sites.long: Helper Functions to Prepare Plotting of Accumulation,...
In BiodiversityR: Package for Community Ecology and Suitability Analysis
sites.long R Documentation
Helper Functions to Prepare Plotting of Accumulation, Diversity Profile and Ordiplot Results via ggplot2
Description
These functions organize outputs from ordiplot, accumcomp and renyicomp so these can be plotted with ggplot.
Usage
sites.long(x, env.data = NULL)
species.long(x, spec.data = NULL)
centroids.long(y, grouping, FUN = mean, centroids.only = FALSE)
vectorfit.long(z)
ordisurfgrid.long(z)
ordiellipse.long(z, grouping.name = "Grouping")
pvclust.long(cl, cl.data)
axis.long(w, choices = c(1, 2), cmdscale.model=FALSE, CAPdiscrim.model=FALSE)
accumcomp.long(x, ci = 2, label.freq = 1)
renyicomp.long(x, label.freq = 1)
renyi.long(x, env.data=NULL, label.freq = 1)
Arguments
x
Result of ordiplot, accumcomp or renyicomp
env.data
Environmental descriptors for each site.
spec.data
Descriptors for each species.
y
Result of function sites.long.
grouping
Variable defining the centroids
FUN
A function to compute the summary statistics which can be applied to all data subsets, as in aggregate
centroids.only
Return the coordinates for the centroids
z
Result of envfit, ordisurf or ordiellipse
grouping.name
Name for the categorical variable, expected as the factor used in the earlier ordiellipse call.
cl
Result of pvclust
cl.data
Result of ordicluster
w
Ordination object from which the ordiplot was obtained, expected to be fitted in vegan.
choices
Ordination axes selected, as in ordiplot
cmdscale.model
Use TRUE is the model was fitted via cmdscale
CAPdiscrim.model
Use TRUE is the model was fitted via CAPdiscrim
ci
Multiplier for confidence intervals as in specaccum. In case 'NA' is provided, then the multiplier is calculated via qt.
label.freq
Frequency of labels along the x-axis (count between labels).
Details
Examples for ordiplot results are shown below.
Function pvclust.long combines data from pvclust with coordinates of nodes and branches from ordicluster. The variable of prune allows to remove higher levels of nodes and branches in the clustering hierarchy in a similar way as argument prune for ordicluster - see examples.
See also section: see examples for species accumulation curves and Renyi diversity profiles
Value
These functions produce data.frames that can subsequentially be plotted via ggplot methods.
Author(s)
Roeland Kindt
References
Kindt, R. & Coe, R. (2005)
Tree diversity analysis: A manual and
software for common statistical methods for ecological and
biodiversity studies.
https://www.worldagroforestry.org/output/tree-diversity-analysis
https://rpubs.com/Roeland-KINDT
See Also
accumcomp, renyicomp
Examples
## Not run:
# ggplot2 plotting method
library(ggplot2)
library(ggforce)
library(concaveman)
library(ggrepel)
library(ggsci)
library(dplyr)
library(vegan)
data(dune)
data(dune.env)
attach(dune)
attach(dune.env)
Ordination.model1 <- capscale(dune ~ Management, data=dune.env,
distance='kulczynski', sqrt.dist=F, add='cailliez')
plot1 <- ordiplot(Ordination.model1, choices=c(1,2), scaling='species')
# obtain 'long' data from the ordiplot object
sites1 <- sites.long(plot1, env.data=dune.env)
species1 <- species.long(plot1)
centroids1 <- centroids.long(sites1, Management, FUN=median)
