mp_envfit {MicrobiotaProcess} | R Documentation

## 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)

[Package MicrobiotaProcess version 1.4.4 Index]