trans_alpha: Create 'trans_alpha' object for alpha diversity statistics...
In microeco: Microbial Community Ecology Data Analysis
trans_alpha R Documentation
Create trans_alpha object for alpha diversity statistics and plot.
Description
This class is a wrapper for a series of alpha diversity analysis, including the statistics and visualization.
Methods
Public methods
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Method new()
Usage
trans_alpha$new(
dataset = NULL,
group = NULL,
by_group = NULL,
by_ID = NULL,
order_x = NULL
)
Arguments
datasetthe object of microtable class.
groupdefault NULL; a column of sample_table used for the statistics; If provided, can return data_stat.
by_groupdefault NULL; a column of sample_table used to perform the differential test
among groups (group parameter) for each group (by_group parameter). So by_group has a higher level than group parameter.
by_IDdefault NULL; a column of sample_table used to perform paired t test or paired wilcox test for the paired data,
such as the data of plant compartments for different plant species (ID).
So by_ID in sample_table should be the smallest unit of sample collection without any repetition in it.
order_xdefault NULL; a sample_table column name or a vector with sample names; if provided, order samples by using factor.
Returns
data_alpha and data_stat stored in the object.
Examples
\donttest{
data(dataset)
t1 <- trans_alpha$new(dataset = dataset, group = "Group")
}
Method cal_diff()
Differential test on alpha diversity.
Usage
trans_alpha$cal_diff(
method = c("KW", "KW_dunn", "wilcox", "t.test", "anova", "scheirerRayHare", "lme",
"betareg", "glmm")[1],
measure = NULL,
p_adjust_method = "fdr",
formula = NULL,
KW_dunn_letter = TRUE,
alpha = 0.05,
anova_post_test = "duncan.test",
return_model = FALSE,
...
)
Arguments
methoddefault "KW"; see the following available options:
- 'KW'
Kruskal-Wallis Rank Sum Test for all groups (>= 2)
- 'KW_dunn'
Dunn's Kruskal-Wallis Multiple Comparisons <10.1080/00401706.1964.10490181> based on dunnTest function in FSA package
- 'wilcox'
Wilcoxon Rank Sum Test for all paired groups
- 't.test'
Student's t-Test for all paired groups
- 'anova'
Variance analysis. For one-way anova, the post hoc test is Duncan's new multiple range test
based on duncan.test function of agricolae package. Please use anova_post_test parameter to select other post hoc method.
For multi-way anova, Please use formula parameter to specify the model and see aov for more details
- 'scheirerRayHare'
Scheirer Ray Hare test (nonparametric test) for a two-way factorial experiment;
see scheirerRayHare function of rcompanion package
- 'lme'
Linear Mixed Effect Model based on the lmerTest package
- 'betareg'
Beta Regression for Rates and Proportions based on the betareg package
- 'glmm'
Generalized linear mixed model (GLMM) based on the glmmTMB package
measuredefault NULL; character vector; If NULL, all indexes will be calculated; see names of microtable$alpha_diversity,
e.g. c("Observed", "Chao1", "Shannon").
p_adjust_methoddefault "fdr" (for "KW", "wilcox", "t.test") or "holm" (for "KW_dunn"); P value adjustment method;
For method = 'KW', 'wilcox' or 't.test', please see method parameter of p.adjust function for available options;
For method = 'KW_dunn', please see dunn.test::p.adjustment.methods for available options.
formuladefault NULL; applied to two-way or multi-factor anova when
method = "anova" or "scheirerRayHare" or "lme" or "betareg" or "glmm";
specified set for independent variables, i.e. the latter part of a general formula,
such as 'block + N*P*K'.
KW_dunn_letterdefault TRUE; For method = 'KW_dunn', TRUE denotes paired significances are presented by letters;
FALSE means significances are shown by asterisk for paired comparison.
alphadefault 0.05; Significant level; used for generating significance letters when method is 'anova' or 'KW_dunn'.
anova_post_testdefault "duncan.test". The post hoc test method for one-way anova. Other available options include "LSD.test" and "HSD.test".
All those are the function names in agricolae package.
return_modeldefault FALSE; whether return the original lmer or glmm model list in the object.