centroids2 <- centroids.long(sites1, Management, FUN=median, centroids.only=TRUE)
# information on percentage variation from the fitted ordination
axislabs <- axis.long(Ordination.model1, choices=c(1 , 2))
# change the theme
# possibly need for extrafont::loadfonts(device="win") to have Arial
# as alternative, use library(ggThemeAssist)
BioR.theme <- theme(
panel.background = element_blank(),
panel.border = element_blank(),
panel.grid = element_blank(),
axis.line = element_line("gray25"),
text = element_text(size = 12, family="Arial"),
axis.text = element_text(size = 10, colour = "gray25"),
axis.title = element_text(size = 14, colour = "gray25"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.key = element_blank())
# no species scores
# centroids calculated directly via the centroids.long function
plotgg1 <- ggplot() +
geom_vline(xintercept = c(0), color = "grey70", linetype = 2) +
geom_hline(yintercept = c(0), color = "grey70", linetype = 2) +
xlab(axislabs[1, "label"]) +
ylab(axislabs[2, "label"]) +
scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_mark_hull(data=sites1, aes(x=axis1, y=axis2, colour = Management),
concavity = 0.1, alpha=0.8, size=0.2, show.legend=FALSE) +
geom_point(data=sites1, aes(x=axis1, y=axis2, colour=Management, shape=Management),
size=5) +
# geom_segment(data=species1, aes(x=0, y=0, xend=axis1*2, yend=axis2*2),
# size=1.2, arrow=arrow()) +
# geom_label_repel(data=species1, aes(x=axis1*2, y=axis2*2, label=labels)) +
geom_point(data=centroids.long(sites1, grouping=Management, centroids.only=TRUE),
aes(x=axis1c, y=axis2c, colour=Centroid, shape=Centroid), size=10, show.legend=FALSE) +
geom_segment(data=centroids.long(sites1, grouping=Management),
aes(x=axis1c, y=axis2c, xend=axis1, yend=axis2, colour=Management),
size=1, show.legend=FALSE) +
BioR.theme +
ggsci::scale_colour_npg() +
coord_fixed(ratio=1)
plotgg1
# select species to plot based on goodness of fit
spec.envfit <- envfit(plot1, env=dune)
spec.data1 <- data.frame(r=spec.envfit$vectors$r, p=spec.envfit$vectors$pvals)
species2 <- species.long(plot1, spec.data=spec.data1)
species2 <- species2[species2$r > 0.6, ]
# after_scale introduced in ggplot2 3.3.0
plotgg2 <- ggplot() +
geom_vline(xintercept = c(0), color = "grey70", linetype = 2) +
geom_hline(yintercept = c(0), color = "grey70", linetype = 2) +
xlab(axislabs[1, "label"]) +
ylab(axislabs[2, "label"]) +
scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_mark_ellipse(data=sites1, aes(x=axis1, y=axis2,
colour=Management, fill=after_scale(alpha(colour, 0.2))),
expand=0, size=0.2, show.legend=TRUE) +
geom_point(data=sites1, aes(x=axis1, y=axis2, colour=Management, shape=Management),
size=5) +
geom_segment(data=centroids.long(sites1, grouping=Management),
aes(x=axis1c, y=axis2c, xend=axis1, yend=axis2, colour=Management),
size=1, show.legend=FALSE) +
geom_segment(data=species2, aes(x=0, y=0, xend=axis1*2, yend=axis2*2),
size=1.2, arrow=arrow()) +
geom_label_repel(data=species2, aes(x=axis1*2, y=axis2*2, label=labels)) +
BioR.theme +
ggsci::scale_colour_npg() +
coord_fixed(ratio=1)
plotgg2
# Add contour and vector for a continuous explanatory variable
Ordination.model2 <- capscale(dune ~ Management, data=dune.env,
distance='kulczynski', sqrt.dist=F, add='cailliez')
plot2 <- ordiplot(Ordination.model2, choices=c(1,2), scaling='species')
