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 visualization.
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$new(
dataset = NULL,
group = NULL,
by_group = NULL,
by_ID = NULL,
order_x = NULL
)
Arguments
datasetmicrotable object.
groupdefault NULL; a column name of sample_table in the input microtable object used for the statistics across groups.
by_groupdefault NULL; a column name of sample_table used to perform the differential test
among groups (from group parameter) for each group (from by_group parameter) separately.
by_IDdefault NULL; a column name of sample_table used to perform paired t test or paired wilcox test for the paired data,
such as the data of plant compartments for different plant species (ID).
So by_ID in sample_table should be the smallest unit of sample collection without any repetition in it.
order_xdefault NULL; a column name of sample_table or a vector with sample names. If provided, sort samples using factor.
Returns
data_alpha and data_stat stored in the object.
Examples
\donttest{
data(dataset)
t1 <- trans_alpha$new(dataset = dataset, group = "Group")
}
Method cal_diff()
Differential test on alpha diversity.
Usage
trans_alpha$cal_diff(
measure = NULL,
method = c("KW", "KW_dunn", "wilcox", "t.test", "anova", "scheirerRayHare", "lm",
"lme", "betareg", "glmm", "glmm_beta")[1],
formula = NULL,
p_adjust_method = "fdr",
KW_dunn_letter = TRUE,
alpha = 0.05,
anova_post_test = "duncan.test",
return_model = FALSE,
...
)
Arguments
measuredefault NULL; character vector; If NULL, all indexes will be used; see names of microtable$alpha_diversity,
e.g. c("Observed", "Chao1", "Shannon").
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 default post hoc test is Duncan's new multiple range test.
Please use anova_post_test parameter to change the 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
- 'lm'
Linear Model based on the lm function
- '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.
A family function can be provided using parameter passing, such as: family = glmmTMB::lognormal(link = "log")
- 'glmm_beta'
Generalized linear mixed model (GLMM) with a family function of beta distribution.
This is an extension of the GLMM model in 'glmm' option.
The only difference is in glmm_beta the family function is fixed with the beta distribution function,
facilitating the fitting for proportional data (ranging from 0 to 1). The link function is fixed with "logit".
formuladefault NULL; applied to two-way or multi-factor analysis when
method is "anova", "scheirerRayHare", "lm", "lme", "betareg" or "glmm";
specified set for independent variables, i.e. the latter part of a general formula,
such as 'block + N*P*K'.
p_adjust_methoddefault "fdr" (for "KW", "wilcox", "t.test" methods) 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.
KW_dunn_letterdefault TRUE; For method = 'KW_dunn', TRUE denotes 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.
The default option represents the Duncan's new multiple range test.
Other available options include "LSD.test" (LSD post hoc test) and "HSD.test" (HSD post hoc test).
All those are the function names from agricolae package.
return_modeldefault FALSE; whether return the original "lm", "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
stats::lm (when method = "lm") 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.
When method is "betareg", "lm", "lme" or "glmm",
"Estimate" and "Std.Error" columns represent the fitted coefficient and its standard error, respectively.
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.
Box plot (and others for visualizing data in groups of single factor) is used for the visualization of alpha diversity when the group is found in the object.
When the formula is found in the res_diff table in the object,
heatmap is employed automatically to show the significances of differential test for multiple indexes,
and errorbar (coefficient and standard errors) can be used for single index.
Usage
trans_alpha$plot_alpha(
plot_type = "ggboxplot",
color_values = RColorBrewer::brewer.pal(8, "Dark2"),
measure = "Shannon",
group = NULL,
add = NULL,
add_sig = TRUE,
add_sig_label = "Significance",
add_sig_text_size = 3.88,
add_sig_label_num_dec = 4,
order_x_mean = FALSE,
y_start = 0.1,
y_increase = 0.05,
xtext_angle = 30,
xtext_size = 13,
ytitle_size = 17,
bar_width = 0.9,
bar_alpha = 0.8,
dodge_width = 0.9,
plot_SE = TRUE,
errorbar_size = 1,
errorbar_width = 0.2,
errorbar_addpoint = TRUE,
errorbar_color_black = FALSE,
point_size = 3,
point_alpha = 0.8,
add_line = FALSE,
line_size = 0.8,
line_type = 2,
line_color = "grey50",
line_alpha = 0.5,
heatmap_cell = "P.unadj",
heatmap_sig = "Significance",
heatmap_x = "Factors",
heatmap_y = "Measure",
heatmap_lab_fill = "P value",
coefplot_sig_pos = 2,
...
