omu_anova {omu} | R Documentation |
Perform anova
Description
Performs an anova across all response variables, followed by a Tukeys test on every possible contrast in your model and calculates group means and fold changes for each contrast. Returns a list of data frames for each contrast, and includes a dataframe of model residuals
Usage
omu_anova(
count_data,
metadata,
response_variable = "Metabolite",
model,
log_transform = FALSE,
method = "anova"
)
Arguments
count_data |
A metabolomics count data frame |
metadata |
Metadata dataframe for the metabolomics count data frame |
response_variable |
String of the column header for the response variables, usually "Metabolite" |
model |
A formual class object, see ?formula for more info on formulas in R. an interaction between independent variables. Optional parameter |
log_transform |
Boolean of TRUE or FALSE for whether or not you wish to log transform your metabolite counts |
method |
A string of 'anova', 'kruskal', or 'welch'. anova performs an anova with a post hoc tukeys test, kruskal performs a kruskal wallis with a post hoc dunn test, welch performs a welch's anova with a post hoc games howell test |
Examples
anova_df <- omu_anova(count_data = c57_nos2KO_mouse_countDF, metadata = c57_nos2KO_mouse_metadata,
response_variable = "Metabolite", model = ~ Treatment, log_transform = TRUE)
anova_df <- omu_anova(count_data = c57_nos2KO_mouse_countDF, metadata = c57_nos2KO_mouse_metadata,
response_variable = "Metabolite", model = ~ Treatment + Background, log_transform = TRUE)
anova_df <- omu_anova(count_data = c57_nos2KO_mouse_countDF, metadata = c57_nos2KO_mouse_metadata,
response_variable = "Metabolite", model = ~ Treatment + Background + Treatment*Background,
log_transform = TRUE)