sites2 <- sites.long(plot2, env.data=dune.env)
axislabs <- axis.long(Ordination.model2, choices=c(1 , 2))
dune.envfit <- envfit(plot2, dune.env)
vectors2 <- vectorfit.long(dune.envfit)
A1.surface <- ordisurf(plot2, y=A1)
A1.surface
A1.grid <- ordisurfgrid.long(A1.surface)
plotgg3 <- ggplot() +
geom_contour_filled(data=A1.grid, aes(x=x, y=y, z=z)) +
geom_vline(xintercept = c(0), color = "grey70", linetype = 2) +
geom_hline(yintercept = c(0), color = "grey70", linetype = 2) +
xlab(axislabs[1, "label"]) +
ylab(axislabs[2, "label"]) +
scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_point(data=sites2, aes(x=axis1, y=axis2, size=A1), shape=21, colour="black", fill="red") +
geom_segment(data=subset(vectors2, vector=A1), aes(x=0, y=0, xend=axis1*2, yend=axis2*2),
size=1.2, arrow=arrow()) +
geom_label_repel(data=subset(vectors2, vector=A1), aes(x=axis1*2, y=axis2*2,
label=vector), size=5) +
BioR.theme +
scale_fill_viridis_d() +
scale_size(range=c(1, 20)) +
labs(fill="A1") +
coord_fixed(ratio=1)
plotgg3
# after_stat introduced in ggplot2 3.3.0
plotgg4 <- ggplot() +
geom_contour(data=A1.grid, aes(x=x, y=y, z=z, colour=factor(after_stat(level))), size=2) +
geom_vline(xintercept = c(0), color = "grey70", linetype = 2) +
geom_hline(yintercept = c(0), color = "grey70", linetype = 2) +
xlab(axislabs[1, "label"]) +
ylab(axislabs[2, "label"]) +
scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_point(data=sites2, aes(x=axis1, y=axis2, size=A1), shape=21, colour="black", fill="red") +
geom_label_repel(data=sites2, aes(x=axis1, y=axis2, label=labels),
colour='black', size=4) +
BioR.theme +
scale_colour_viridis_d() +
scale_size(range=c(1, 20)) +
labs(colour="A1") +
coord_fixed(ratio=1)
plotgg4
# example of Voronoi segmentation
plotgg5 <- ggplot(data=sites2, aes(x=axis1, y=axis2)) +
geom_voronoi_tile(aes(fill = Management, group=-1L), max.radius=0.2) +
geom_voronoi_segment(colour="grey50") +
geom_vline(xintercept = c(0), color = "grey70", linetype = 2) +
geom_hline(yintercept = c(0), color = "grey70", linetype = 2) +
xlab(axislabs[1, "label"]) +
ylab(axislabs[2, "label"]) +
scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_point() +
BioR.theme +
ggsci::scale_colour_npg() +
coord_fixed(ratio=1)
plotgg5
# adding ellipse via ordiellipse
plot3 <- ordiplot(Ordination.model1, choices=c(1,2), scaling='species')
axislabs <- axis.long(Ordination.model1, choices=c(1 , 2))
Management.ellipses <- ordiellipse(plot3, groups=Management, display="sites", kind="sd")
Management.ellipses.long2 <- ordiellipse.long(Management.ellipses, grouping.name="Management")
plotgg6 <- ggplot() +
geom_vline(xintercept = c(0), color = "grey70", linetype = 2) +
geom_hline(yintercept = c(0), color = "grey70", linetype = 2) +
xlab(axislabs[1, "label"]) +
ylab(axislabs[2, "label"]) +
scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_polygon(data=Management.ellipses.long2,
aes(x=axis1, y=axis2, colour=Management,
fill=after_scale(alpha(colour, 0.2))),
size=0.2, show.legend=FALSE) +
geom_point(data=sites1, aes(x=axis1, y=axis2, colour=Management, shape=Management),
size=5) +
geom_segment(data=centroids.long(sites1, grouping=Management),
aes(x=axis1c, y=axis2c, xend=axis1, yend=axis2, colour=Management),
size=1, show.legend=FALSE) +
BioR.theme +
ggsci::scale_colour_npg() +
coord_fixed(ratio=1)
plotgg6
