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Building envfit vectors (vegan package) in ggplot2

I am working on completing the NMDS graph that I created in vegan and ggplot2, but cannot figure out how to add envfit view loading vectors to the plot. When I try, this indicates an invalid graphic state.

The example below is slightly modified from another question ( Building the ordiellipse function from the vegan package on the NMDS graph created in ggplot2 ), but he expressed exactly the example I wanted to include since I used this question to help me get metaMDS in ggplot2 in first of all:

library(vegan)
library(ggplot2)
data(dune)

# calculate distance for NMDS
NMDS.log<-log(dune+1)
sol <- metaMDS(NMDS.log)

# Create meta data for grouping
MyMeta = data.frame(
  sites = c(2,13,4,16,6,1,8,5,17,15,10,11,9,18,3,20,14,19,12,7),
  amt = c("hi", "hi", "hi", "md", "lo", "hi", "hi", "lo", "md", "md", "lo", 
      "lo", "hi", "lo", "hi", "md", "md", "lo", "hi", "lo"),
row.names = "sites")

# plot NMDS using basic plot function and color points by "amt" from MyMeta
plot(sol$points, col = MyMeta$amt)

# same in ggplot2
NMDS = data.frame(MDS1 = sol$points[,1], MDS2 = sol$points[,2])
ggplot(data = NMDS, aes(MDS1, MDS2)) + 
  geom_point(aes(data = MyMeta, color = MyMeta$amt))

#Add species loadings
vec.sp<-envfit(sol$points, NMDS.log, perm=1000)
plot(vec.sp, p.max=0.1, col="blue")
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3 answers

Start by adding libraries. In addition, a library is needed grid.

library(ggplot2)
library(vegan)
library(grid)
data(dune)

metaMDS .

NMDS.log<-log(dune+1)
sol <- metaMDS(NMDS.log)

NMDS = data.frame(MDS1 = sol$points[,1], MDS2 = sol$points[,2])

. vectors arrows. , r2, vectors$r. - scores(), @Gavin Simpson. , species.

vec.sp<-envfit(sol$points, NMDS.log, perm=1000)
vec.sp.df<-as.data.frame(vec.sp$vectors$arrows*sqrt(vec.sp$vectors$r))
vec.sp.df$species<-rownames(vec.sp.df)

geom_segment() geom_text(). vec.sp.df.

ggplot(data = NMDS, aes(MDS1, MDS2)) + 
  geom_point(aes(data = MyMeta, color = MyMeta$amt))+
  geom_segment(data=vec.sp.df,aes(x=0,xend=MDS1,y=0,yend=MDS2),
      arrow = arrow(length = unit(0.5, "cm")),colour="grey",inherit_aes=FALSE) + 
  geom_text(data=vec.sp.df,aes(x=MDS1,y=MDS2,label=species),size=5)+
  coord_fixed()

enter image description here

+3

( ) , [ , , , , Q & A], , , $vectors$arrows , envfit(), . , , , @Didzis Elferts . plot(envfit(sol, NMDS.log)) , ( "" ). , , , . sqrt(r2), r2 - , . vegan , , . , ?envfit vegan:::ordiArrowMul(result_of_envfit).

, plot.envfit ggplot2:

library(vegan)
library(ggplot2)
library(grid)
data(dune)

# calculate distance for NMDS
NMDS.log<-log1p(dune)
set.seed(42)
sol <- metaMDS(NMDS.log)

scrs <- as.data.frame(scores(sol, display = "sites"))
scrs <- cbind(scrs, Group = c("hi","hi","hi","md","lo","hi","hi","lo","md","md",
                              "lo","lo","hi","lo","hi","md","md","lo","hi","lo"))

set.seed(123)
vf <- envfit(sol, NMDS.log, perm = 999)

vf:

> vf

***VECTORS

             NMDS1       NMDS2     r2 Pr(>r)    
Belper -0.78061195 -0.62501598 0.1942  0.174    
Empnig -0.01315693  0.99991344 0.2501  0.054 .  
Junbuf  0.22941001 -0.97332987 0.1397  0.293    
Junart  0.99999981 -0.00062172 0.3647  0.022 *  
Airpra -0.20995196  0.97771170 0.5376  0.002 ** 
Elepal  0.98959723  0.14386566 0.6634  0.001 ***
Rumace -0.87985767 -0.47523728 0.0948  0.429
.... <truncated>

, r2 NMDS1 NMDS2. :

spp.scrs <- as.data.frame(scores(vf, display = "vectors"))
spp.scrs <- cbind(spp.scrs, Species = rownames(spp.scrs))

p <- ggplot(scrs) +
  geom_point(mapping = aes(x = NMDS1, y = NMDS2, colour = Group)) +
  coord_fixed() + ## need aspect ratio of 1!
  geom_segment(data = spp.scrs,
               aes(x = 0, xend = NMDS1, y = 0, yend = NMDS2),
               arrow = arrow(length = unit(0.25, "cm")), colour = "grey") +
  geom_text(data = spp.scrs, aes(x = NMDS1, y = NMDS2, label = Species),
            size = 3)

:

enter image description here

+6

- ?

Envfit pvalues, (-, vegan p = 0,05 plot). ggplot2. , , ?

:

ef<-envfit(sol$points, NMDS.log, perm=1000)
ef.df<-as.data.frame(ef$vectors$arrows*sqrt(ef$vectors$r))
ef.df$species<-rownames(ef.df)

#only significant pvalues
#shortcutting ef$vectors
A <- as.list(ef$vectors)
#creating the dataframe
pvals<-as.data.frame(A$pvals)
arrows<-as.data.frame(A$arrows*sqrt(A$r))
C<-cbind(arrows, pvals)
#subset
Cred<-subset(C,pvals<0.05)
Cred <- cbind(Cred, Species = rownames(Cred))

"Cred" geom_segment, .

+2

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