bstr_anova.RdThis function accepts a main_effect and a set of covariates (using the R formula notation) and uses an
F-test to compare the full model including the main_effect + covariates with the reduced (null) model
that only includes the covariates.
bstr_anova(main_effect = "", covariates = "", bstr_data, mult_comp = "fdr")Character string containing an independent variable whose effect you want to measure. It could be disease status, age, gender etc. This should strictly be a single variable. This can be either a categorical or a continuous variable.
Character string containing a set of other predictors (variables) in the model. If more than
one covariates are included, they should be separated by a + operator similar to an R formula.
Object of type BstrData()
method for multiple comparisons correction. The default method is "fdr". See bstr_p_adjust() for valid values.
Slightly different from the standard R anova function, bstr_anova currently does not directly accept the
results from lm_vec. This could be accomodated in the future versions.
lm_vec() for linear regression, bstr_ttest() for independent sample and paired t-tests.