# adding cluster results via pvclust.long
library(pvclust)
# transformation as pvclust works with Euclidean distance
dune.Hellinger <- disttransform(dune, method='hellinger')
dune.pv <- pvclust(t(dune.Hellinger),
method.hclust="mcquitty",
method.dist="euclidean",
nboot=1000)
plot(dune.pv)
pvrect(dune.pv, alpha=0.89, pv="au")
# Model fitted earlier
plot1 <- ordiplot(Ordination.model1, choices=c(1,2), scaling='species')
cl.data1 <- ordicluster(plot1, cluster=as.hclust(dune.pv$hclust))
sites1 <- sites.long(plot1, env.data=dune.env)
axislabs <- axis.long(Ordination.model1, choices=c(1 , 2))
cl.data1 <- ordicluster(plot2, cluster=as.hclust(dune.pv$hclust))
pvlong <- pvclust.long(dune.pv, cl.data1)
# as in example for ordicluster, prune higher level hierarchies
plotgg7 <- ggplot() +
geom_vline(xintercept = c(0), color = "grey70", linetype = 2) +
geom_hline(yintercept = c(0), color = "grey70", linetype = 2) +
xlab(axislabs[1, "label"]) +
ylab(axislabs[2, "label"]) +
scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_segment(data=subset(pvlong$segments, pvlong$segments$prune > 3),
aes(x=x1, y=y1, xend=x2, yend=y2, colour=au>=0.89,
size=au),
show.legend=TRUE) +
geom_point(data=subset(pvlong$nodes, pvlong$nodes$prune > 3),
aes(x=x, y=y, fill=au>=0.89),
shape=21, size=2, colour="black") +
geom_point(data=sites1,
aes(x=axis1, y=axis2, shape=Management),
colour="darkolivegreen4", alpha=0.9, size=5) +
geom_text(data=sites1,
aes(x=axis1, y=axis2, label=labels)) +
BioR.theme +
ggsci::scale_colour_npg() +
scale_size(range=c(0.3, 2)) +
scale_shape_manual(values=c(15, 16, 17, 18)) +
guides(shape = guide_legend(override.aes = list(linetype = 0))) +
coord_fixed(ratio=1)
plotgg7
## End(Not run) # dontrun
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| sites.long | R Documentation |
Helper Functions to Prepare Plotting of Accumulation, Diversity Profile and Ordiplot Results via ggplot2
Description
These functions organize outputs from ordiplot, accumcomp and renyicomp so these can be plotted with ggplot.
Usage
sites.long(x, env.data = NULL)
species.long(x, spec.data = NULL)
centroids.long(y, grouping, FUN = mean, centroids.only = FALSE)
vectorfit.long(z)
ordisurfgrid.long(z)
ordiellipse.long(z, grouping.name = "Grouping")
pvclust.long(cl, cl.data)
axis.long(w, choices = c(1, 2), cmdscale.model=FALSE, CAPdiscrim.model=FALSE)
accumcomp.long(x, ci = 2, label.freq = 1)
renyicomp.long(x, label.freq = 1)
renyi.long(x, env.data=NULL, label.freq = 1)
Arguments
x |
Result of |
env.data |
Environmental descriptors for each site. |
spec.data |
Descriptors for each species. |
y |
Result of function sites.long. |
grouping |
Variable defining the centroids |
FUN |
A function to compute the summary statistics which can be applied to all data subsets, as in |
centroids.only |
Return the coordinates for the centroids |
z |
Result of |
grouping.name |
Name for the categorical variable, expected as the factor used in the earlier ordiellipse call. |
cl |
Result of |
cl.data |
Result of |
w |
Ordination object from which the |
choices |
Ordination axes selected, as in |
cmdscale.model |
Use |
CAPdiscrim.model |
Use |
ci |
Multiplier for confidence intervals as in |
label.freq |
Frequency of labels along the x-axis (count between labels). |
Details
Examples for ordiplot results are shown below.
