R : Copyright 2005, The R Foundation for Statistical Computing Version 2.1.1 (2005-06-20), ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for a HTML browser interface to help. Type 'q()' to quit R. > ### *
> ### > attach(NULL, name = "CheckExEnv") > assign(".CheckExEnv", as.environment(2), pos = length(search())) # base > ## add some hooks to label plot pages for base and grid graphics > setHook("plot.new", ".newplot.hook") > setHook("persp", ".newplot.hook") > setHook("grid.newpage", ".gridplot.hook") > > assign("cleanEx", + function(env = .GlobalEnv) { + rm(list = ls(envir = env, all.names = TRUE), envir = env) + RNGkind("default", "default") + set.seed(1) + options(warn = 1) + delayedAssign("T", stop("T used instead of TRUE"), + assign.env = .CheckExEnv) + delayedAssign("F", stop("F used instead of FALSE"), + assign.env = .CheckExEnv) + sch <- search() + newitems <- sch[! sch %in% .oldSearch] + for(item in rev(newitems)) + eval(substitute(detach(item), list(item=item))) + missitems <- .oldSearch[! .oldSearch %in% sch] + if(length(missitems)) + warning("items ", paste(missitems, collapse=", "), + " have been removed from the search path") + }, + env = .CheckExEnv) > assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now > assign("ptime", proc.time(), env = .CheckExEnv) > grDevices::postscript("hierfstat-Examples.ps") > assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv) > options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly")) > options(warn = 1) > library('hierfstat') > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .CheckExEnv) > cleanEx(); ..nameEx <- "boot.vc" > > ### * boot.vc > > flush(stderr()); flush(stdout()) > > ### Name: boot.vc > ### Title: Bootstrap confidence intervals for variance components > ### Aliases: boot.vc > ### Keywords: univar > > ### ** Examples > > #load data set > data(gtrunchier) > boot.vc(gtrunchier[,c(1:2)],gtrunchier[,-c(1:2)],nboot=100) $boot Locality Patch Ind Error 1 2.552384 0.3955016 1.1347297 0.3893008 2 2.446031 0.5052575 1.2307607 0.4020581 3 2.267727 0.5857943 1.2990292 0.3323447 4 2.301441 0.5659117 1.2162161 0.3780006 5 2.222488 0.4498070 0.8764198 0.3647017 6 2.337888 0.4780197 1.1671660 0.3342621 7 2.161374 0.6955502 1.3950602 0.3451021 8 2.143254 0.6786298 1.3651323 0.3581197 9 2.381901 0.4755479 1.1586395 0.3432439 10 2.146552 0.6091551 1.1273850 0.3906173 11 2.640665 0.3046474 1.0327944 0.3782006 12 2.182743 0.6071737 1.1632173 0.3759425 13 2.196548 0.4333771 0.8908507 0.3540627 14 2.381901 0.4755479 1.1586395 0.3432439 15 2.345500 0.5992620 1.2317131 0.3756220 16 2.349976 0.6324242 1.4010783 0.3781413 17 2.157059 0.5048331 1.0927657 0.3362399 18 2.415615 0.4556654 1.0758265 0.3888998 19 2.275500 0.5494818 1.2306470 0.3673617 20 1.939621 0.7770934 1.2003449 0.3882990 21 2.333365 0.4090354 0.9737774 0.3431032 22 2.402019 0.5077293 1.2392872 0.3930764 23 2.181444 0.6919094 