Adjust local association measures' p-values
p.adjustSP.RdMake an adjustment to local association measures' p-values based on the number of neighbours (+1) of each region, rather than the total number of regions.
Arguments
- p
vector of p-values
- nb
a list of neighbours of class
nb- method
correction method as defined in
p.adjust: "The adjustment methods include the Bonferroni correction ('"bonferroni"') in which the p-values are multiplied by the number of comparisons. Four less conservative corrections are also included by Holm (1979) ('holm'), Hochberg (1988) ('hochberg'), Hommel (1988) ('hommel') and Benjamini & Hochberg (1995) ('fdr'), respectively. A pass-through option ('none') is also included."
Author
Danlin Yu and Roger Bivand Roger.Bivand@nhh.no
Examples
data(afcon, package="spData")
oid <- order(afcon$id)
resG <- as.vector(localG(afcon$totcon, nb2listw(include.self(paper.nb))))
non <- format.pval(pnorm(2*(abs(resG)), lower.tail=FALSE), 2)
bon <- format.pval(p.adjustSP(pnorm(2*(abs(resG)), lower.tail=FALSE),
paper.nb, "bonferroni"), 2)
tot <- format.pval(p.adjust(pnorm(2*(abs(resG)), lower.tail=FALSE),
"bonferroni", n=length(resG)), 2)
data.frame(resG, non, bon, tot, row.names=afcon$name)[oid,]
#> resG non bon tot
#> THE GAMBIA -0.98383592 0.02455 0.04911 1.0000
#> MALI -1.69893391 0.00034 0.00272 0.0143
#> SENEGAL -1.46321911 0.00171 0.00857 0.0720
#> BENIN -1.30139679 0.00462 0.02312 0.1942
#> MAURITANIA -0.60496775 0.11315 0.56576 1.0000
#> NIGER -1.04877003 0.01797 0.14378 0.7549
#> IVORY COAST -1.41712454 0.00230 0.01378 0.0965
#> GUINEA -1.44888005 0.00188 0.01128 0.0789
#> BURKINA FASO -1.75085492 0.00023 0.00162 0.0097
#> LIBERIA -1.04053617 0.01871 0.07485 0.7860
#> SIERRA LEONE -0.87032623 0.04087 0.12262 1.0000
#> GHANA -1.10269327 0.01371 0.05485 0.5760
#> TOGO -0.99053008 0.02379 0.09517 0.9993
#> CAMEROON -1.13328519 0.01171 0.07025 0.4917
#> NIGERIA -1.17261672 0.00951 0.04754 0.3993
#> GABON -0.78935857 0.05720 0.17160 1.0000
#> CENTRAL AFRICAN REPUBLIC 1.17349763 0.00946 0.05678 0.3974
#> CHAD 0.46259185 0.17744 1.00000 1.0000
#> CONGO -0.20253005 0.34272 1.00000 1.0000
#> ZAIRE 2.02270432 2.6e-05 0.00026 0.0011
#> ANGOLA 1.23450728 0.00677 0.02710 0.2845
#> UGANDA 3.33600851 1.3e-11 7.6e-11 5.3e-10
#> KENYA 3.50301896 1.2e-12 7.4e-12 5.1e-11
#> TANZANIA 1.09843592 0.01401 0.12613 0.5886
#> BURUNDI 0.77417084 0.06077 0.24308 1.0000
#> RWANDA 1.45720776 0.00178 0.00891 0.0748
#> SOMALIA 1.18316273 0.00898 0.02695 0.3773
#> ETHIOPIA 2.62720027 7.4e-08 3.0e-07 3.1e-06
#> ZAMBIA 0.75273285 0.06610 0.59492 1.0000
#> ZIMBABWE -0.19956472 0.34490 1.00000 1.0000
#> MALAWI 0.21195283 0.33582 1.00000 1.0000
#> MOZAMBIQUE -0.28761679 0.28257 1.00000 1.0000
#> SOUTH AFRICA -0.86814954 0.04126 0.33004 1.0000
#> LESOTHO -0.29841469 0.27531 0.55062 1.0000
#> BOTSWANA 0.04090396 0.46740 1.00000 1.0000
#> SWAZILAND -0.65938417 0.09362 0.28087 1.0000
#> MOROCCO 0.02191606 0.48252 1.00000 1.0000
#> ALGERIA -0.36307938 0.23387 1.00000 1.0000
#> TUNISIA 0.57910139 0.12339 0.37017 1.0000
#> LIBYA 2.55272169 1.7e-07 1.2e-06 6.9e-06
#> SUDAN 4.03925235 3.3e-16 3.0e-15 1.4e-14
#> EGYPT 4.42133637 < 2e-16 < 2e-16 < 2e-16