...parameters passed to kruskal.test (when method = "KW") or wilcox.test function (when method = "wilcox") or
dunnTest function of FSA package (when method = "KW_dunn") or
agricolae::duncan.test/agricolae::LSD.test/agricolae::HSD.test (when method = "anova", one-way anova) or
rcompanion::scheirerRayHare (when method = "scheirerRayHare") or
lmerTest::lmer (when method = "lme") or
betareg::betareg (when method = "betareg") or
glmmTMB::glmmTMB (when method = "glmm").
Returns
res_diff, stored in object with the format data.frame.
Examples
\donttest{
t1$cal_diff(method = "KW")
t1$cal_diff(method = "anova")
t1 <- trans_alpha$new(dataset = dataset, group = "Type", by_group = "Group")
t1$cal_diff(method = "anova")
}
Method plot_alpha()
Plot the alpha diversity.
Usage
trans_alpha$plot_alpha(
color_values = RColorBrewer::brewer.pal(8, "Dark2"),
measure = "Shannon",
group = NULL,
add_sig = TRUE,
add_sig_label = "Significance",
add_sig_text_size = 3.88,
add_sig_label_num_dec = 4,
boxplot_add = "jitter",
order_x_mean = FALSE,
y_start = 0.1,
y_increase = 0.05,
xtext_angle = NULL,
xtext_size = 15,
ytitle_size = 17,
barwidth = 0.9,
use_boxplot = TRUE,
plot_SE = TRUE,
errorbar_size = 1,
errorbar_width = 0.2,
point_size = 3,
point_alpha = 0.8,
add_line = FALSE,
line_size = 0.8,
line_type = 1,
line_color = "grey50",
line_alpha = 0.5,
...
)
Arguments
color_valuesdefault RColorBrewer::brewer.pal(8, "Dark2"); color pallete for groups.
measuredefault "Shannon"; one alpha diversity index in the object.
groupdefault NULL; group name used for the plot.
add_sigdefault TRUE; wheter add significance label using the result of cal_diff function, i.e. object$res_diff;
This is manily designed to add post hoc test of anova or other significances to make the label mapping easy.
add_sig_labeldefault "Significance"; select a colname of object$res_diff for the label text when 'Letter' is not in the table,
such as 'P.adj' or 'Significance'.
add_sig_text_sizedefault 3.88; the size of text in added label.
add_sig_label_num_decdefault 4; reserved decimal places when the parameter add_sig_label use numeric column, like 'P.adj'.
boxplot_adddefault "jitter"; points type, see the add parameter in ggpubr::ggboxplot.
order_x_meandefault FALSE; whether order x axis by the means of groups from large to small.
y_startdefault 0.1; the y axis value from which to add the significance asterisk label;
the default 0.1 means max(values) + 0.1 * (max(values) - min(values)).
y_increasedefault 0.05; the increasing y axia space to add the label (asterisk or letter); the default 0.05 means 0.05 * (max(values) - min(values));
this parameter is also used to label the letters of anova result with the fixed space.
xtext_angledefault NULL; number (e.g. 30) used to make x axis text generate angle.
xtext_sizedefault 15; x axis text size.
ytitle_sizedefault 17; y axis title size.
barwidthdefault 0.9; the bar width in plot; applied when by_group is not NULL.
use_boxplotdefault TRUE; TRUE denotes boxplot by using the data_alpha table in the object.
FALSE represents mean-sd or mean-se plot by invoking the data_stat table in the object.
plot_SEdefault TRUE; TRUE: the errorbar is mean±se; FALSE: the errorbar is mean±sd.
errorbar_sizedefault 1; errorbar size. Available when use_boxplot = FALSE.
errorbar_widthdefault 0.2; errorbar width. Available when use_boxplot = FALSE and by_group is NULL.
point_sizedefault 3; point size for taxa. Available when use_boxplot = FALSE.
point_alphadefault 0.8; point transparency. Available when use_boxplot = FALSE.
add_linedefault FALSE; whether add line. Available when use_boxplot = FALSE.
line_sizedefault 0.8; line size when add_line = TRUE. Available when use_boxplot = FALSE.
line_typedefault 1; an integer; line type when add_line = TRUE. Available when use_boxplot = FALSE.
line_colordefault "grey50"; line color when add_line = TRUE. Available when use_boxplot = FALSE and by_group is NULL.
line_alphadefault 0.5; line transparency when add_line = TRUE. Available when use_boxplot = FALSE.
