mp_envfit-methods: Fits an Environmental Vector or Factor onto an Ordination...

In YuLab-SMU/MicrobiotaProcess: A comprehensive R package for managing and analyzing microbiome and other ecological data within the tidy framework

Fits an Environmental Vector or Factor onto an Ordination With MPSE or tbl_mpse Object

Description

Fits an Environmental Vector or Factor onto an Ordination With MPSE or tbl_mpse Object

Usage

mp_envfit(
  .data,
  .ord,
  .env,
  .dim = 3,
  action = "only",
  permutations = 999,
  seed = 123,
  ...
)

## S4 method for signature 'MPSE'
mp_envfit(
  .data,
  .ord,
  .env,
  .dim = 3,
  action = "only",
  permutations = 999,
  seed = 123,
  ...
)

## S4 method for signature 'tbl_mpse'
mp_envfit(
  .data,
  .ord,
  .env,
  .dim = 3,
  action = "only",
  permutations = 999,
  seed = 123,
  ...
)

## S4 method for signature 'grouped_df_mpse'
mp_envfit(
  .data,
  .ord,
  .env,
  .dim = 3,
  action = "only",
  permutations = 999,
  seed = 123,
  ...
)

Arguments

.data	MPSE or tbl_mpse object
.ord	a name of ordination, option it is DCA, NMDS, RDA, CCA.
.env	the names of columns of sample group or environment information.
.dim	integer The number of dimensions to be returned, default is 3.
action	character "add" joins the envfit result to internal attributes of the object, "only" return a non-redundant tibble with the envfit result. "get" return 'envfit' object can be analyzed using the related vegan funtion.
permutations	the number of permutations required, default is 999.
seed	a random seed to make the analysis reproducible, default is 123.
...	additional parameters see also 'vegan::envfit'

Value

update object according action

Author(s)

Shuangbin Xu

Examples

library(vegan)
data(varespec, varechem)
mpse <- MPSE(assays=list(Abundance=t(varespec)), colData=varechem)
envformula <- paste("~", paste(colnames(varechem), collapse="+")) %>% as.formula
mpse %<>% 
       mp_cal_cca(.abundance=Abundance, .formula=envformula, action="add")
mpse2 <- mpse %>%
         mp_envfit(.ord=cca, 
                   .env=colnames(varechem), 
                   permutations=9999, 
                   action="add")
mpse2 %>% mp_plot_ord(.ord=cca, .group=Al, .size=Mn, show.shample=TRUE, show.envfit=TRUE)
## Not run: 
tbl <- mpse %>%
       mp_envfit(.ord=CCA, 
                 .env=colnames(varechem), 
                 permutations=9999, 
                 action="only")
tbl
library(ggplot2)
library(ggrepel)
x <- names(tbl)[grepl("^CCA1 ", names(tbl))] %>% as.symbol()
y <- names(tbl)[grepl("^CCA2 ", names(tbl))] %>% as.symbol()
p <- tbl %>%
     ggplot(aes(x=!!x, y=!!y)) + 
     geom_point(aes(color=Al, size=Mn)) + 
     geom_segment(data=dr_extract(
                            name="CCA_ENVFIT_tb", 
                            .f=td_filter(pvals<=0.05 & label!="Humdepth")
                       ), 
                  aes(x=0, y=0, xend=CCA1, yend=CCA2), 
                  arrow=arrow(length = unit(0.02, "npc"))
     ) + 
     geom_text_repel(data=dr_extract(
                              name="CCA_ENVFIT_tb", 
                              .f=td_filter(pvals<=0.05 & label!="Humdepth")
                          ), 
                  aes(x=CCA1, y=CCA2, label=label)
     ) +
     geom_vline(xintercept=0, color="grey20", linetype=2) +
     geom_hline(yintercept=0, color="grey20", linetype=2) +
     theme_bw() +
     theme(panel.grid=element_blank())
p

## End(Not run)

YuLab-SMU/MicrobiotaProcess documentation built on Nov. 8, 2024, 4:37 p.m.