1.4516843 0.4062950 24 2.556908 0.4644859 1.3281183 0.3804597 25 2.319560 0.5828321 1.2461440 0.3649830 26 2.464360 0.4004452 1.1517827 0.3713372 27 2.578325 0.4119316 1.1202989 0.3999397 28 2.108314 0.5600533 1.0168095 0.3538025 29 2.275500 0.5494818 1.2306470 0.3673617 30 2.305917 0.5990739 1.3855813 0.3805200 31 2.715094 0.3517678 1.1792022 0.4003406 32 2.298143 0.6353864 1.4539635 0.3455030 33 2.264993 0.6538037 1.2652663 0.4217391 34 2.777482 0.2803058 1.1157212 0.3672410 35 2.420138 0.5246497 1.2692150 0.3800587 36 2.393989 0.6299524 1.3925518 0.3871231 37 2.327334 0.5465195 1.1777618 0.4000000 38 2.446031 0.5052575 1.2307607 0.4020581 39 2.090195 0.5431330 0.9868816 0.3668201 40 2.175178 0.5217535 1.1226936 0.3232223 41 2.415615 0.4556654 1.0758265 0.3888998 42 2.182743 0.6071737 1.1632173 0.3759425 43 2.178476 0.4522788 0.8849463 0.3557199 44 2.526491 0.4148938 1.1731841 0.3673014 45 2.213207 0.6925880 1.3421750 0.3777404 46 2.411139 0.4225031 0.9064613 0.3863805 47 2.389722 0.4750575 1.1142808 0.3669004 48 2.097807 0.6643753 1.0514288 0.4081799 49 2.485567 0.4696235 1.0528691 0.4085205 50 2.014050 0.8242137 1.3467527 0.4104390 51 2.108314 0.5600533 1.0168095 0.3538025 52 2.296965 0.5327495 1.0468510 0.3754813 53 2.271024 0.5163195 1.0612819 0.3648423 54 2.420091 0.4888276 1.2451916 0.3914192 55 2.504865 0.5891809 1.4899094 0.3655246 56 2.271024 0.5163195 1.0612819 0.3648423 57 2.327334 0.5465195 1.1777618 0.4000000 58 2.452062 0.3677734 1.0267763 0.3451613 59 2.082421 0.5794455 1.0552638 0.3318031 60 2.371346 0.5440478 1.1692353 0.4089818 61 2.490044 0.5027857 1.2222342 0.4110399 62 2.112838 0.6290376 1.2101981 0.3449614 63 2.521968 0.3459095 0.9797955 0.3761425 64 2.345662 0.4417072 1.0987838 0.3692791 65 2.482270 0.5390983 1.2906164 0.3760229 66 1.965514 0.7577012 1.1618906 0.4102984 67 2.371602 0.4581371 1.0843529 0.3799181 68 2.368049 0.6135225 1.4069827 0.3764841 69 2.231279 0.6736862 1.3480794 0.3760832 70 2.356008 0.4949401 1.1970939 0.3212445 71 2.046183 0.5456047 0.9954081 0.3578383 72 2.220724 0.7421861 1.3586752 0.4418211 73 2.050497 0.7363218 1.2977025 0.3667005 74 2.283321 0.5489913 1.1862883 0.3910183 75 2.160148 0.5570911 0.9639243 0.3864408 76 2.252905 0.4993992 1.0313540 0.3778600 77 2.058271 0.7000092 1.2293203 0.4017175 78 2.208684 0.6236037 1.1487864 0.3865815 79 2.219191 0.5192817 1.1141671 0.3322040 80 2.271024 0.5163195 1.0612819 0.3648423 81 2.243576 0.7063580 1.4730857 0.4022591 82 2.172236 0.7114956 1.1978365 0.4303200 83 2.178476 0.4522788 0.8849463 0.3557199 84 2.199564 0.7088298 1.4816122 0.3932774 85 2.275500 0.5494818 1.2306470 0.3673617 86 2.319560 0.5828321 1.2461440 0.3649830 87 2.236011 0.6209378 1.4325621 0.3495389 88 2.156850 0.6265659 1.2016716 0.3539432 89 2.327334 0.5465195 1.1777618 0.4000000 90 2.116135 0.5595629 0.9724507 0.3774590 91 2.415615 0.4556654 1.0758265 0.3888998 92 2.305917 0.5990739 1.3855813 