...parameters pass to ggpubr::ggboxplot function.
Returns
ggplot.
Examples
\donttest{
t1 <- trans_alpha$new(dataset = dataset, group = "Group")
t1$cal_diff(method = "wilcox")
t1$plot_alpha(measure = "Shannon", add_sig = TRUE)
t1 <- trans_alpha$new(dataset = dataset, group = "Type", by_group = "Group")
t1$cal_diff(method = "wilcox")
t1$plot_alpha(measure = "Shannon", add_sig = TRUE)
}
Method print()
Print the trans_alpha object.
Usage
trans_alpha$print()
Method clone()
The objects of this class are cloneable with this method.
Usage
trans_alpha$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## ------------------------------------------------
## Method `trans_alpha$new`
## ------------------------------------------------
data(dataset)
t1 <- trans_alpha$new(dataset = dataset, group = "Group")
## ------------------------------------------------
## Method `trans_alpha$cal_diff`
## ------------------------------------------------
t1$cal_diff(method = "KW")
t1$cal_diff(method = "anova")
t1 <- trans_alpha$new(dataset = dataset, group = "Type", by_group = "Group")
t1$cal_diff(method = "anova")
## ------------------------------------------------
## Method `trans_alpha$plot_alpha`
## ------------------------------------------------
t1 <- trans_alpha$new(dataset = dataset, group = "Group")
t1$cal_diff(method = "wilcox")
t1$plot_alpha(measure = "Shannon", add_sig = TRUE)
t1 <- trans_alpha$new(dataset = dataset, group = "Type", by_group = "Group")
t1$cal_diff(method = "wilcox")
t1$plot_alpha(measure = "Shannon", add_sig = TRUE)
microeco documentation built on Nov. 18, 2023, 9:06 a.m.
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| trans_alpha | R Documentation |
Create trans_alpha object for alpha diversity statistics and plot.
Description
This class is a wrapper for a series of alpha diversity analysis, including the statistics and visualization.
Methods
Public methods
Method new()
Usage
trans_alpha$new( dataset = NULL, group = NULL, by_group = NULL, by_ID = NULL, order_x = NULL )
Arguments
datasetthe object of
microtableclass.groupdefault NULL; a column of
sample_tableused for the statistics; If provided, can returndata_stat.by_groupdefault NULL; a column of
sample_tableused to perform the differential test among groups (groupparameter) for each group (by_groupparameter). Soby_grouphas a higher level thangroupparameter.by_IDdefault NULL; a column of
sample_tableused to perform paired t test or paired wilcox test for the paired data, such as the data of plant compartments for different plant species (ID). Soby_IDin sample_table should be the smallest unit of sample collection without any repetition in it.order_xdefault NULL; a
sample_tablecolumn name or a vector with sample names; if provided, order samples by usingfactor.
Returns
data_alpha and data_stat stored in the object.
Examples
\donttest{
data(dataset)
t1 <- trans_alpha$new(dataset = dataset, group = "Group")
}
Method cal_diff()
Differential test on alpha diversity.
Usage
trans_alpha$cal_diff(
method = c("KW", "KW_dunn", "wilcox", "t.test", "anova", "scheirerRayHare", "lme",
"betareg", "glmm")[1],
measure = NULL,
p_adjust_method = "fdr",
formula = NULL,
KW_dunn_letter = TRUE,
alpha = 0.05,
anova_post_test = "duncan.test",
return_model = FALSE,
...
