trans_beta: Create trans_beta object for the analysis of distance matrix...

Description Methods Examples

Description

This class is a wrapper for a series of beta-diversity related analysis, including several ordination calculations and plotting based on An et al. (2019) <doi:10.1016/j.geoderma.2018.09.035>, group distance comparision, clustering and perMANOVA based on Anderson al. (2008) <doi:10.1111/j.1442-9993.2001.01070.pp.x>.

Methods

Public methods


Method new()

Usage
trans_beta$new(
  dataset = NULL,
  ordination = NULL,
  measure = NULL,
  group = NULL,
  trans_otu = FALSE,
  ncomp = 3,
  scale_species = FALSE
)
Arguments
dataset

the object of microtable Class.

ordination

default NULL; PCA, PCoA or NMDS.

measure

default NULL; bray, jaccard, wei_unifrac or unwei_unifrac, or other name of matrix you add; beta diversity index used for ordination, manova or group distance.

group

default NULL; sample group used for manova or group distance.

trans_otu

default FALSE; whether species abundance will be square transformed, used for PCA.

ncomp

default 3; the returned dimensions.

scale_species

default FALSE; whether species loading in PCA will be scaled.

Returns

res_ordination stored in the object.

Examples
data(dataset)
t1 <- trans_beta$new(dataset = dataset, ordination = "PCoA", measure = "bray", group = "Group")

Method plot_ordination()

Plotting the ordination result based on An et al. (2019) <doi:10.1016/j.geoderma.2018.09.035>.

Usage
trans_beta$plot_ordination(
  color_values = RColorBrewer::brewer.pal(8, "Dark2"),
  shape_values = c(16, 17, 7, 8, 15, 18, 11, 10, 12, 13, 9, 3, 4, 0, 1, 2, 14),
  plot_color = NULL,
  plot_shape = NULL,
  plot_group_order = NULL,
  plot_point_size = 3,
  plot_point_alpha = 0.9,
  plot_sample_label = NULL,
  plot_group_centroid = FALSE,
  plot_group = NULL,
  segment_alpha = 0.6,
  centroid_linetype = 3,
  plot_group_ellipse = FALSE,
  ellipse_level = 0.9,
  ellipse_alpha = 0.1,
  ellipse_type = "t"
)
Arguments
color_values

default RColorBrewer::brewer.pal(8, "Dark2"); colors for presentation.

shape_values

default c(16, 17, 7, 8, 15, 18, 11, 10, 12, 13, 9, 3, 4, 0, 1, 2, 14); a vector used in the shape type, see ggplot2 tutorial.

plot_color

default NULL; the sample group name used for color in plot.

plot_shape

default NULL; the sample group name used for shape in plot.

plot_group_order

default NULL; a vector used to order the groups in the legend of plot.

plot_point_size

default 3; point size in plot.

plot_point_alpha

default .9; point transparency in plot.

plot_sample_label

default NULL; the column name in sample table, if provided, show the point name in plot.

plot_group_centroid

default FALSE; whether show the centroid in each group of plot.

plot_group

default NULL; the column name in sample table, generally used with plot_group_centroid and plot_group_ellipse.

segment_alpha

default .6; segment transparency in plot.

centroid_linetype

default 3; the line type related with centroid in plot.

plot_group_ellipse

default FALSE; whether show the confidence ellipse in each group of plot.

ellipse_level

default .9; confidence level of ellipse.

ellipse_alpha

default .1; color transparency in the ellipse.

ellipse_type

default t; see type in stat_ellipse.

Returns

ggplot.

Examples
t1$plot_ordination(plot_color = "Group", plot_shape = "Group", plot_group_ellipse = TRUE)

Method cal_manova()

Calculate perMANOVA based on Anderson al. (2008) <doi:10.1111/j.1442-9993.2001.01070.pp.x> and R vegan adonis function.

Usage
trans_beta$cal_manova(
  cal_manova_all = FALSE,
  cal_manova_paired = FALSE,
  cal_manova_set = NULL,
  permutations = 999
)
Arguments
cal_manova_all

default FALSE; whether manova is used for all data.

cal_manova_paired

default FALSE; whether manova is used for all the paired groups.

cal_manova_set

default NULL; specified group set for manova, see adonis.

permutations

default 999; see permutations in adonis.

Returns

res_manova stored in object.

Examples
t1$cal_manova(cal_manova_all = TRUE)

Method cal_group_distance()

Transform sample distances within groups or between groups.

Usage
trans_beta$cal_group_distance(within_group = TRUE)
Arguments
within_group

default TRUE; whether transform sample distance within groups, if FALSE, transform sample distance between any two groups.

Returns

res_group_distance stored in object.

Examples
\donttest{
t1$cal_group_distance(within_group = TRUE)
}

Method plot_group_distance()

Plotting the distance between samples within or between groups.

Usage
trans_beta$plot_group_distance(
  plot_group_order = NULL,
  color_values = RColorBrewer::brewer.pal(8, "Dark2"),
  distance_pair_stat = FALSE,
  pair_compare_filter = "",
  pair_compare_method = "wilcox.test",
  plot_distance_xtype = NULL
)
Arguments
plot_group_order

default NULL; a vector used to order the groups in the plot.

color_values

colors for presentation.

distance_pair_stat

default FALSE; whether do the paired comparisions.

pair_compare_filter

default ""; if provided, remove the matched groups.

pair_compare_method

default wilcox.test; wilcox.test, kruskal.test, t.test or anova.

plot_distance_xtype

default NULL; number used to make x axis text generate angle.

Returns

ggplot.

Examples
\donttest{
t1$plot_group_distance(distance_pair_stat = TRUE)
}

Method plot_clustering()

Plotting clustering result. Require ggdendro package.

Usage
trans_beta$plot_clustering(
  use_colors = RColorBrewer::brewer.pal(8, "Dark2"),
  measure = NULL,
  group = NULL,
  replace_name = NULL
)
Arguments
use_colors

colors for presentation.

measure

default NULL; beta diversity index; suggest using the measure when creating object

group

default NULL; if provided, use this group to assign color.

replace_name

default NULL; if provided, use this as label.

Returns

ggplot.

Examples
t1$plot_clustering(group = "Group", replace_name = c("Saline", "Type"))

Method print()

Print the trans_beta object.

Usage
trans_beta$print()

Method clone()

The objects of this class are cloneable with this method.

Usage
trans_beta$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

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## ------------------------------------------------
## Method `trans_beta$new`
## ------------------------------------------------

data(dataset)
t1 <- trans_beta$new(dataset = dataset, ordination = "PCoA", measure = "bray", group = "Group")

## ------------------------------------------------
## Method `trans_beta$plot_ordination`
## ------------------------------------------------

t1$plot_ordination(plot_color = "Group", plot_shape = "Group", plot_group_ellipse = TRUE)

## ------------------------------------------------
## Method `trans_beta$cal_manova`
## ------------------------------------------------

t1$cal_manova(cal_manova_all = TRUE)

## ------------------------------------------------
## Method `trans_beta$cal_group_distance`
## ------------------------------------------------


t1$cal_group_distance(within_group = TRUE)


## ------------------------------------------------
## Method `trans_beta$plot_group_distance`
## ------------------------------------------------


t1$plot_group_distance(distance_pair_stat = TRUE)


## ------------------------------------------------
## Method `trans_beta$plot_clustering`
## ------------------------------------------------

t1$plot_clustering(group = "Group", replace_name = c("Saline", "Type"))

microeco documentation built on May 30, 2021, 5:06 p.m.