Plots an NMDS of a matrix of 0s and 1s, the censoring indicator columns for multiple variables, to discern the pattern of data below vs. above the detection limit. With multiple detection limits within a column, re-censoring to the highest limit in the column must be done prior to running this function. May have different censoring levels in different columns.
binaryMDS(dat.frame, group = NULL, title = NULL, legend.pos = "bottomleft")Arguments
- dat.frame
A data frame containing only the columns of 0/1 values.
- group
Optional grouping variable. Sites will be represented by different colored symbols for each group.
- title
Optional title for the NMDS graph.
- legend.pos
When group is specified, determines the location of the legend on the graph showing the colors representing each group’s data. Default is “bottomleft”. Alternatives are “topright” and “centerleft”, etc.
Value
Plots an NMDS of censored data represented as the binary Above vs Below a detection limit for each parameter.
Details
Binary data may not provide sufficient information to discern differences in location on the plot if sample size is small. Prior to running this analysis it is suggested to consult best analysis practice when performing NMDS. As a rule of thumb, an NMDS ordination with a stress value around or above 0.2 is deemed suspect and a stress value approaching 0.3 indicates that the ordination is arbitrary. Stress values equal to or below 0.1 are considered fair, while values equal to or below 0.05 indicate good fit.
References
Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.
See also
Examples
# \donttest{
data(PbHeron)
# without group specified
binaryMDS(PbHeron[,4:15])
#> Run 0 stress 9.202781e-05
#> Run 1 stress 1.466907e-05
#> ... New best solution
#> ... Procrustes: rmse 0.1013861 max resid 0.2439043
#> Run 2 stress 8.769836e-05
#> ... Procrustes: rmse 0.03703487 max resid 0.08854904
#> Run 3 stress 0.004165126
#> Run 4 stress 0
#> ... New best solution
#> ... Procrustes: rmse 0.03855667 max resid 0.09147319
#> Run 5 stress 0
#> ... Procrustes: rmse 0.05338103 max resid 0.1381518
#> Run 6 stress 8.19578e-05
#> ... Procrustes: rmse 0.1518665 max resid 0.3255942
#> Run 7 stress 0.001640053
#> Run 8 stress 0
#> ... Procrustes: rmse 0.05479433 max resid 0.1269638
#> Run 9 stress 0.0003911527
#> ... Procrustes: rmse 0.1332422 max resid 0.3250677
#> Run 10 stress 0.05797334
#> Run 11 stress 0
#> ... Procrustes: rmse 0.1327785 max resid 0.3018827
#> Run 12 stress 8.820044e-05
#> ... Procrustes: rmse 0.0220441 max resid 0.05008114
#> Run 13 stress 9.84104e-05
#> ... Procrustes: rmse 0.1357786 max resid 0.2831029
#> Run 14 stress 0.00268514
#> Run 15 stress 0.0486347
#> Run 16 stress 9.987608e-05
#> ... Procrustes: rmse 0.05424793 max resid 0.1583581
#> Run 17 stress 0.042751
#> Run 18 stress 1.847916e-05
#> ... Procrustes: rmse 0.06464497 max resid 0.1532921
#> Run 19 stress 6.875621e-05
#> ... Procrustes: rmse 0.138447 max resid 0.3154074
#> Run 20 stress 9.688858e-05
#> ... Procrustes: rmse 0.04907303 max resid 0.1710118
#> *** Best solution was not repeated -- monoMDS stopping criteria:
#> 6: no. of iterations >= maxit
#> 13: stress < smin
#> 1: stress ratio > sratmax
#> Warning: stress is (nearly) zero: you may have insufficient data
#> species scores not available
# With Group argument
binaryMDS(PbHeron[,4:15],group=PbHeron$DosageGroup)
#> Run 0 stress 9.202781e-05
#> Run 1 stress 8.45528e-05
#> ... New best solution
#> ... Procrustes: rmse 0.1149288 max resid 0.2794459
#> Run 2 stress 0
#> ... New best solution
#> ... Procrustes: rmse 0.0523586 max resid 0.1266343
#> Run 3 stress 0.0004428903
#> ... Procrustes: rmse 0.05560114 max resid 0.1806574
#> Run 4 stress 3.43748e-05
#> ... Procrustes: rmse 0.04696447 max resid 0.1311468
#> Run 5 stress 0.004104365
#> Run 6 stress 9.898126e-05
#> ... Procrustes: rmse 0.03306021 max resid 0.06085298
#> Run 7 stress 0.0004019165
#> ... Procrustes: rmse 0.1029445 max resid 0.2927876
#> Run 8 stress 0.0590162
#> Run 9 stress 0.000261955
#> ... Procrustes: rmse 0.09829195 max resid 0.136617
#> Run 10 stress 8.105593e-05
#> ... Procrustes: rmse 0.04490103 max resid 0.1280056
#> Run 11 stress 0.0008435532
#> Run 12 stress 0.0002201946
#> ... Procrustes: rmse 0.0624239 max resid 0.09528927
#> Run 13 stress 9.47651e-05
#> ... Procrustes: rmse 0.1379335 max resid 0.355345
#> Run 14 stress 9.881733e-05
#> ... Procrustes: rmse 0.1016692 max resid 0.2885338
#> Run 15 stress 0.000285213
#> ... Procrustes: rmse 0.1100588 max resid 0.2981109
#> Run 16 stress 6.288697e-05
#> ... Procrustes: rmse 0.1432157 max resid 0.3684405
#> Run 17 stress 0.0002502776
#> ... Procrustes: rmse 0.1058918 max resid 0.2957649
#> Run 18 stress 0
#> ... Procrustes: rmse 0.1473273 max resid 0.386913
#> Run 19 stress 0
#> ... Procrustes: rmse 0.1268899 max resid 0.3745752
#> Run 20 stress 0
#> ... Procrustes: rmse 0.1359374 max resid 0.3595656
#> *** Best solution was not repeated -- monoMDS stopping criteria:
#> 9: no. of iterations >= maxit
#> 11: stress < smin
#> Warning: stress is (nearly) zero: you may have insufficient data
#> species scores not available
# }