)Arguments
methoddefault "KW"; see the following available options:
- 'KW'
Kruskal-Wallis Rank Sum Test for all groups (>= 2)
- 'KW_dunn'
Dunn's Kruskal-Wallis Multiple Comparisons <10.1080/00401706.1964.10490181> based on
dunnTestfunction inFSApackage- 'wilcox'
Wilcoxon Rank Sum Test for all paired groups
- 't.test'
Student's t-Test for all paired groups
- 'anova'
Variance analysis. For one-way anova, the post hoc test is Duncan's new multiple range test based on
duncan.testfunction ofagricolaepackage. Please useanova_post_testparameter to select other post hoc method. For multi-way anova, Please useformulaparameter to specify the model and seeaovfor more details- 'scheirerRayHare'
Scheirer Ray Hare test (nonparametric test) for a two-way factorial experiment; see
scheirerRayHarefunction ofrcompanionpackage- 'lme'
Linear Mixed Effect Model based on the
lmerTestpackage- 'betareg'
Beta Regression for Rates and Proportions based on the
betaregpackage- 'glmm'
Generalized linear mixed model (GLMM) based on the
glmmTMBpackage
measuredefault NULL; character vector; If NULL, all indexes will be calculated; see names of
microtable$alpha_diversity, e.g. c("Observed", "Chao1", "Shannon").p_adjust_methoddefault "fdr" (for "KW", "wilcox", "t.test") or "holm" (for "KW_dunn"); P value adjustment method; For
method = 'KW', 'wilcox' or 't.test', please see method parameter ofp.adjustfunction for available options; Formethod = 'KW_dunn', please seedunn.test::p.adjustment.methodsfor available options.formuladefault NULL; applied to two-way or multi-factor anova when method =
"anova"or"scheirerRayHare"or"lme"or"betareg"or"glmm"; specified set for independent variables, i.e. the latter part of a general formula, such as'block + N*P*K'.KW_dunn_letterdefault TRUE; For
method = 'KW_dunn',TRUEdenotes paired significances are presented by letters;FALSEmeans significances are shown by asterisk for paired comparison.alphadefault 0.05; Significant level; used for generating significance letters when method is 'anova' or 'KW_dunn'.
anova_post_testdefault "duncan.test". The post hoc test method for one-way anova. Other available options include "LSD.test" and "HSD.test". All those are the function names in
agricolaepackage.return_modeldefault FALSE; whether return the original lmer or glmm model list in the object.
...parameters passed to
kruskal.test(whenmethod = "KW") orwilcox.testfunction (whenmethod = "wilcox") ordunnTestfunction ofFSApackage (whenmethod = "KW_dunn") oragricolae::duncan.test/agricolae::LSD.test/agricolae::HSD.test(whenmethod = "anova", one-way anova) orrcompanion::scheirerRayHare(whenmethod = "scheirerRayHare") orlmerTest::lmer(whenmethod = "lme") orbetareg::betareg(whenmethod = "betareg") orglmmTMB::glmmTMB(whenmethod = "glmm").
Returns
res_diff, stored in object with the format data.frame.
Examples
\donttest{
t1$cal_diff(method = "KW")
t1$cal_diff(method = "anova")
t1 <- trans_alpha$new(dataset = dataset, group = "Type", by_group = "Group")
t1$cal_diff(method = "anova")
}
Method plot_alpha()
Plot the alpha diversity.
Usage
trans_alpha$plot_alpha( color_values = RColorBrewer::brewer.pal(8, "Dark2"), measure = "Shannon", group = NULL, add_sig = TRUE, add_sig_label = "Significance", add_sig_text_size = 3.88, add_sig_label_num_dec = 4, boxplot_add = "jitter", order_x_mean = FALSE, y_start = 0.1, y_increase = 0.05, xtext_angle = NULL, xtext_size = 15, ytitle_size = 17, barwidth = 0.9, use_boxplot = TRUE, plot_SE = TRUE, errorbar_size = 1, errorbar_width = 0.2, point_size = 3, point_alpha = 0.8, add_line = FALSE, line_size = 0.8, line_type = 1, line_color = "grey50", line_alpha = 0.5, ... )
Arguments
color_valuesdefault
RColorBrewer::brewer.pal(8, "Dark2"); color pallete for groups.measuredefault "Shannon"; one alpha diversity index in the object.
groupdefault NULL; group name used for the plot.
