Create trans_env object for the analysis of the effects of environmental factors on communities.
This class is a wrapper for a series of operations associated with environmental measurements, including redundancy analysis, mantel test, correlation analysis and linear fitting based on An et al. (2019) <doi:10.1016/j.geoderma.2018.09.035>.
new()
trans_env$new( dataset = NULL, env_cols = NULL, add_data = NULL, character2numeric = TRUE, complete_na = FALSE )
datasetthe object of microtable Class.
env_colsdefault NULL; either numeric vector or character vector to select columns in sample_table of your microtable object. This parameter should be used in the case that all the required environmental data is in sample_table of your microtable object. Otherwise, please use add_data parameter.
add_datadefault NULL; data.frame format; provide the environmental data in the format data.frame; rownames should be sample names. This parameter should be used when the sample_table in your microtable object has no environmental data. Under this circumstance, the env_cols parameter can not be used because no data can be selected.
character2numericdefault TRUE; whether transform the characters or factors to numeric attributes.
complete_nadefault FALSE; Whether fill the NA (missing value) in the environmental data; If TRUE, the function can run the interpolation with the mice package; to use this parameter, please first install mice package.
data_env in trans_env object.
data(dataset) data(env_data_16S) t1 <- trans_env$new(dataset = dataset, add_data = env_data_16S[, 4:11])
cal_diff()
Test the difference of environmental variable across groups.
trans_env$cal_diff(
group = NULL,
method = c("KW", "KW_dunn", "wilcox", "t.test", "anova")[1],
measure = NULL,
p_adjust_method = "fdr",
anova_set = NULL,
...
)groupdefault NULL; a colname of sample_table used to compare values across groups.
methoddefault "KW"; see the following available options:
KW: Kruskal-Wallis Rank Sum Test for all groups (>= 2)
Dunn's Kruskal-Wallis Multiple Comparisons, see dunnTest function in FSA package
Wilcoxon Rank Sum and Signed Rank Tests for all paired groups
Student's t-Test for all paired groups
Duncan's multiple range test for anova
measuredefault NULL; a vector; if null, all variables will be calculated.
p_adjust_methoddefault "fdr"; p.adjust method; see method parameter of p.adjust function for available options.
anova_setdefault NULL; specified group set for anova, such as 'block + N*P*K', see aov.
...parameters passed to kruskal.test or wilcox.test function (method = "KW") or dunnTest function of FSA package (method = "KW_dunn") or agricolae::duncan.test (method = "anova").
res_diff in object. A data.frame generally. A list for anova when anova_set is assigned. In the data frame, 'Group' column means that the group has the maximum median or mean value across the test groups; For non-parametric methods, maximum median value; For t.test, maximum mean value.
\donttest{
t1$cal_diff(group = "Group", method = "KW")
t1$cal_diff(group = "Group", method = "KW_dunn")
t1$cal_diff(group = "Group", method = "anova")
}
cal_autocor()
Calculate the autocorrelations among environmental variables and plot the result.
trans_env$cal_autocor( group = NULL, color_values = RColorBrewer::brewer.pal(8, "Dark2"), alpha = 0.8, ... )
groupdefault NULL; a colname of sample_table; used to perform calculations for different groups.
color_valuesdefault RColorBrewer::brewer.pal(8, "Dark2"); colors palette.
alphadefault 0.8; the alpha value to add transparency in colors; useful when group is not NULL.
...default parameters passed to GGally::ggpairs.
ggmatrix.
\donttest{
t1$cal_autocor(method = "GGally")
}
cal_ordination()
Redundancy analysis (RDA) and Correspondence Analysis (CCA) based on the vegan package.
trans_env$cal_ordination(
method = c("RDA", "dbRDA", "CCA")[1],
feature_sel = FALSE,
taxa_level = NULL,
taxa_filter_thres = NULL,
use_measure = NULL,
add_matrix = NULL,
...
)methoddefault c("RDA", "dbRDA", "CCA")[1]; the ordination method.
feature_seldefault FALSE; whether perform the feature selection based on forward selection method.
taxa_leveldefault NULL; If use RDA or CCA, provide the taxonomic rank, such as "Phylum" or "Genus"; If use otu_table; please input "OTU".
taxa_filter_thresdefault NULL; If want to filter taxa, provide the relative abundance threshold.
use_measuredefault NULL; a name of beta diversity matrix; only useful when parameter method = "dbRDA"; If not provided, use the first beta diversity matrix automatically.
add_matrixdefault NULL; additional distance matrix provided, when the user does not want to use the beta diversity matrix within the dataset; only available when method = "dbRDA".