Function pvclust.long combines data from pvclust with coordinates of nodes and branches from ordicluster. The variable of prune allows to remove higher levels of nodes and branches in the clustering hierarchy in a similar way as argument prune for ordicluster - see examples.
See also section: see examples for species accumulation curves and Renyi diversity profiles
Value
These functions produce data.frames that can subsequentially be plotted via ggplot methods.
Author(s)
Roeland Kindt
References
Kindt, R. & Coe, R. (2005) Tree diversity analysis: A manual and software for common statistical methods for ecological and biodiversity studies.
https://www.worldagroforestry.org/output/tree-diversity-analysis
https://rpubs.com/Roeland-KINDT
See Also
accumcomp, renyicomp
Examples
## Not run:
# ggplot2 plotting method
library(ggplot2)
library(ggforce)
library(concaveman)
library(ggrepel)
library(ggsci)
library(dplyr)
library(vegan)
data(dune)
data(dune.env)
attach(dune)
attach(dune.env)
Ordination.model1 <- capscale(dune ~ Management, data=dune.env,
distance='kulczynski', sqrt.dist=F, add='cailliez')
plot1 <- ordiplot(Ordination.model1, choices=c(1,2), scaling='species')
# obtain 'long' data from the ordiplot object
sites1 <- sites.long(plot1, env.data=dune.env)
species1 <- species.long(plot1)
centroids1 <- centroids.long(sites1, Management, FUN=median)
centroids2 <- centroids.long(sites1, Management, FUN=median, centroids.only=TRUE)
# information on percentage variation from the fitted ordination
axislabs <- axis.long(Ordination.model1, choices=c(1 , 2))
# change the theme
# possibly need for extrafont::loadfonts(device="win") to have Arial
# as alternative, use library(ggThemeAssist)
BioR.theme <- theme(
panel.background = element_blank(),
panel.border = element_blank(),
panel.grid = element_blank(),
axis.line = element_line("gray25"),
text = element_text(size = 12, family="Arial"),
axis.text = element_text(size = 10, colour = "gray25"),
axis.title = element_text(size = 14, colour = "gray25"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.key = element_blank())
# no species scores
# centroids calculated directly via the centroids.long function
plotgg1 <- ggplot() +
geom_vline(xintercept = c(0), color = "grey70", linetype = 2) +
geom_hline(yintercept = c(0), color = "grey70", linetype = 2) +
xlab(axislabs[1, "label"]) +
ylab(axislabs[2, "label"]) +
scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_mark_hull(data=sites1, aes(x=axis1, y=axis2, colour = Management),
concavity = 0.1, alpha=0.8, size=0.2, show.legend=FALSE) +
geom_point(data=sites1, aes(x=axis1, y=axis2, colour=Management, shape=Management),
size=5) +
# geom_segment(data=species1, aes(x=0, y=0, xend=axis1*2, yend=axis2*2),
# size=1.2, arrow=arrow()) +
# geom_label_repel(data=species1, aes(x=axis1*2, y=axis2*2, label=labels)) +
geom_point(data=centroids.long(sites1, grouping=Management, centroids.only=TRUE),
aes(x=axis1c, y=axis2c, colour=Centroid, shape=Centroid), size=10, show.legend=FALSE) +
geom_segment(data=centroids.long(sites1, grouping=Management),
aes(x=axis1c, y=axis2c, xend=axis1, yend=axis2, colour=Management),
size=1, show.legend=FALSE) +
BioR.theme +
ggsci::scale_colour_npg() +
coord_fixed(ratio=1)
plotgg1