)
Arguments
plot_typedefault "ggboxplot"; plot type; available options include "ggboxplot", "ggdotplot", "ggviolin",
"ggstripchart", "ggerrorplot", "errorbar" and "barerrorbar".
The options starting with "gg" are function names coming from ggpubr package.
All those methods with ggpubr package use the data_alpha table in the object.
"errorbar" represents Mean±SD or Mean±SE plot based on ggplot2 package by invoking the data_stat table in the object.
"barerrorbar" denotes "bar plot + error bar". It is similar with "errorbar" and has a bar plot.
color_valuesdefault RColorBrewer::brewer.pal(8, "Dark2"); color pallete for groups.
measuredefault "Shannon"; one alpha diversity index in the object.
groupdefault NULL; group name used for the plot.
adddefault NULL; add another plot element; passed to the add parameter of the function (e.g., ggboxplot) from ggpubr package
when plot_type starts with "gg" (functions coming from ggpubr package).
add_sigdefault TRUE; wheter add significance label using the result of cal_diff function, i.e. object$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'.
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 30; number (e.g. 30). Angle of text in x axis.
xtext_sizedefault 13; x axis text size. NULL means the default size in ggplot2.
ytitle_sizedefault 17; y axis title size.
bar_widthdefault 0.9; the bar width when plot_type = "barerrorbar".
bar_alphadefault 0.8; the alpha of bar color when plot_type = "barerrorbar".
dodge_widthdefault 0.9; the dodge width used in position_dodge function of ggplot2 package when plot_type is "errorbar" or "barerrorbar".
plot_SEdefault TRUE; TRUE: the errorbar is mean±se; FALSE: the errorbar is mean±sd. Available when plot_type is "errorbar" or "barerrorbar".
errorbar_sizedefault 1; errorbar size. Available when plot_type is "errorbar" or "barerrorbar".
errorbar_widthdefault 0.2; errorbar width. Available when plot_type is "errorbar" or "barerrorbar" and by_group is NULL.
errorbar_addpointdefault TRUE; whether add point for mean. Available when plot_type is "errorbar" or "barerrorbar" and by_group is NULL.
errorbar_color_blackdefault FALSE; whether use black for the color of errorbar when plot_type is "errorbar" or "barerrorbar".
point_sizedefault 3; point size for taxa. Available when plot_type is "errorbar" or "barerrorbar".
point_alphadefault 0.8; point transparency. Available when plot_type is "errorbar" or "barerrorbar".
add_linedefault FALSE; whether add line. Available when plot_type is "errorbar" or "barerrorbar".
line_sizedefault 0.8; line size when add_line = TRUE. Available when plot_type is "errorbar" or "barerrorbar".
line_typedefault 2; an integer; line type when add_line = TRUE. The available case is same with line_size.
line_colordefault "grey50"; line color when add_line = TRUE. Available when by_group is NULL. Other available case is same with line_size.
line_alphadefault 0.5; line transparency when add_line = TRUE. The available case is same with line_size.
heatmap_celldefault "P.unadj"; the column of res_diff table for the cell of heatmap when formula with multiple factors is found in the method.
heatmap_sigdefault "Significance"; the column of res_diff for the significance label of heatmap.
heatmap_xdefault "Factors"; the column of res_diff for the x axis of heatmap.
heatmap_ydefault "Taxa"; the column of res_diff for the y axis of heatmap.
heatmap_lab_filldefault "P value"; legend title of heatmap.
coefplot_sig_posdefault 2; Significance label position in the coefficient point and errorbar plot.
The formula is Estimate + coefplot_sig_pos * Std.Error.
This plot is used when there is only one measure found in the table,
and 'Estimate' and 'Std.Error' are both in the column names (such as for lm and lme methods).
The x axis is 'Estimate', and y axis denotes 'Factors'.
When coefplot_sig_pos is a negative value, the label is in the left of the errorbar.
Errorbar size and width in the coefficient point plot can be adjusted with the parameters errorbar_size and errorbar_width.
Point size and alpha can be adjusted with parameters point_size and point_alpha.
The significance label size can be adjusted with parameter add_sig_text_size.
Furthermore, the vertical line around 0 can be adjusted with parameters line_size, line_type, line_color and line_alpha.
...parameters passing to ggpubr::ggboxplot function (or other functions shown by plot_type parameter when it starts with "gg") or
plot_cor function in trans_env class for the heatmap of multiple factors when formula is found in the res_diff of the object.
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$plot_alpha(measure = "Shannon", add_sig = TRUE)
microeco documentation built on Oct. 30, 2024, 9:12 a.m.