...paremeters pass to dbrda or rda or cca function according to the input of method.
res_ordination, res_ordination_R2, res_ordination_terms and res_ordination_axis in object.
\donttest{
t1$cal_ordination(method = "dbRDA", use_measure = "bray")
t1$cal_ordination(method = "RDA", taxa_level = "Genus")
t1$cal_ordination(method = "CCA", taxa_level = "Genus")
}
cal_ordination_envsquare()
Fits each environmental vector onto the ordination to obtain the contribution of each variable.
trans_env$cal_ordination_envsquare(...)
...the parameters passing to vegan::envfit function.
res_ordination_envsquare in object.
\donttest{
t1$cal_ordination_envsquare()
}
trans_ordination()
transform ordination result for the following plotting.
trans_env$trans_ordination( show_taxa = 10, adjust_arrow_length = FALSE, min_perc_env = 0.1, max_perc_env = 0.8, min_perc_tax = 0.1, max_perc_tax = 0.8 )
show_taxadefault 10; taxa number shown in the plot.
adjust_arrow_lengthdefault FALSE; whether adjust the arrow length to be clearer.
min_perc_envdefault 0.1; used for scaling up the minimum of env arrow; multiply by the maximum distance between samples and origin.
max_perc_envdefault 0.8; used for scaling up the maximum of env arrow; multiply by the maximum distance between samples and origin.
min_perc_taxdefault 0.1; used for scaling up the minimum of tax arrow; multiply by the maximum distance between samples and origin.
max_perc_taxdefault 0.8; used for scaling up the maximum of tax arrow; multiply by the maximum distance between samples and origin.
res_ordination_trans in object.
\donttest{
t1$trans_ordination(adjust_arrow_length = TRUE, min_perc_env = 0.1, max_perc_env = 1)
}
plot_ordination()
plot ordination result.
trans_env$plot_ordination( plot_color = NULL, plot_shape = NULL, 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), env_text_color = "black", env_arrow_color = "grey30", taxa_text_color = "firebrick1", taxa_arrow_color = "firebrick1", env_text_size = 3.7, taxa_text_size = 3, taxa_text_italic = TRUE, plot_type = "point", point_size = 3, point_alpha = 0.8, centroid_segment_alpha = 0.6, centroid_segment_size = 1, centroid_segment_linetype = 3, ellipse_chull_fill = TRUE, ellipse_chull_alpha = 0.1, ellipse_level = 0.9, ellipse_type = "t", add_sample_label = NULL, env_nudge_x = NULL, env_nudge_y = NULL, taxa_nudge_x = NULL, taxa_nudge_y = NULL, ... )
plot_colordefault NULL; a colname of sample_table to assign colors to different groups in plot.
plot_shapedefault NULL; a colname of sample_table to assign shapes to different groups in plot.
color_valuesdefault RColorBrewer::brewer.pal(8, "Dark2"); color pallete for different groups.
shape_valuesdefault c(16, 17, 7, 8, 15, 18, 11, 10, 12, 13, 9, 3, 4, 0, 1, 2, 14); a vector for point shape types of groups, see ggplot2 tutorial.
env_text_colordefault "black"; environmental variable text color.
env_arrow_colordefault "grey30"; environmental variable arrow color.
taxa_text_colordefault "firebrick1"; taxa text color.
taxa_arrow_colordefault "firebrick1"; taxa arrow color.
env_text_sizedefault 3.7; environmental variable text size.
taxa_text_sizedefault 3; taxa text size.
taxa_text_italicdefault TRUE; "italic"; whether use "italic" style for the taxa text in the plot.
plot_typedefault "point"; one or more elements of "point", "ellipse", "chull" and "centroid".
add point
add confidence ellipse for points of each group
add convex hull for points of each group
add centroid line of each group
point_sizedefault 3; point size in plot when "point" is in plot_type.
point_alphadefault .8; point transparency in plot when "point" is in plot_type.
centroid_segment_alphadefault 0.6; segment transparency in plot when "centroid" is in plot_type.
centroid_segment_sizedefault 1; segment size in plot when "centroid" is in plot_type.
