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Implements the robust functional analysis of variance (RoFANOVA), described in Centofanti et al. (2021) <arXiv:2112.10643>. It allows testing mean differences among groups of functional data by being robust against the presence of outliers.
Package: | rofanova |
Type: | Package |
Version: | 1.0.0 |
Date: | 2022-01-21 |
License: | GPL-3 |
Fabio Centofanti, Bianca Maria Colosimo, Marco Luigi Grasso, Antonio Lepore, Alessandra Menafoglio, Biagio Palumbo, Simone Vantini
Centofanti, F., Colosimo, B.M., Grasso, M.L., Menafoglio, A., Palumbo, B., Vantini, S. (2021). Robust Functional ANOVA with Application to Additive Manufacturing. arXiv preprint arXiv:2112.10643.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | library(rofanova)
data_out<-simulate_data(scenario="one-way")
label_1=data_out$label_1
X_fdata<-data_out$X_fdata
B=10
cores=1
per_list_median<-rofanova(X_fdata,label_1,B = B,family="median",cores=cores)
pvalue_median_vec<-per_list_median$pval_vec
per_list_huber<-rofanova(X_fdata,label_1,B = B,family="huber",cores=cores)
pvalue_huber_vec<-per_list_huber$pval_vec
per_list_bisquare<-rofanova(X_fdata,label_1,B = B,family="bisquare",cores=cores)
pvalue_bisquare_vec<-per_list_bisquare$pval_vec
per_list_hampel<-rofanova(X_fdata,label_1,B = B,family="hampel",cores=cores)
pvalue_hampel_vec<-per_list_hampel$pval_vec
per_list_optimal<-rofanova(X_fdata,label_1,B = B,family="optimal",cores=cores)
pvalue_optimal<-per_list_optimal$pval
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