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| trans_alpha | R Documentation |
Create trans_alpha object for alpha diversity statistics and visualization.
Description
This class is a wrapper for a series of alpha diversity analysis, including the statistics and visualization.
Methods
Public methods
Method new()
Usage
trans_alpha$new( dataset = NULL, group = NULL, by_group = NULL, by_ID = NULL, order_x = NULL )
Arguments
datasetmicrotableobject.groupdefault NULL; a column name of
sample_tablein the input microtable object used for the statistics across groups.by_groupdefault NULL; a column name of
sample_tableused to perform the differential test among groups (fromgroupparameter) for each group (fromby_groupparameter) separately.by_IDdefault NULL; a column name 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 column name of
sample_tableor a vector with sample names. If provided, sort samples 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(
measure = NULL,
method = c("KW", "KW_dunn", "wilcox", "t.test", "anova", "scheirerRayHare", "lm",
"lme", "betareg", "glmm", "glmm_beta")[1],
formula = NULL,
p_adjust_method = "fdr",
KW_dunn_letter = TRUE,
alpha = 0.05,
anova_post_test = "duncan.test",
return_model = FALSE,
...
)Arguments
measuredefault NULL; character vector; If NULL, all indexes will be used; see names of
microtable$alpha_diversity, e.g.c("Observed", "Chao1", "Shannon").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 default post hoc test is Duncan's new multiple range test. Please use
anova_post_testparameter to change the 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- 'lm'
Linear Model based on the
lmfunction- '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. A family function can be provided using parameter passing, such as:family = glmmTMB::lognormal(link = "log")- 'glmm_beta'
Generalized linear mixed model (GLMM) with a family function of beta distribution. This is an extension of the GLMM model in
'glmm'option. The only difference is inglmm_betathe family function is fixed with the beta distribution function, facilitating the fitting for proportional data (ranging from 0 to 1). The link function is fixed with"logit".
formuladefault NULL; applied to two-way or multi-factor analysis when method is
"anova","scheirerRayHare","lm","lme","betareg"or"glmm"; specified set for independent variables, i.e. the latter part of a general formula, such as'block + N*P*K'.p_adjust_methoddefault "fdr" (for "KW", "wilcox", "t.test" methods) or "holm" (for "KW_dunn"); P value adjustment method; For
method = 'KW', 'wilcox' or 't.test', please seemethodparameter ofp.adjustfunction for available options; Formethod = 'KW_dunn', please seedunn.test::p.adjustment.methodsfor available options.KW_dunn_letterdefault TRUE; For
method = 'KW_dunn',TRUEdenotes 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. The default option represents the Duncan's new multiple range test. Other available options include "LSD.test" (LSD post hoc test) and "HSD.test" (HSD post hoc test). All those are the function names from
agricolaepackage.return_modeldefault FALSE; whether return the original "lm", "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") orstats::lm(whenmethod = "lm") orlmerTest::lmer(whenmethod = "lme") orbetareg::betareg(whenmethod = "betareg") orglmmTMB::glmmTMB(whenmethod = "glmm").
Returns
res_diff, stored in object with the format data.frame.
When method is "betareg", "lm", "lme" or "glmm",
"Estimate" and "Std.Error" columns represent the fitted coefficient and its standard error, respectively.
Examples
\donttest{
t1$cal_diff(method = "KW")
t1$cal_diff(method = "anova")
t1 <- trans_alpha$new(dataset = dataset, group = "Type", by_group = "Group")
t1$cal_diff(method = "anova")
}
Method plot_alpha()
Plot the alpha diversity.
Box plot (and others for visualizing data in groups of single factor) is used for the visualization of alpha diversity when the group is found in the object.
When the formula is found in the res_diff table in the object,
heatmap is employed automatically to show the significances of differential test for multiple indexes,
and errorbar (coefficient and standard errors) can be used for single index.
