trans_beta {microeco} | R Documentation |
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>.
new()
trans_beta$new( dataset = NULL, ordination = NULL, measure = NULL, group = NULL, trans_otu = FALSE, ncomp = 3, scale_species = FALSE )
dataset
the object of microtable
Class.
ordination
default NULL; PCA, PCoA or NMDS.
measure
default NULL; bray, jaccard, wei_unifrac or unwei_unifrac, or other name of matrix you add; beta diversity index used for ordination, manova or group distance.
group
default NULL; sample group used for manova or group distance.
trans_otu
default FALSE; whether species abundance will be square transformed, used for PCA.
ncomp
default 3; the returned dimensions.
scale_species
default FALSE; whether species loading in PCA will be scaled.
res_ordination stored in the object.
data(dataset) t1 <- trans_beta$new(dataset = dataset, ordination = "PCoA", measure = "bray", group = "Group")
plot_ordination()
Plotting the ordination result based on An et al. (2019) <doi:10.1016/j.geoderma.2018.09.035>.
trans_beta$plot_ordination( color_values = RColorBrewer::brewer.pal(8, "Dark2"), shape_values = c(16, 17, 7, 8, 15, 18, 11, 10, 12, 13, 9, 3, 4, 0, 1, 2, 14), plot_color = NULL, plot_shape = NULL, plot_group_order = NULL, plot_point_size = 3, plot_point_alpha = 0.9, plot_sample_label = NULL, plot_group_centroid = FALSE, plot_group = NULL, segment_alpha = 0.6, centroid_linetype = 3, plot_group_ellipse = FALSE, ellipse_level = 0.9, ellipse_alpha = 0.1, ellipse_type = "t" )
color_values
default RColorBrewer::brewer.pal(8, "Dark2"); colors for presentation.
shape_values
default c(16, 17, 7, 8, 15, 18, 11, 10, 12, 13, 9, 3, 4, 0, 1, 2, 14); a vector used in the shape type, see ggplot2 tutorial.
plot_color
default NULL; the sample group name used for color in plot.
plot_shape
default NULL; the sample group name used for shape in plot.
plot_group_order
default NULL; a vector used to order the groups in the legend of plot.
plot_point_size
default 3; point size in plot.
plot_point_alpha
default .9; point transparency in plot.
plot_sample_label
default NULL; the column name in sample table, if provided, show the point name in plot.
plot_group_centroid
default FALSE; whether show the centroid in each group of plot.
plot_group
default NULL; the column name in sample table, generally used with plot_group_centroid and plot_group_ellipse.
segment_alpha
default .6; segment transparency in plot.
centroid_linetype
default 3; the line type related with centroid in plot.
plot_group_ellipse
default FALSE; whether show the confidence ellipse in each group of plot.
ellipse_level
default .9; confidence level of ellipse.
ellipse_alpha
default .1; color transparency in the ellipse.
ellipse_type
default t; see type in stat_ellipse
.
ggplot.
t1$plot_ordination(plot_color = "Group", plot_shape = "Group", plot_group_ellipse = TRUE)
cal_manova()
Calculate perMANOVA based on Anderson al. (2008) <doi:10.1111/j.1442-9993.2001.01070.pp.x> and R vegan adonis function.
trans_beta$cal_manova( cal_manova_all = FALSE, cal_manova_paired = FALSE, cal_manova_set = NULL, permutations = 999 )
res_manova stored in object.
t1$cal_manova(cal_manova_all = TRUE)
cal_group_distance()
Transform sample distances within groups or between groups.
trans_beta$cal_group_distance(within_group = TRUE)
within_group
default TRUE; whether transform sample distance within groups, if FALSE, transform sample distance between any two groups.
res_group_distance stored in object.
t1$cal_group_distance(within_group = TRUE)
plot_group_distance()
Plotting the distance between samples within or between groups.
trans_beta$plot_group_distance( plot_group_order = NULL, color_values = RColorBrewer::brewer.pal(8, "Dark2"), distance_pair_stat = FALSE, pair_compare_filter = "", pair_compare_method = "wilcox.test", plot_distance_xtype = NULL )
plot_group_order
default NULL; a vector used to order the groups in the plot.
color_values
colors for presentation.
distance_pair_stat
default FALSE; whether do the paired comparisions.
pair_compare_filter
default ""; if provided, remove the matched groups.
pair_compare_method
default wilcox.test; wilcox.test, kruskal.test, t.test or anova.
plot_distance_xtype
default NULL; number used to make x axis text generate angle.
ggplot.
t1$plot_group_distance(distance_pair_stat = TRUE)
plot_clustering()
Plotting clustering result. Require ggdendro package.
trans_beta$plot_clustering( use_colors = RColorBrewer::brewer.pal(8, "Dark2"), measure = NULL, group = NULL, replace_name = NULL )
use_colors
colors for presentation.
measure
default NULL; beta diversity index; suggest using the measure when creating object
group
default NULL; if provided, use this group to assign color.
replace_name
default NULL; if provided, use this as label.
ggplot.
t1$plot_clustering(group = "Group", replace_name = c("Saline", "Type"))
print()
Print the trans_beta object.
trans_beta$print()
clone()
The objects of this class are cloneable with this method.
trans_beta$clone(deep = FALSE)
deep
Whether to make a deep clone.
## ------------------------------------------------ ## 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"))