0.3805200 93 1.983633 0.7746216 1.1918184 0.3972807 94 2.161374 0.6955502 1.3950602 0.3451021 95 2.600920 0.4620141 1.3195919 0.3894414 96 2.164671 0.6260754 1.1573129 0.3775997 97 2.293620 0.5664021 1.2605749 0.3543441 98 2.014259 0.7024810 1.2378468 0.3927358 99 2.442734 0.5747323 1.4685080 0.3695605 100 2.126643 0.4552410 0.9378315 0.3230816 $res H-Total F-Locality/Total F-Patch/Total F-Ind/Total H-Locality 1 0.7453194 0.5707585 0.6591997 0.9129454 0.3199220 2 0.7640179 0.5335894 0.6438088 0.9122930 0.3563461 3 0.7474825 0.5056365 0.6362515 0.9258969 0.3695280 4 0.7435949 0.5158366 0.6426780 0.9152763 0.3600214 5 0.6522361 0.5679151 0.6828548 0.9068074 0.2818214 6 0.7195560 0.5415118 0.6522327 0.9225768 0.3299080 7 0.7661810 0.4701617 0.6214641 0.9249303 0.4059521 8 0.7575227 0.4715490 0.6208580 0.9212081 0.4003136 9 0.7265553 0.5463912 0.6554785 0.9212623 0.3295719 10 0.7122849 0.5022691 0.6448045 0.9085999 0.3545262 11 0.7260513 0.6061705 0.6761030 0.9131832 0.2859404 12 0.7215128 0.5042053 0.6444600 0.9131587 0.3577223 13 0.6458064 0.5668747 0.6787186 0.9086252 0.2797151 14 0.7265553 0.5463912 0.6554785 0.9212623 0.3295719 15 0.7586829 0.5152571 0.6469023 0.9174838 0.3677662 16 0.7936034 0.4935245 0.6263416 0.9205856 0.4019406 17 0.6818163 0.5272826 0.6506866 0.9178078 0.3223065 18 0.7226677 0.5571059 0.6621946 0.9103092 0.3200653 19 0.7371651 0.5144710 0.6387040 0.9169427 0.3579151 20 0.7175597 0.4505132 0.6310077 0.9098103 0.3942895 21 0.6765468 0.5748222 0.6755877 0.9154768 0.2876527 22 0.7570186 0.5288331 0.6406157 0.9134596 0.3566821 23 0.7885555 0.4610634 0.6073032 0.9141267 0.4249815 24 0.7883286 0.5405757 0.6387763 0.9195641 0.3621773 25 0.7522532 0.5139139 0.6430442 0.9191356 0.3656599 26 0.7313208 0.5616230 0.6528838 0.9153729 0.3205942 27 0.7517491 0.5716279 0.6629552 0.9113313 0.3220284 28 0.6731633 0.5219918 0.6606539 0.9124030 0.3217776 29 0.7371651 0.5144710 0.6387040 0.9169427 0.3579151 30 0.7785153 0.4936569 0.6219083 0.9185372 0.3941959 31 0.7744008 0.5843430 0.6600505 0.9138386 0.3218851 32 0.7888327 0.4855578 0.6198039 0.9270012 0.4058088 33 0.7676337 0.4917695 0.6337217 0.9084331 0.3901348 34 0.7567917 0.6116791 0.6734103 0.9191233 0.2938780 35 0.7656769 0.5267971 0.6409988 0.9172717 0.3623206 36 0.8006027 0.4983722 0.6295135 0.9194101 0.4016046 37 0.7419358 0.5228066 0.6455754 0.9101450 0.3540469 38 0.7640179 0.5335894 0.6438088 0.9122930 0.3563461 39 0.6645050 0.5242487 0.6604736 0.9079966 0.3161391 40 0.6904746 0.5250443 0.6509850 0.9219807 0.3279449 41 0.7226677 0.5571059 0.6621946 0.9103092 0.3200653 42 0.7215128 0.5042053 0.6444600 0.9131587 0.3577223 43 0.6452368 0.5627071 0.6795321 0.9081165 0.2821575 44 0.7469784 0.5637136 0.6562851 0.9180473 0.3258965 45 0.7709517 0.4784578 0.6281835 0.9183389 0.4020839 46 0.6877473 0.5843083 0.6866965 0.9063657 0.2858908 47 0.7243267 0.5498719 0.6591821 0.9155767 0.3260398 48 0.7036318 