Usage
trans_alpha$plot_alpha( plot_type = "ggboxplot", color_values = RColorBrewer::brewer.pal(8, "Dark2"), measure = "Shannon", group = NULL, add = NULL, add_sig = TRUE, add_sig_label = "Significance", add_sig_text_size = 3.88, add_sig_label_num_dec = 4, order_x_mean = FALSE, y_start = 0.1, y_increase = 0.05, xtext_angle = 30, xtext_size = 13, ytitle_size = 17, bar_width = 0.9, bar_alpha = 0.8, dodge_width = 0.9, plot_SE = TRUE, errorbar_size = 1, errorbar_width = 0.2, errorbar_addpoint = TRUE, errorbar_color_black = FALSE, point_size = 3, point_alpha = 0.8, add_line = FALSE, line_size = 0.8, line_type = 2, line_color = "grey50", line_alpha = 0.5, heatmap_cell = "P.unadj", heatmap_sig = "Significance", heatmap_x = "Factors", heatmap_y = "Measure", heatmap_lab_fill = "P value", coefplot_sig_pos = 2, ... )
Arguments
plot_typedefault "ggboxplot"; plot type; available options include "ggboxplot", "ggdotplot", "ggviolin", "ggstripchart", "ggerrorplot", "errorbar" and "barerrorbar". The options starting with "gg" are function names coming from
ggpubrpackage. All those methods withggpubrpackage use thedata_alphatable in the object. "errorbar" represents Mean±SD or Mean±SE plot based onggplot2package by invoking thedata_stattable in the object. "barerrorbar" denotes "bar plot + error bar". It is similar with "errorbar" and has a bar plot.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.
adddefault NULL; add another plot element; passed to the
addparameter of the function (e.g.,ggboxplot) fromggpubrpackage whenplot_typestarts with "gg" (functions coming from ggpubr package).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'.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 30; number (e.g. 30). Angle of text in x axis.
xtext_sizedefault 13; x axis text size. NULL means the default size in ggplot2.
ytitle_sizedefault 17; y axis title size.
bar_widthdefault 0.9; the bar width when
plot_type = "barerrorbar".bar_alphadefault 0.8; the alpha of bar color when
plot_type = "barerrorbar".dodge_widthdefault 0.9; the dodge width used in
position_dodgefunction of ggplot2 package whenplot_typeis "errorbar" or "barerrorbar".plot_SEdefault TRUE; TRUE: the errorbar is
mean±se; FALSE: the errorbar ismean±sd. Available whenplot_typeis "errorbar" or "barerrorbar".errorbar_sizedefault 1; errorbar size. Available when
plot_typeis "errorbar" or "barerrorbar".errorbar_widthdefault 0.2; errorbar width. Available when
plot_typeis "errorbar" or "barerrorbar" andby_groupis NULL.errorbar_addpointdefault TRUE; whether add point for mean. Available when
plot_typeis "errorbar" or "barerrorbar" andby_groupis NULL.errorbar_color_blackdefault FALSE; whether use black for the color of errorbar when
plot_typeis "errorbar" or "barerrorbar".point_sizedefault 3; point size for taxa. Available when
plot_typeis "errorbar" or "barerrorbar".point_alphadefault 0.8; point transparency. Available when
plot_typeis "errorbar" or "barerrorbar".add_linedefault FALSE; whether add line. Available when
plot_typeis "errorbar" or "barerrorbar".line_sizedefault 0.8; line size when
add_line = TRUE. Available whenplot_typeis "errorbar" or "barerrorbar".line_typedefault 2; an integer; line type when
add_line = TRUE. The available case is same withline_size.line_colordefault "grey50"; line color when
add_line = TRUE. Available whenby_groupis NULL. Other available case is same withline_size.line_alphadefault 0.5; line transparency when
add_line = TRUE. The available case is same withline_size.heatmap_celldefault "P.unadj"; the column of
res_difftable for the cell of heatmap when formula with multiple factors is found in the method.heatmap_sigdefault "Significance"; the column of
res_difffor the significance label of heatmap.heatmap_xdefault "Factors"; the column of
res_difffor the x axis of heatmap.heatmap_ydefault "Taxa"; the column of
res_difffor the y axis of heatmap.heatmap_lab_filldefault "P value"; legend title of heatmap.
coefplot_sig_posdefault 2; Significance label position in the coefficient point and errorbar plot. The formula is
Estimate + coefplot_sig_pos * Std.Error. This plot is used when there is only one measure found in the table, and 'Estimate' and 'Std.Error' are both in the column names (such as forlmandlme methods). The x axis is 'Estimate', and y axis denotes 'Factors'. When coefplot_sig_pos is a negative value, the label is in the left of the errorbar. Errorbar size and width in the coefficient point plot can be adjusted with the parameterserrorbar_sizeanderrorbar_width. Point size and alpha can be adjusted with parameterspoint_sizeandpoint_alpha. The significance label size can be adjusted with parameteradd_sig_text_size. Furthermore, the vertical line around 0 can be adjusted with parametersline_size,line_type,line_colorandline_alpha....parameters passing to
ggpubr::ggboxplotfunction (or other functions shown byplot_typeparameter when it starts with "gg") orplot_corfunction intrans_envclass for the heatmap of multiple factors when formula is found in theres_diffof the object.
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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