centroid_segment_linetypedefault 3; an integer; the line type related with centroid in plot when "centroid" is in plot_type.
ellipse_chull_filldefault TRUE; whether fill colors to the area of ellipse or chull.
ellipse_chull_alphadefault 0.1; color transparency in the ellipse or convex hull depending on whether "ellipse" or "centroid" is in plot_type.
ellipse_leveldefault .9; confidence level of ellipse when "ellipse" is in plot_type.
ellipse_typedefault "t"; ellipse type when "ellipse" is in plot_type; see type in stat_ellipse.
add_sample_labeldefault NULL; the column name in sample table, if provided, show the point name in plot.
env_nudge_xdefault NULL; numeric vector to adjust the env text x axis position; passed to nudge_x parameter of geom_text_repel function of ggrepel package; default NULL represents automatic adjustment; the length must be same with the row number of object$res_ordination_trans$df_arrows. For example, if there are 5 env variables, env_nudge_x should be something like c(0.1, 0, -0.2, 0, 0). Note that this parameter and env_nudge_y is generally used when the automatic text adjustment is not very well.
env_nudge_ydefault NULL; numeric vector to adjust the env text y axis position; passed to nudge_y parameter of ggrepel::geom_text_repel function; default NULL represents automatic adjustment; the length must be same with the row number of object$res_ordination_trans$df_arrows. For example, if there are 5 env variables, env_nudge_y should be something like c(0.1, 0, -0.2, 0, 0).
taxa_nudge_xdefault NULL; numeric vector to adjust the taxa text x axis position; passed to nudge_x parameter of ggrepel::geom_text_repel function; default NULL represents automatic adjustment; the length must be same with the row number of object$res_ordination_trans$df_arrows_spe. For example, if 3 taxa are shown, taxa_nudge_x should be something like c(0.3, -0.2, 0).
taxa_nudge_ydefault NULL; numeric vector to adjust the taxa text y axis position; passed to nudge_y parameter of ggrepel::geom_text_repel function; default NULL represents automatic adjustment; the length must be same with the row number of object$res_ordination_trans$df_arrows_spe. For example, if 3 taxa are shown, taxa_nudge_y should be something like c(-0.2, 0, 0.4).
...paremeters pass to geom_point for controlling sample points.
ggplot object.
\donttest{
t1$cal_ordination(method = "RDA")
t1$trans_ordination(adjust_arrow_length = TRUE, max_perc_env = 1.5)
t1$plot_ordination(plot_color = "Group")
t1$plot_ordination(plot_color = "Group", plot_shape = "Group", plot_type = c("point", "ellipse"))
t1$plot_ordination(plot_color = "Group", plot_type = c("point", "chull"))
t1$plot_ordination(plot_color = "Group", plot_type = c("point", "centroid"),
centroid_segment_linetype = 1)
t1$plot_ordination(plot_color = "Group", env_nudge_x = c(0.4, 0, 0, 0, 0, -0.2, 0, 0),
env_nudge_y = c(0.6, 0, 0.2, 0.5, 0, 0.1, 0, 0.2))
}
cal_mantel()
Mantel test between beta diversity matrix and environmental data.
trans_env$cal_mantel( select_env_data = NULL, partial_mantel = FALSE, add_matrix = NULL, use_measure = NULL, method = "pearson", p_adjust_method = "fdr", ... )
select_env_datadefault NULL; numeric or character vector to select columns in data_env; if not provided, automatically select the columns with numeric attributes.
partial_manteldefault FALSE; whether use partial mantel test; If TRUE, use other measurements as the zdis.
add_matrixdefault NULL; additional distance matrix provided, if you donot want to use the beta diversity matrix in the dataset.
use_measuredefault NULL; name of beta diversity matrix. If necessary and not provided, use the first beta diversity matrix.
methoddefault "pearson"; one of "pearson", "spearman" and "kendall"; correlation method; see method parameter in mantel function of vegan package.
p_adjust_methoddefault "fdr"; p.adjust method; see method parameter of p.adjust function for available options.
...paremeters pass to mantel of vegan package.
res_mantel in object.
