 0.9708738 0.9708738 0.9708738 0.9708738 0.9708738 0.9708738
S154 S155 S156 S157 S158 S159 S160
0.9708738 0.9708738 0.9708738 0.9708738 0.9708738 0.9708738 0.9708738
S161 S162 S163 S92 S74 S114 S90
0.9708738 0.9708738 0.9708738 4.8543689 6.7961165 2.9126214 4.8543689
S91 S107 S119 S120 S121 S125 S127
4.8543689 3.8834951 1.9417476 1.9417476 1.9417476 1.9417476 1.9417476
S128 S131 S132 S133 S134 S93 S96
1.9417476 1.9417476 1.9417476 1.9417476 1.9417476 3.8834951 3.8834951
S98 S85 S83 S113 S115 S117 S102
3.8834951 4.8543689 4.8543689 1.9417476 1.9417476 1.9417476 2.9126214
S103 S108 S122 S123 S124 S126 S129
2.9126214 2.9126214 0.9708738 0.9708738 0.9708738 0.9708738 0.9708738
S130 S89 S94 S95 S97 S101 S110
0.9708738 3.8834951 2.9126214 2.9126214 2.9126214 2.9126214 1.9417476
S111 S81 S116 S118 S104 S105 S106
1.9417476 3.8834951 0.9708738 0.9708738 1.9417476 1.9417476 1.9417476
S63 S99 S87 S109 S60 S75 S82
5.8252427 1.9417476 2.9126214 0.9708738 5.8252427 3.8834951 1.9417476
S76 S79 S86 S57 S65 S66 S44
2.9126214 1.9417476 0.9708738 3.8834951 1.9417476 0.9708738 3.8834951
S28 S40
4.8543689 0.9708738
[1] "S8" "S2" "S3" "S4" "S6" "S13" "S1" "S5" "S10" "S14"
[11] "S9" "S15" "S21" "S17" "S22" "S39" "S12" "S26" "S25" "S11"
[21] "S38" "S19" "S20" "S29" "S37" "S41" "S27" "S16" "S45" "S23"
[31] "S43" "S33" "S36" "S24" "S47" "S49" "S52" "S31" "S32" "S46"
[41] "S67" "S58" "S50" "S71" "S54" "S35" "S61" "S18" "S34" "S68"
[51] "S7" "S69" "S64" "S72" "S62" "S48" "S70" "S59" "S78" "S51"
[61] "S42" "S80" "S53" "S73" "S88" "S100" "S84" "S77" "S55" "S56"
[71] "S30" "S112" "S135" "S136" "S137" "S138" "S139" "S140" "S141" "S142"
[81] "S143" "S144" "S145" "S146" "S147" "S148" "S149" "S150" "S151" "S152"
[91] "S153" "S154" "S155" "S156" "S157" "S158" "S159" "S160" "S161" "S162"
[101] "S163" "S92" "S74" "S114" "S90" "S91" "S107" "S119" "S120" "S121"
[111] "S125" "S127" "S128" "S131" "S132" "S133" "S134" "S93" "S96" "S98"
[121] "S85" "S83" "S113" "S115" "S117" "S102" "S103" "S108" "S122" "S123"
[131] "S124" "S126" "S129" "S130" "S89" "S94" "S95" "S97" "S101" "S110"
[141] "S111" "S81" "S116" "S118" "S104" "S105" "S106" "S63" "S99" "S87"
[151] "S109" "S60" "S75" "S82" "S76" "S79" "S86" "S57" "S65" "S66"
[161] "S44" "S28" "S40"
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