0.4968998 0.6542679 0.9033159 0.3539973 49 0.7360968 0.5627810 0.6691129 0.9075030 0.3218355 50 0.7659092 0.4382699 0.6176240 0.9106859 0.4302342 51 0.6731633 0.5219918 0.6606539 0.9124030 0.3217776 52 0.7086744 0.5402021 0.6654946 0.9116940 0.3258470 53 0.7022447 0.5389917 0.6615320 0.9134104 0.3237406 54 0.7575882 0.5324112 0.6399515 0.9138892 0.3542397 55 0.8249134 0.5060865 0.6251255 0.9261489 0.4074358 56 0.7022447 0.5389917 0.6615320 0.9134104 0.3237406 57 0.7419358 0.5228066 0.6455754 0.9101450 0.3540469 58 0.6986289 0.5849702 0.6727071 0.9176574 0.2899518 59 0.6748223 0.5143135 0.6574242 0.9180517 0.3277521 60 0.7489352 0.5277150 0.6487864 0.9089859 0.3537108 61 0.7710172 0.5382594 0.6469439 0.9111477 0.3560100 62 0.7161725 0.4916967 0.6380855 0.9197211 0.3640329 63 0.7039692 0.5970829 0.6789779 0.9109472 0.2836412 64 0.7092387 0.5512159 0.6550144 0.9132217 0.3182950 65 0.7813346 0.5294936 0.6444888 0.9197905 0.3676229 66 0.7159006 0.4575853 0.6339834 0.9044797 0.3883150 67 0.7156684 0.5523047 0.6589969 0.9115237 0.3204014 68 0.7941730 0.4969632 0.6257182 0.9209903 0.3994982 69 0.7715213 0.4820085 0.6275405 0.9187572 0.3996415 70 0.7282144 0.5392203 0.6524974 0.9264767 0.3355464 71 0.6575056 0.5186730 0.6569747 0.9092940 0.3164752 72 0.7939011 0.4662051 0.6220150 0.9072468 0.4237804 73 0.7418703 0.4606594 0.6260795 0.9176180 0.4001208 74 0.7349365 0.5178047 0.6423032 0.9113261 0.3543830 75 0.6779340 0.5310615 0.6680195 0.9049955 0.3179094 76 0.6935863 0.5413661 0.6613702 0.9092014 0.3181022 77 0.7315530 0.4689273 0.6284075 0.9084784 0.3885078 78 0.7279425 0.5056909 0.6484686 0.9114899 0.3598286 79 0.6974740 0.5302924 0.6543787 0.9206173 0.3276088 80 0.7022447 0.5389917 0.6615320 0.9134104 0.3237406 81 0.8042132 0.4649630 0.6113500 0.9166351 0.4302838 82 0.7519813 0.4814472 0.6391408 0.9046253 0.3899420 83 0.6452368 0.5627071 0.6795321 0.9081165 0.2821575 84 0.7972139 0.4598439 0.6080329 0.9177809 0.4306199 85 0.7371651 0.5144710 0.6387040 0.9169427 0.3579151 86 0.7522532 0.5139139 0.6430442 0.9191356 0.3656599 87 0.7731750 0.4819977 0.6158479 0.9246529 0.4005065 88 0.7231718 0.4970811 0.6414834 0.9184281 0.3636968 89 0.7419358 0.5228066 0.6455754 0.9101450 0.3540469 90 0.6709347 0.5256685 0.6646694 0.9062355 0.3182454 91 0.7226677 0.5571059 0.6621946 0.9103092 0.3200653 92 0.7785153 0.4936569 0.6219083 0.9185372 0.3941959 93 0.7245590 0.4562852 0.6344675 0.9086155 0.3939535 94 0.7661810 0.4701617 0.6214641 0.9249303 0.4059521 95 0.7953279 0.5450414 0.6418598 0.9183898 0.3618412 96 0.7209432 0.5004257 0.6451610 0.9127070 0.3601647 97 0.7458235 0.5125475 0.6391195 0.9208159 0.3635535 98 0.7245537 0.4633332 0.6249225 0.9096603 0.3888439 99 0.8092557 0.5030823 0.6214488 0.9238888 0.4021335 100 0.6404661 0.5534101 0.6718762 0.9159254 0.2860257 F-Patch/Locality F-Ind/Locality H-Patch F-Ind/Patch Hobs 1 0.2060406 0.7971898 0.2540051 0.7445584 0.06488346 2 