\donttest{
t1$cal_mantel(use_measure = "bray")
t1$cal_mantel(partial_mantel = TRUE, use_measure = "bray")
}
cal_cor()
Calculating the correlations between taxa abundance and environmental variables. Actually, it can also be used for calculating other correlation between any two variables from two tables.
trans_env$cal_cor(
use_data = c("Genus", "all", "other")[1],
select_env_data = NULL,
cor_method = c("pearson", "spearman", "kendall")[1],
p_adjust_method = "fdr",
p_adjust_type = c("Type", "Taxa", "Env")[3],
add_abund_table = NULL,
by_group = NULL,
use_taxa_num = NULL,
other_taxa = NULL,
group_use = NULL,
group_select = NULL,
taxa_name_full = TRUE
)use_datadefault "Genus"; "Genus", "all" or "other"; "Genus" or other taxonomic name: use genus or other taxonomic abundance table in taxa_abund; "all": use all merged taxa abundance table; "other": provide additional taxa name with other_taxa parameter which is necessary.
select_env_datadefault NULL; numeric or character vector to select columns in data_env; if not provided, automatically select the columns with numeric attributes.
cor_methoddefault "pearson"; "pearson", "spearman" or "kendall"; correlation method.
p_adjust_methoddefault "fdr"; p.adjust method; see method parameter of p.adjust function for available options.
p_adjust_typedefault "Env"; "Type", "Taxa" or "Env"; p.adjust type; Env: environmental data; Taxa: taxa data; Type: group used.
add_abund_tabledefault NULL; additional data table to be used. Samples must be rows.
by_groupdefault NULL; one column name or number in sample_table; calculate correlations for different groups separately.
use_taxa_numdefault NULL; integer; a number used to select high abundant taxa; only useful when use_data parameter is a taxonomic level, e.g., "Genus".
other_taxadefault NULL; character vector containing a series of taxa names; used when use_data = "other"; the provided names should be standard full names used to select taxa from all the tables in taxa_abund list of the microtable object; please see the example.
group_usedefault NULL; numeric or character vector to select one column in sample_table for selecting samples; together with group_select.
group_selectdefault NULL; the group name used; remain samples within the group.
taxa_name_fulldefault TRUE; Whether use the complete taxonomic name of taxa.
res_cor in object.
\donttest{
t2 <- trans_diff$new(dataset = dataset, method = "rf", group = "Group", rf_taxa_level = "Genus")
t1 <- trans_env$new(dataset = dataset, add_data = env_data_16S[, 4:11])
t1$cal_cor(use_data = "other", p_adjust_method = "fdr", other_taxa = t2$res_diff$Taxa[1:40])
}
plot_cor()
Plot correlation heatmap.
trans_env$plot_cor(
color_vector = c("#053061", "white", "#A50026"),
color_palette = NULL,
pheatmap = FALSE,
filter_feature = NULL,
ylab_type_italic = FALSE,
keep_full_name = FALSE,
keep_prefix = TRUE,
text_y_order = NULL,
text_x_order = NULL,
font_family = NULL,
cluster_ggplot = "none",
cluster_height_rows = 0.2,
cluster_height_cols = 0.2,
text_y_position = "right",
mylabels_x = NULL,
...
)color_vectordefault c("#053061", "white", "#A50026"); colors with only three values representing low, middle and high value.
color_palettedefault NULL; a customized palette with more color values; if provided, use it instead of color_vector.
pheatmapdefault FALSE; whether use pheatmap package to plot the heatmap.
filter_featuredefault NULL; character vector; used to filter features that only have significance labels in the filter_feature vector. For example, filter_feature = "" can be used to filter features that only have "", no any "*".
ylab_type_italicdefault FALSE; whether use italic type for y lab text.
keep_full_namedefault FALSE; whether use the complete taxonomic name.
keep_prefixdefault TRUE; whether retain the taxonomic prefix.
text_y_orderdefault NULL; character vector; provide customized text order for y axis; shown in the plot from the top down.
text_x_orderdefault NULL; character vector; provide customized text order for x axis.
font_familydefault NULL; font family used in ggplot2; only available when pheatmap = FALSE.
cluster_ggplotdefault "none"; add clustering dendrogram for ggplot2 based heatmap; available options: "none", "row", "col" or "both". "none": no any clustering used; "row": add clustering for rows; "col": add clustering for columns; "both": add clustering for both rows and columns. Only available when pheatmap = FALSE.
cluster_height_rowsdefault 0.2, the dendrogram plot height for rows; available when cluster_ggplot != "none".
cluster_height_colsdefault 0.2, the dendrogram plot height for columns; available cluster_ggplot != "none".
text_y_positiondefault "right"; "left" or "right"; the y axis text position; ggplot2 based heatmap.
mylabels_xdefault NULL; provide x axis text labels additionally; only available when pheatmap = TRUE.
