jointest R package

A package devoted to multivariate resampling-based tests. By resampling jointly on all univariate tests (e.i. sign-flip score tests by Hemerik, Goeman and Finos (2020)) it allows for multivariate and selective inference – e.g. weak and strong control of the Familywise Error Rate or confidence bounds for True Discovery Proportion.

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Set up

To install this github version type (in R):

##if devtools is not installed yet: 
## install.packages("devtools") 
library(devtools)
install_github("livioivil/jointest")

A (minimal) example

n=20
set.seed(123)
D=data.frame(X=rnorm(n),Z1=rnorm(n),Z2=rnorm(n))
D$Y=D$Z1+D$X+rnorm(n)
mod1=glm(Y~X+Z1+Z2,data=D)
mod2=glm(Y~X+poly(Z1,2)+Z2,data=D)
mod3=glm(Y~X+poly(Z1,2)+poly(Z2,2),data=D)
mod4=glm(Y~X+Z1+poly(Z2,2),data=D)
mods=list(mod1=mod1,mod2=mod2,mod3=mod3,mod4=mod4)
for(i in 1:length(mods))
 mods[[i]]$call$data=eval(D)
library(jointest)
res=join_flipscores(mods,n_flips = 5000,        seed = 1, tested_coeffs = "X")
summary(res)
#>   Model Coeff  Estimate    Score Std. Error  z value Part. Cor   Pr(>|t|)
#> 1  mod1     X 1.1254046 19.78375   6.309305 3.135646 0.7605058 0.01739652
#> 2  mod2     X 0.9552644 15.38036   5.053879 3.043278 0.7608195 0.01519696
#> 3  mod3     X 1.0121921 14.31866   4.836669 2.960437 0.7643816 0.01859628
#> 4  mod4     X 1.1696906 17.62979   5.875380 3.000621 0.7501552 0.00799840
summary(combine(res))
#>   Coeff Stat nMods        S          p
#> 1     X maxT     4 19.78375 0.01879624
res=p.adjust.fwer(res)
summary(res)
#>   Model Coeff  Estimate    Score Std. Error  z value Part. Cor   Pr(>|t|)
#> 1  mod1     X 1.1254046 19.78375   6.309305 3.135646 0.7605058 0.01739652
#> 2  mod2     X 0.9552644 15.38036   5.053879 3.043278 0.7608195 0.01519696
#> 3  mod3     X 1.0121921 14.31866   4.836669 2.960437 0.7643816 0.01859628
#> 4  mod4     X 1.1696906 17.62979   5.875380 3.000621 0.7501552 0.00799840
#>        p.adj
#> 1 0.01879624
#> 2 0.02019596
#> 3 0.02019596
#> 4 0.01879624

References

J Hemerik, JJ Goeman and L Finos (2019) Robust testing in generalized linear models by sign-flipping score contributions. Journal of the Royal Statistical Society Series B: Statistical Methodology, Volume 82, Issue 3, July 2020, Pages 841–864.
https://doi.org/10.1111/rssb.12369

R De Santis, J Goeman, J Hemerik, L Finos (2022) Inference in generalized linear models with robustness to misspecified variances arXiv: 2209.13918.
https://arxiv.org/abs/2209.13918

P Girardi, A Vesely, D Lakens, G Altoè, M Pastore, A Calcagnì, L Finos (2022) Post-selection Inference in Multiverse Analysis (PIMA): an inferential framework based on the sign flipping score test. arxiv: 2210.02794.
https://arxiv.org/abs/2210.02794

Bug reports

If you encounter a bug, please file a reprex (minimal reproducible example) on github.