0.2363141 0.8119533 0.2721365 0.7537644 0.06700969 3 0.2642083 0.8501040 0.2718957 0.7962793 0.05539079 4 0.2619806 0.8250101 0.2657028 0.7628926 0.06300011 5 0.2660118 0.7843187 0.2068536 0.7061515 0.06078361 6 0.2414914 0.8311336 0.2502380 0.7773705 0.05571036 7 0.2855633 0.8583158 0.2900270 0.8016840 0.05751701 8 0.2825409 0.8509004 0.2872087 0.7921838 0.05968662 9 0.2404877 0.8264193 0.2503139 0.7714577 0.05720732 10 0.2863705 0.8163665 0.2530004 0.7426767 0.06510289 11 0.1775705 0.7795575 0.2351658 0.7319618 0.06303343 12 0.2828888 0.8248443 0.2565266 0.7557482 0.06265709 13 0.2582253 0.7890337 0.2074856 0.7155925 0.05901045 14 0.2404877 0.8264193 0.2503139 0.7714577 0.05720732 15 0.2715774 0.8297732 0.2678892 0.7663076 0.06260366 16 0.2622378 0.8432018 0.2965366 0.7874679 0.06302355 17 0.2610523 0.8261283 0.2381676 0.7647036 0.05603998 18 0.2372773 0.7974893 0.2441210 0.7344898 0.06481664 19 0.2558716 0.8289344 0.2663348 0.7701128 0.06122695 20 0.3284783 0.8358656 0.2647740 0.7555783 0.06471650 21 0.2369961 0.8012052 0.2194801 0.7394576 0.05718387 22 0.2372464 0.8163274 0.2720606 0.7591980 0.06551273 23 0.2713489 0.8406617 0.3096632 0.7813243 0.06771583 24 0.2137470 0.8249202 0.2847630 0.7773238 0.06340995 25 0.2656531 0.8336418 0.2685212 0.7734611 0.06083050 26 0.2081786 0.8069536 0.2538533 0.7561996 0.06188954 27 0.2131963 0.7930101 0.2533731 0.7369231 0.06665661 28 0.2900831 0.8167458 0.2284353 0.7418653 0.05896708 29 0.2558716 0.8289344 0.2663348 0.7701128 0.06122695 30 0.2532894 0.8391155 0.2943502 0.7845424 0.06342000 31 0.1821394 0.7927104 0.2632571 0.7465465 0.06672344 32 0.2609548 0.8581011 0.2999111 0.8079970 0.05758384 33 0.2793067 0.8198319 0.2811676 0.7500072 0.07028986 34 0.1589695 0.7917270 0.2471604 0.7523598 0.06120684 35 0.2413377 0.8251738 0.2748790 0.7695599 0.06334312 36 0.2614315 0.8393432 0.2966125 0.7824754 0.06452051 37 0.2572727 0.8117010 0.2629603 0.7464763 0.06666667 38 0.2363141 0.8119533 0.2721365 0.7537644 0.06700969 39 0.2863365 0.8066146 0.2256170 0.7290244 0.06113668 40 0.2651632 0.8357334 0.2409860 0.7764584 0.05387038 41 0.2372773 0.7974893 0.2441210 0.7344898 0.06481664 42 0.2828888 0.8248443 0.2565266 0.7557482 0.06265709 43 0.2671550 0.7898810 0.2067777 0.7132831 0.05928665 44 0.2121807 0.8121585 0.2567476 0.7615678 0.06121690 45 0.2870827 0.8434239 0.2866526 0.7803727 0.06295673 46 0.2463080 0.7747506 0.2154736 0.7011386 0.06439674 47 0.2428423 0.8124460 0.2468635 0.7522920 0.06115007 48 0.3127967 0.8078234 0.2432681 0.7203498 0.06802999 49 0.2432006 0.7884424 0.2435649 0.7204575 0.06808675 50 0.3192888 0.8410017 0.2928653 0.7664233 0.06840651 51 0.2900831 0.8167458 0.2284353 0.7418653 0.05896708 52 0.2724947 0.8079460 0.2370554 0.7360102 0.06258021 53 0.2658093 0.8121735 0.2376874 0.7441721 0.06080706 54 0.2299891 0.8158407 0.2727685 0.7608355 0.06523653 55 0.2410117 0.8504776 0.3092390 0.8029978 0.06092077 56 