# select species to plot based on goodness of fit
spec.envfit <- envfit(plot1, env=dune)
spec.data1 <- data.frame(r=spec.envfit$vectors$r, p=spec.envfit$vectors$pvals)
species2 <- species.long(plot1, spec.data=spec.data1)
species2 <- species2[species2$r > 0.6, ]
# after_scale introduced in ggplot2 3.3.0
plotgg2 <- ggplot() +
geom_vline(xintercept = c(0), color = "grey70", linetype = 2) +
geom_hline(yintercept = c(0), color = "grey70", linetype = 2) +
xlab(axislabs[1, "label"]) +
ylab(axislabs[2, "label"]) +
scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_mark_ellipse(data=sites1, aes(x=axis1, y=axis2,
colour=Management, fill=after_scale(alpha(colour, 0.2))),
expand=0, size=0.2, show.legend=TRUE) +
geom_point(data=sites1, aes(x=axis1, y=axis2, colour=Management, shape=Management),
size=5) +
geom_segment(data=centroids.long(sites1, grouping=Management),
aes(x=axis1c, y=axis2c, xend=axis1, yend=axis2, colour=Management),
size=1, show.legend=FALSE) +
geom_segment(data=species2, aes(x=0, y=0, xend=axis1*2, yend=axis2*2),
size=1.2, arrow=arrow()) +
geom_label_repel(data=species2, aes(x=axis1*2, y=axis2*2, label=labels)) +
BioR.theme +
ggsci::scale_colour_npg() +
coord_fixed(ratio=1)
plotgg2
# Add contour and vector for a continuous explanatory variable
Ordination.model2 <- capscale(dune ~ Management, data=dune.env,
distance='kulczynski', sqrt.dist=F, add='cailliez')
plot2 <- ordiplot(Ordination.model2, choices=c(1,2), scaling='species')
sites2 <- sites.long(plot2, env.data=dune.env)
axislabs <- axis.long(Ordination.model2, choices=c(1 , 2))
dune.envfit <- envfit(plot2, dune.env)
vectors2 <- vectorfit.long(dune.envfit)
A1.surface <- ordisurf(plot2, y=A1)
A1.surface
A1.grid <- ordisurfgrid.long(A1.surface)
plotgg3 <- ggplot() +
geom_contour_filled(data=A1.grid, aes(x=x, y=y, z=z)) +
geom_vline(xintercept = c(0), color = "grey70", linetype = 2) +
geom_hline(yintercept = c(0), color = "grey70", linetype = 2) +
xlab(axislabs[1, "label"]) +
ylab(axislabs[2, "label"]) +
scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_point(data=sites2, aes(x=axis1, y=axis2, size=A1), shape=21, colour="black", fill="red") +
geom_segment(data=subset(vectors2, vector=A1), aes(x=0, y=0, xend=axis1*2, yend=axis2*2),
size=1.2, arrow=arrow()) +
geom_label_repel(data=subset(vectors2, vector=A1), aes(x=axis1*2, y=axis2*2,
label=vector), size=5) +
BioR.theme +
scale_fill_viridis_d() +
scale_size(range=c(1, 20)) +
labs(fill="A1") +
coord_fixed(ratio=1)
plotgg3
# after_stat introduced in ggplot2 3.3.0
plotgg4 <- ggplot() +
geom_contour(data=A1.grid, aes(x=x, y=y, z=z, colour=factor(after_stat(level))), size=2) +
geom_vline(xintercept = c(0), color = "grey70", linetype = 2) +
geom_hline(yintercept = c(0), color = "grey70", linetype = 2) +
xlab(axislabs[1, "label"]) +
ylab(axislabs[2, "label"]) +
scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_point(data=sites2, aes(x=axis1, y=axis2, size=A1), shape=21, colour="black", fill="red") +
geom_label_repel(data=sites2, aes(x=axis1, y=axis2, label=labels),
colour='black', size=4) +
BioR.theme +
scale_colour_viridis_d() +
scale_size(range=c(1, 20)) +
labs(colour="A1") +
coord_fixed(ratio=1)
plotgg4
# example of Voronoi segmentation
plotgg5 <- ggplot(data=sites2, aes(x=axis1, y=axis2)) +
