partialFtest: partial F test for Wasserstein regression

In WRI: Wasserstein Regression and Inference

partial F test for Wasserstein regression

Description

partial F test for Wasserstein regression

Usage

partialFtest(reduced_res, full_res, alpha = 0.05)

Arguments

reduced_res	a reduced model list returned by the wass_regress function
full_res	a full model list returned by the wass_regress function
alpha	type one error rate

Details

two methods used to compute p value using asymptotic distribution of F statistic

• 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 approximation method of mixtures of chi-square.

Value

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

Examples

data(strokeCTdensity)
predictor = strokeCTdensity$predictors
dSup = strokeCTdensity$densitySupport
densityCurves = strokeCTdensity$densityCurve

full_res <- wass_regress(rightside_formula = ~., Xfit_df = predictor,
 Ymat = densityCurves, Ytype = 'density', Sup = dSup)
reduced_res <- wass_regress(~ log_b_vol + b_shapInd + midline_shift + B_TimeCT, Xfit_df = predictor,
 Ymat = densityCurves, Ytype = 'density', Sup = dSup)
partialFtable = partialFtest(reduced_res, full_res, alpha = 0.05)

WRI documentation built on March 29, 2026, 1:06 a.m.