...paremeters pass to ggplot2::geom_tile or pheatmap, depending on the pheatmap = FALSE or TRUE.
plot.
\donttest{
t1$plot_cor(pheatmap = FALSE)
}
plot_scatterfit()
Scatter plot and add fitted line. The most important thing is to make sure that the input x and y have correponding sample orders. If one of x and y is a matrix, the other will be also transformed to matrix with Euclidean distance. Then, both of them are transformed to be vectors. If x or y is a vector with a single value, x or y will be assigned according to the column selection of the data_env inside.
trans_env$plot_scatterfit(
x = NULL,
y = NULL,
group = NULL,
group_order = NULL,
color_values = RColorBrewer::brewer.pal(8, "Dark2"),
shape_values = NULL,
type = c("cor", "lm")[1],
cor_method = "pearson",
label_sep = ";",
label.x.npc = "left",
label.y.npc = "top",
label.x = NULL,
label.y = NULL,
x_axis_title = "",
y_axis_title = "",
point_size = 5,
point_alpha = 0.6,
line_size = 0.8,
line_alpha = 1,
line_color = "black",
line_se = TRUE,
line_se_color = "grey70",
pvalue_trim = 4,
cor_coef_trim = 3,
lm_fir_trim = 2,
lm_sec_trim = 2,
lm_squ_trim = 2,
...
)xdefault NULL; a single numeric or character value or a vector or a distance matrix used for the x axis. If x is a single value, it will be used to select the column of data_env inside. If x is a distance matrix, it will be transformed to be a vector.
ydefault NULL; a single numeric or character value or a vector or a distance matrix used for the y axis. If y is a single value, it will be used to select the column of data_env inside. If y is a distance matrix, it will be transformed to be a vector.
groupdefault NULL; a character vector; if length is 1, must be a colname of dataset$sample_table; Otherwise, group should be a vector with same length of x/y (for vector) or ncol of x/y (for matrix).
group_orderdefault NULL; a vector to order groups, i.e. reorder the legend and colors in plot when group is not NULL; If group_order is NULL and group is provided, the function can first check whether the group column of dataset$sample_table is factor. If provided, overlook the levels in the group of dataset$sample_table.
color_valuesdefault RColorBrewer::brewer.pal(8, "Dark2"); color pallete for different groups.
shape_valuesdefault NULL; a numeric vector for point shape types of groups when group is not NULL, see ggplot2 tutorial.
typedefault c("cor", "lm")[1]; "cor": correlation; "lm" for regression.
cor_methoddefault "pearson"; one of "pearson", "kendall" and "spearman"; correlation method.
label_sepdefault ";"; the separator string between different label parts.
label.x.npcdefault "left"; can be numeric or character vector of the same length as the number of groups and/or panels. If too short they will be recycled.
value should be between 0 and 1. Coordinates to be used for positioning the label, expressed in "normalized parent coordinates"
allowed values include: i) one of c('right', 'left', 'center', 'centre', 'middle') for x-axis; ii) and one of c( 'bottom', 'top', 'center', 'centre', 'middle') for y-axis.
label.y.npcdefault "top"; same usage with label.x.npc; see also label.y.npc parameter of stat_cor of ggpubr package.
label.xdefault NULL; x axis absolute position for adding the statictic label.
label.ydefault NULL; x axis absolute position for adding the statictic label.
x_axis_titledefault ""; the title of x axis.
y_axis_titledefault ""; the title of y axis.
point_sizedefault 5; point size value.
point_alphadefault 0.6; alpha value for the point color transparency.
line_sizedefault 0.8; line size value.
line_alphadefault 1; alpha value for the line color transparency.
line_colordefault "black"; fitted line color only useful when group = NULL.
line_sedefault TRUE; Whether show the confidence interval for the fitting.
line_se_colordefault "grey70"; the color to fill the confidence interval when line_se = TRUE.
pvalue_trimdefault 4; trim the decimal places of p value.
cor_coef_trimdefault 3; trim the decimal places of correlation coefficient.
lm_fir_trimdefault 2; trim the decimal places of regression first coefficient.
lm_sec_trimdefault 2; trim the decimal places of regression second coefficient.
lm_squ_trimdefault 2; trim the decimal places of regression R square.
