compare_multi_features_multi_metrics: Calculate p values for multiple variables
In ayanglab/How-to-Perform-Technical-Systematic-Review-And-Meta-Analysis-Tutorial: What the Package Does (One Line, Title Case)
Description
This function calculates the p value between all possible pairs
of variables between those in x_vals and those in y_vals. The y_vals
must be numerical. If the x_vals is logical, Wilcoxon rank sum test
will be carried out. If the x_vals is numerical, p value will be that from
linear regression.
Usage
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compare_multi_features_multi_metrics(
com_df,
x_vals,
y_vals,
padj_method = "hochberg"
)
Value
a dataframe with the following column:
x: the independent variable
y: the dependent variable
feature: which group in x
presence: the value of y in that group
absence: the value of y outside that group
presence_num: number of samples in that group
absence_num: number of samples outside that group
p: unadjusted p value
p_adj: adjusted p value
ayanglab/How-to-Perform-Technical-Systematic-Review-And-Meta-Analysis-Tutorial documentation built on Feb. 7, 2022, 12:45 a.m.
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Description
This function calculates the p value between all possible pairs
of variables between those in x_vals and those in y_vals. The y_vals
must be numerical. If the x_vals is logical, Wilcoxon rank sum test
will be carried out. If the x_vals is numerical, p value will be that from
linear regression.
Usage
1 2 3 4 5 6 | compare_multi_features_multi_metrics(
com_df,
x_vals,
y_vals,
padj_method = "hochberg"
)
|
Value
a dataframe with the following column:
x: the independent variable
y: the dependent variable
feature: which group in x
presence: the value of y in that group
absence: the value of y outside that group
presence_num: number of samples in that group
absence_num: number of samples outside that group
p: unadjusted p value
p_adj: adjusted p value
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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