geom_voronoi_tile(aes(fill = Management, group=-1L), max.radius=0.2) +
geom_voronoi_segment(colour="grey50") +
geom_vline(xintercept = c(0), color = "grey70", linetype = 2) +
geom_hline(yintercept = c(0), color = "grey70", linetype = 2) +
xlab(axislabs[1, "label"]) +
ylab(axislabs[2, "label"]) +
scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_point() +
BioR.theme +
ggsci::scale_colour_npg() +
coord_fixed(ratio=1)
plotgg5
# adding ellipse via ordiellipse
plot3 <- ordiplot(Ordination.model1, choices=c(1,2), scaling='species')
axislabs <- axis.long(Ordination.model1, choices=c(1 , 2))
Management.ellipses <- ordiellipse(plot3, groups=Management, display="sites", kind="sd")
Management.ellipses.long2 <- ordiellipse.long(Management.ellipses, grouping.name="Management")
plotgg6 <- ggplot() +
geom_vline(xintercept = c(0), color = "grey70", linetype = 2) +
geom_hline(yintercept = c(0), color = "grey70", linetype = 2) +
xlab(axislabs[1, "label"]) +
ylab(axislabs[2, "label"]) +
scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_polygon(data=Management.ellipses.long2,
aes(x=axis1, y=axis2, colour=Management,
fill=after_scale(alpha(colour, 0.2))),
size=0.2, show.legend=FALSE) +
geom_point(data=sites1, aes(x=axis1, y=axis2, colour=Management, shape=Management),
size=5) +
geom_segment(data=centroids.long(sites1, grouping=Management),
aes(x=axis1c, y=axis2c, xend=axis1, yend=axis2, colour=Management),
size=1, show.legend=FALSE) +
BioR.theme +
ggsci::scale_colour_npg() +
coord_fixed(ratio=1)
plotgg6
# adding cluster results via pvclust.long
library(pvclust)
# transformation as pvclust works with Euclidean distance
dune.Hellinger <- disttransform(dune, method='hellinger')
dune.pv <- pvclust(t(dune.Hellinger),
method.hclust="mcquitty",
method.dist="euclidean",
nboot=1000)
plot(dune.pv)
pvrect(dune.pv, alpha=0.89, pv="au")
# Model fitted earlier
plot1 <- ordiplot(Ordination.model1, choices=c(1,2), scaling='species')
cl.data1 <- ordicluster(plot1, cluster=as.hclust(dune.pv$hclust))
sites1 <- sites.long(plot1, env.data=dune.env)
axislabs <- axis.long(Ordination.model1, choices=c(1 , 2))
cl.data1 <- ordicluster(plot2, cluster=as.hclust(dune.pv$hclust))
pvlong <- pvclust.long(dune.pv, cl.data1)
# as in example for ordicluster, prune higher level hierarchies
plotgg7 <- ggplot() +
geom_vline(xintercept = c(0), color = "grey70", linetype = 2) +
geom_hline(yintercept = c(0), color = "grey70", linetype = 2) +
xlab(axislabs[1, "label"]) +
ylab(axislabs[2, "label"]) +
scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_segment(data=subset(pvlong$segments, pvlong$segments$prune > 3),
aes(x=x1, y=y1, xend=x2, yend=y2, colour=au>=0.89,
size=au),
show.legend=TRUE) +
geom_point(data=subset(pvlong$nodes, pvlong$nodes$prune > 3),
aes(x=x, y=y, fill=au>=0.89),
shape=21, size=2, colour="black") +
geom_point(data=sites1,
aes(x=axis1, y=axis2, shape=Management),
colour="darkolivegreen4", alpha=0.9, size=5) +
geom_text(data=sites1,
aes(x=axis1, y=axis2, label=labels)) +
BioR.theme +
ggsci::scale_colour_npg() +
scale_size(range=c(0.3, 2)) +
scale_shape_manual(values=c(15, 16, 17, 18)) +
guides(shape = guide_legend(override.aes = list(linetype = 0))) +
coord_fixed(ratio=1)
plotgg7
## End(Not run) # dontrun
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