globalFtest | R Documentation |
global F test for Wasserstein regression
globalFtest( wass_regress_res, alpha = 0.05, permutation = FALSE, numPermu = 200, bootstrap = FALSE, numBoot = 200 )
wass_regress_res |
an object returned by the |
alpha |
type one error rate |
permutation |
logical; perform permutation global F test (default: FALSE) |
numPermu |
number of permutation samples if permutation = TRUE |
bootstrap |
logical; bootstrap global F test (default: FALSE) |
numBoot |
number of bootstrap samples if bootstrap = TRUE |
four methods used to compute p value of global F test
truncated: asymptotic inference, p-value is obtained by truncating the infinite summation of eigenvalues into the first K terms, where the first K terms explain more than 99.99% of the variance.
satterthwaite: asymptotic inference, p-value is computed using Satterthwaite's approximation method of mixtures of chi-square.
permutation: resampling technique; Wasserstein SSR is used as the F statistic.
bootstrap: resampling technique; Wasserstein SSR is used as the F statistic.
a list containing the following fields:
wasserstein.F_stat |
the Wasserstein F statistic value in Satterthwaite method . |
chisq_df |
the degree of freedom of the null chi-square distribution. |
summary_df |
a dataframe containing the following columns: |
method: methods used to compute p value, see details
statistic: the test statistics
critical_value: critical value
p_value: p value of global F test
data(strokeCTdensity) predictor = strokeCTdensity$predictors dSup = strokeCTdensity$densitySupport densityCurves = strokeCTdensity$densityCurve res = wass_regress(rightside_formula = ~., Xfit_df = predictor, Ytype = 'density', Ymat = densityCurves, Sup = dSup) globalF_res = globalFtest(res, alpha = 0.05, permutation = TRUE, numPermu = 200)
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