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Publication-Quality Non-metric Multi-dimensional Scaling (NMS) Ordinations

Source: R/nms_ord.R
nms_ord.Rd

[Superseded]

This function has been superseded by ordination because this is just a special case of that function. Additionally, ordination provides users much more control over the internal graphics functions used to create the fundamental elements of the graph

Produces Non-Metric Multi-dimensional Scaling (NMS) ordinations for up to 10 groups. Assigns a unique color for each group and draws an ellipse around the standard deviation of the points. Automatically adds stress (see vegan::metaMDS for explanation of "stress") as legend title. Because there are only five hollow shapes (see ?graphics::pch()) all shapes are re-used a maximum of 2 times when more than 5 groups are supplied.

Usage

nms_ord(
  mod = NULL,
  groupcol = NULL,
  title = NA,
  colors = c("#41b6c4", "#c51b7d", "#7fbc41", "#d73027", "#4575b4", "#e08214", "#8073ac",
    "#f1b6da", "#b8e186", "#8c96c6"),
  shapes = rep(x = 21:25, times = 2),
  lines = rep(x = 1, times = 10),
  pt_size = 1.5,
  pt_alpha = 1,
  lab_text_size = 1.25,
  axis_text_size = 1,
  leg_pos = "bottomleft",
  leg_cont = unique(groupcol)
)

Arguments

mod

(metaMDS/monoMDS) object returned by vegan::metaMDS

groupcol

(dataframe) column specification in the data that includes the groups (accepts either bracket or $ notation)

title

(character) string to use as title for plot

colors

(character) vector of colors (as hexadecimal codes) of length >= group levels (default not colorblind safe because of need for 10 built-in unique colors)

shapes

(numeric) vector of shapes (as values accepted by pch) of length >= group levels

lines

(numeric) vector of line types (as integers) of length >= group levels

pt_size

(numeric) value for point size (controlled by character expansion i.e., cex)

pt_alpha

(numeric) value for transparency of points (ranges from 0 to 1)

lab_text_size

(numeric) value for axis label text size

axis_text_size

(numeric) value for axis tick text size

leg_pos

(character or numeric) legend position, either numeric vector of x/y coordinates or shorthand accepted by graphics::legend

leg_cont

(character) vector of desired legend entries. Defaults to unique entries in groupcol argument (this argument provided in case syntax of legend contents should differ from data contents)

Value

(plot) base R ordination with an ellipse for each group

Examples

# \donttest{
# Use data from the vegan package
utils::data("varespec", package = 'vegan')
resp <- varespec

# Make some columns of known number of groups
factor_4lvl <- c(rep.int("Trt1", (nrow(resp)/4)),
                 rep.int("Trt2", (nrow(resp)/4)),
                 rep.int("Trt3", (nrow(resp)/4)),
                 rep.int("Trt4", (nrow(resp)/4)))

# And combine them into a single data object
data <- cbind(factor_4lvl, resp)

# Actually perform multidimensional scaling
mds <- vegan::metaMDS(data[-1], autotransform = FALSE, expand = FALSE, k = 2, try = 50)
#> Run 0 stress 0.1000211 
#> Run 1 stress 0.1715395 
#> Run 2 stress 0.1000211 
#> ... Procrustes: rmse 1.068764e-05  max resid 4.170213e-05 
#> ... Similar to previous best
#> Run 3 stress 0.1605747 
#> Run 4 stress 0.1613668 
#> Run 5 stress 0.1000211 
#> ... New best solution
#> ... Procrustes: rmse 3.277054e-06  max resid 1.416261e-05 
#> ... Similar to previous best
#> Run 6 stress 0.1607499 
#> Run 7 stress 0.1000211 
#> ... New best solution
#> ... Procrustes: rmse 3.547968e-07  max resid 1.025797e-06 
#> ... Similar to previous best
#> Run 8 stress 0.1000211 
#> ... Procrustes: rmse 6.553308e-06  max resid 2.717474e-05 
#> ... Similar to previous best
#> Run 9 stress 0.1000211 
#> ... Procrustes: rmse 1.847283e-05  max resid 5.887532e-05 
#> ... Similar to previous best
#> Run 10 stress 0.1000211 
#> ... Procrustes: rmse 1.638284e-06  max resid 6.797019e-06 
#> ... Similar to previous best
#> Run 11 stress 0.1607505 
#> Run 12 stress 0.2186732 
#> Run 13 stress 0.1000211 
#> ... Procrustes: rmse 9.336186e-06  max resid 4.046768e-05 
#> ... Similar to previous best
#> Run 14 stress 0.1000211 
#> ... Procrustes: rmse 1.860223e-05  max resid 7.962999e-05 
#> ... Similar to previous best
#> Run 15 stress 0.1000211 
#> ... New best solution
#> ... Procrustes: rmse 3.514954e-06  max resid 1.498052e-05 
#> ... Similar to previous best
#> Run 16 stress 0.1000211 
#> ... Procrustes: rmse 1.420825e-05  max resid 6.162886e-05 
#> ... Similar to previous best
#> Run 17 stress 0.160494 
#> Run 18 stress 0.1000211 
#> ... Procrustes: rmse 6.506975e-06  max resid 2.816072e-05 
#> ... Similar to previous best
#> Run 19 stress 0.1733341 
#> Run 20 stress 0.1000211 
#> ... Procrustes: rmse 1.532844e-05  max resid 6.633466e-05 
#> ... Similar to previous best
#> *** Best solution repeated 4 times

# With the scaled object and original dataframe we can use this function
nms_ord(mod = mds, groupcol = data$factor_4lvl,
                title = '4-Level NMS', leg_pos = 'topright',
                leg_cont = as.character(1:4))

# }