...other arguments to pass to geom_text or geom_label.
plot.
\donttest{
t1$plot_scatterfit(x = 1, y = 2, type = "cor")
t1$plot_scatterfit(x = 1, y = 2, type = "lm", point_alpha = .3)
t1$plot_scatterfit(x = "pH", y = "TOC", type = "lm", group = "Group", line_se = FALSE)
t1$plot_scatterfit(x =
dataset$beta_diversity$bray[rownames(t1$data_env), rownames(t1$data_env)], y = "pH")
}
print()
Print the trans_env object.
trans_env$print()
clone()
The objects of this class are cloneable with this method.
trans_env$clone(deep = FALSE)
deepWhether to make a deep clone.
## ------------------------------------------------
## Method `trans_env$new`
## ------------------------------------------------
data(dataset)
data(env_data_16S)
t1 <- trans_env$new(dataset = dataset, add_data = env_data_16S[, 4:11])
## ------------------------------------------------
## Method `trans_env$cal_diff`
## ------------------------------------------------
t1$cal_diff(group = "Group", method = "KW")
t1$cal_diff(group = "Group", method = "KW_dunn")
t1$cal_diff(group = "Group", method = "anova")
## ------------------------------------------------
## Method `trans_env$cal_autocor`
## ------------------------------------------------
t1$cal_autocor(method = "GGally")
## ------------------------------------------------
## Method `trans_env$cal_ordination`
## ------------------------------------------------
t1$cal_ordination(method = "dbRDA", use_measure = "bray")
t1$cal_ordination(method = "RDA", taxa_level = "Genus")
t1$cal_ordination(method = "CCA", taxa_level = "Genus")
## ------------------------------------------------
## Method `trans_env$cal_ordination_envsquare`
## ------------------------------------------------
t1$cal_ordination_envsquare()
## ------------------------------------------------
## Method `trans_env$trans_ordination`
## ------------------------------------------------
t1$trans_ordination(adjust_arrow_length = TRUE, min_perc_env = 0.1, max_perc_env = 1)
## ------------------------------------------------
## Method `trans_env$plot_ordination`
## ------------------------------------------------
t1$cal_ordination(method = "RDA")
t1$trans_ordination(adjust_arrow_length = TRUE, max_perc_env = 1.5)
t1$plot_ordination(plot_color = "Group")
t1$plot_ordination(plot_color = "Group", plot_shape = "Group", plot_type = c("point", "ellipse"))
t1$plot_ordination(plot_color = "Group", plot_type = c("point", "chull"))
t1$plot_ordination(plot_color = "Group", plot_type = c("point", "centroid"),
centroid_segment_linetype = 1)
t1$plot_ordination(plot_color = "Group", env_nudge_x = c(0.4, 0, 0, 0, 0, -0.2, 0, 0),
env_nudge_y = c(0.6, 0, 0.2, 0.5, 0, 0.1, 0, 0.2))
## ------------------------------------------------
## Method `trans_env$cal_mantel`
## ------------------------------------------------
t1$cal_mantel(use_measure = "bray")
t1$cal_mantel(partial_mantel = TRUE, use_measure = "bray")
## ------------------------------------------------
## Method `trans_env$cal_cor`
## ------------------------------------------------
t2 <- trans_diff$new(dataset = dataset, method = "rf", group = "Group", rf_taxa_level = "Genus")
t1 <- trans_env$new(dataset = dataset, add_data = env_data_16S[, 4:11])
t1$cal_cor(use_data = "other", p_adjust_method = "fdr", other_taxa = t2$res_diff$Taxa[1:40])
## ------------------------------------------------
## Method `trans_env$plot_cor`
## ------------------------------------------------
t1$plot_cor(pheatmap = FALSE)
## ------------------------------------------------
## Method `trans_env$plot_scatterfit`
## ------------------------------------------------
t1$plot_scatterfit(x = 1, y = 2, type = "cor")
t1$plot_scatterfit(x = 1, y = 2, type = "lm", point_alpha = .3)
t1$plot_scatterfit(x = "pH", y = "TOC", type = "lm", group = "Group", line_se = FALSE)
t1$plot_scatterfit(x =
dataset$beta_diversity$bray[rownames(t1$data_env), rownames(t1$data_env)], y = "pH")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.