add_sigdefault TRUE; wheter add significance label using the result of
cal_difffunction, i.e.object$res_diff; This is manily designed to add post hoc test of anova or other significances to make the label mapping easy.add_sig_labeldefault "Significance"; select a colname of
object$res_difffor the label text when 'Letter' is not in the table, such as 'P.adj' or 'Significance'.add_sig_text_sizedefault 3.88; the size of text in added label.
add_sig_label_num_decdefault 4; reserved decimal places when the parameter
add_sig_labeluse numeric column, like 'P.adj'.boxplot_adddefault "jitter"; points type, see the add parameter in
ggpubr::ggboxplot.order_x_meandefault FALSE; whether order x axis by the means of groups from large to small.
y_startdefault 0.1; the y axis value from which to add the significance asterisk label; the default 0.1 means
max(values) + 0.1 * (max(values) - min(values)).y_increasedefault 0.05; the increasing y axia space to add the label (asterisk or letter); the default 0.05 means
0.05 * (max(values) - min(values)); this parameter is also used to label the letters of anova result with the fixed space.xtext_angledefault NULL; number (e.g. 30) used to make x axis text generate angle.
xtext_sizedefault 15; x axis text size.
ytitle_sizedefault 17; y axis title size.
barwidthdefault 0.9; the bar width in plot; applied when by_group is not NULL.
use_boxplotdefault TRUE; TRUE denotes boxplot by using the data_alpha table in the object. FALSE represents mean-sd or mean-se plot by invoking the data_stat table in the object.
plot_SEdefault TRUE; TRUE: the errorbar is
mean±se; FALSE: the errorbar ismean±sd.errorbar_sizedefault 1; errorbar size. Available when
use_boxplot = FALSE.errorbar_widthdefault 0.2; errorbar width. Available when
use_boxplot = FALSEandby_groupis NULL.point_sizedefault 3; point size for taxa. Available when
use_boxplot = FALSE.point_alphadefault 0.8; point transparency. Available when
use_boxplot = FALSE.add_linedefault FALSE; whether add line. Available when
use_boxplot = FALSE.line_sizedefault 0.8; line size when
add_line = TRUE. Available whenuse_boxplot = FALSE.line_typedefault 1; an integer; line type when
add_line = TRUE. Available whenuse_boxplot = FALSE.line_colordefault "grey50"; line color when
add_line = TRUE. Available whenuse_boxplot = FALSEandby_groupis NULL.line_alphadefault 0.5; line transparency when
add_line = TRUE. Available whenuse_boxplot = FALSE....parameters pass to
ggpubr::ggboxplotfunction.
Returns
ggplot.
Examples
\donttest{
t1 <- trans_alpha$new(dataset = dataset, group = "Group")
t1$cal_diff(method = "wilcox")
t1$plot_alpha(measure = "Shannon", add_sig = TRUE)
t1 <- trans_alpha$new(dataset = dataset, group = "Type", by_group = "Group")
t1$cal_diff(method = "wilcox")
t1$plot_alpha(measure = "Shannon", add_sig = TRUE)
}
Method print()
Print the trans_alpha object.
Usage
trans_alpha$print()
Method clone()
The objects of this class are cloneable with this method.
Usage
trans_alpha$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## ------------------------------------------------
## Method `trans_alpha$new`
## ------------------------------------------------
data(dataset)
t1 <- trans_alpha$new(dataset = dataset, group = "Group")
## ------------------------------------------------
## Method `trans_alpha$cal_diff`
## ------------------------------------------------
t1$cal_diff(method = "KW")
t1$cal_diff(method = "anova")
t1 <- trans_alpha$new(dataset = dataset, group = "Type", by_group = "Group")
t1$cal_diff(method = "anova")
## ------------------------------------------------
## Method `trans_alpha$plot_alpha`
## ------------------------------------------------
t1 <- trans_alpha$new(dataset = dataset, group = "Group")
t1$cal_diff(method = "wilcox")
t1$plot_alpha(measure = "Shannon", add_sig = TRUE)
t1 <- trans_alpha$new(dataset = dataset, group = "Type", by_group = "Group")
t1$cal_diff(method = "wilcox")
t1$plot_alpha(measure = "Shannon", add_sig = TRUE)
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