0.2658093 0.8121735 0.2376874 0.7441721 0.06080706 57 0.2572727 0.8117010 0.2629603 0.7464763 0.06666667 58 0.2113991 0.8015985 0.2286563 0.7484133 0.05752689 59 0.2946564 0.8312733 0.2311778 0.7607880 0.05530052 60 0.2563524 0.8072899 0.2630362 0.7408584 0.06816363 61 0.2353800 0.8075710 0.2722124 0.7483338 0.06850665 62 0.2879949 0.8420649 0.2591932 0.7781826 0.05749357 63 0.2032553 0.7789799 0.2259897 0.7225961 0.06269041 64 0.2312881 0.8066369 0.2446772 0.7484582 0.06154652 65 0.2444073 0.8295251 0.2777732 0.7743826 0.06267048 66 0.3252090 0.8238980 0.2620315 0.7390273 0.06838306 67 0.2383142 0.8023739 0.2440452 0.7405412 0.06331968 68 0.2559555 0.8429346 0.2972445 0.7889032 0.06274736 69 0.2809544 0.8431581 0.2873604 0.7818749 0.06268054 70 0.2458379 0.8404371 0.2530564 0.7884236 0.05354075 71 0.2873341 0.8115501 0.2255411 0.7355705 0.05963972 72 0.2918910 0.8262382 0.3000827 0.7546115 0.07363685 73 0.3067081 0.8472542 0.2774005 0.7796805 0.06111676 74 0.2581912 0.8161037 0.2628844 0.7520975 0.06516971 75 0.2920597 0.7974052 0.2250608 0.7138249 0.06440680 76 0.2616555 0.8020238 0.2348690 0.7318647 0.06297666 77 0.3002982 0.8276665 0.2718396 0.7537044 0.06695292 78 0.2888429 0.8209418 0.2558946 0.7482157 0.06443025 79 0.2641777 0.8309956 0.2410619 0.7703190 0.05536734 80 0.2658093 0.8121735 0.2376874 0.7441721 0.06080706 81 0.2736016 0.8441884 0.3125575 0.7855012 0.06704319 82 0.3041032 0.8160752 0.2713594 0.7357011 0.07172000 83 0.2671550 0.7898810 0.2067777 0.7132831 0.05928665 84 0.2743447 0.8477863 0.3124816 0.7902397 0.06554623 85 0.2558716 0.8289344 0.2663348 0.7701128 0.06122695 86 0.2656531 0.8336418 0.2685212 0.7734611 0.06083050 87 0.2583969 0.8545430 0.2970168 0.8038614 0.05825648 88 0.2871283 0.8378030 0.2592691 0.7724738 0.05899053 89 0.2572727 0.8117010 0.2629603 0.7464763 0.06666667 90 0.2930458 0.8023229 0.2249850 0.7203820 0.06290984 91 0.2372773 0.7974893 0.2441210 0.7344898 0.06481664 92 0.2532894 0.8391155 0.2943502 0.7845424 0.06342000 93 0.3277128 0.8319257 0.2648499 0.7499963 0.06621346 94 0.2855633 0.8583158 0.2900270 0.8016840 0.05751701 95 0.2128070 0.8206205 0.2848389 0.7721276 0.06490691 96 0.2897172 0.8252652 0.2558188 0.7539927 0.06293329 97 0.2596601 0.8375553 0.2691532 0.7805809 0.05905735 98 0.3010981 0.8316652 0.2717638 0.7591439 0.06545596 99 0.2382013 0.8468334 0.3063448 0.7989409 0.06159342 100 0.2652681 0.8117409 0.2101522 0.7437717 0.05384693 $ci H-Total F-Locality/Total F-Patch/Total F-Ind/Total H-Locality 2.5% 0.6455074 0.4569027 0.6134865 0.9048011 0.2821575 50% 0.7355167 0.5228066 0.6453682 0.9136491 0.3552681 97.5% 0.8024982 0.5913293 0.6795321 0.9260292 0.4277392 F-Patch/Locality F-Ind/Locality H-Patch F-Ind/Patch Hobs 2.5% 0.1921695 0.7818190 0.2071538 0.7132831 0.05454969 50% 0.2615435 0.8224199 0.2624580 0.7550949 0.06268547 97.5% 0.3223969 0.8564110 0.3094617 0.8023737 0.06944283 > > > > cleanEx(); ..nameEx <- "exhier" > > ### * exhier > > flush(stderr()); flush(stdout()) > > ### Name: exhier > ### Title: Example data set with 4 levels, one diploid and one haploid > ### locus > ### Aliases: exhier > ### Keywords: datasets > > ### ** Examples > > data(exhier) > varcomp(exhier[,1:5]) $df [1] 4 15 20 120 340 500 $k [,1] [,2] [,3] [,4] [,5] [,6] [1,] 200 50 25.04 6.320000 2 1 [2,] 0 50 25.04 6.320000 2 1 [3,] 0 0 24.96 6.295385 2 1 [4,] 0 0 0.00 6.230769 2 1 [5,] 0 0 0.00 0.000000 2 1 [6,] 0 0 0.00 0.000000 0 1 $res 1 2 3 4 5 6 1 -2.500000e-06 -3.348277e-06 3.287759e-06 1.966836e-05 -1.960784e-05 0.002 2 -1.250000e-05 3.893316e-05 1.076523e-06 8.018640e-05 -2.401961e-04 0.012 3 2.750000e-05 -1.449281e-04 8.694133e-05 -1.681897e-04 6.617647e-05 0.012 4 6.666667e-06 -8.593514e-06 6.048894e-05 -4.493464e-04 2.210784e-03 0.012 5 -1.250000e-05 3.911995e-05 -4.002046e-05 -1.656681e-04 2.046569e-03 0.010 6 -1.833333e-05 -7.138961e-05 1.780966e-04 -3.603345e-04 2.696078e-05 0.016 7 -3.750000e-05 -3.627174e-05 1.846139e-04 -1.001069e-04 -8.823529e-05 0.009 8 -3.083333e-05 6.379765e-06 -7.208823e-05 3.580650e-05 -3.676471e-05 0.010 9 -3.166667e-05 -5.909749e-05 5.346730e-05 -1.840757e-04 5.637255e-05 0.013 10 -2.250000e-05 -5.373885e-06 1.363450e-04 -2.045005e-04 7.352941e-05 0.005 11 -1.416667e-05 -7.783341e-06 -5.502389e-05 -1.570947e-04 4.656863e-05 0.014 12 -6.000000e-05 -6.558507e-05 2.693440e-04 -2.077786e-04 1.924020e-03 0.010 13 7.500000e-06 -5.293978e-06 -8.783844e-05 1.013677e-04 -8.823529e-05 0.009 14 -1.250000e-05 8.309126e-05 -8.195150e-05 -2.319858e-05 2.205882e-05 0.004 15 -1.833333e-05 6.104724e-05 -9.952501e-05 1.916404e-05 -7.352941e-06 0.007 16 -1.666667e-05 6.411713e-05 6.335967e-05 -2.355160e-04 1.470588e-05 0.011 17 -1.916667e-05 -9.481523e-06 2.749497e-06 -2.042484e-05 1.933824e-03 0.009 18 3.250000e-05 -3.403530e-05 -4.566978e-05 -2.486283e-04 2.083333e-04 0.010 19 9.166667e-06 -6.718514e-05 -2.749497e-06 2.042484e-05 -1.715686e-05 0.008 20 -1.666667e-05 1.507918e-04 -2.661436e-04 5.587832e-04 -5.367647e-04 0.011 21 -1.750000e-05 4.268644e-05 -1.789355e-04 7.262164e-05 -5.637255e-05 0.012 22 3.250000e-05 -4.052353e-05 -8.709166e-05 4.437989e-05 -3.676471e-05 0.010 23 -3.500000e-05 7.762504e-05 -1.145866e-04 2.486283e-04 -2.916667e-04 0.011 24 2.500000e-06 4.361700e-04 -2.282228e-04 -3.328492e-04 4.901961e-06 0.012 25 1.916667e-05 -2.743750e-05 2.032300e-05 -6.278746e-05 -3.676471e-05 0.010 26 -3.583333e-05 -1.774375e-05 1.244790e-04 -3.000686e-04 1.666667e-04 0.008 27 -3.083333e-05 6.376743e-05 -1.485116e-04 -1.386872e-04 3.676471e-05 0.015 28 1.416667e-05 -1.073184e-04 -6.963453e-06 4.437989e-05 -3.676471e-05 0.010 29 -4.166667e-06 2.627640e-05 -8.287770e-05 2.042484e-05 -1.715686e-05 0.008 30 1.916667e-05 -3.371236e-05 1.524589e-05 4.599371e-04 -4.681373e-04 0.004 31 -2.333333e-05 1.792780e-04 -1.267242e-04 -2.050048e-04 8.578431e-05 0.010 32 -2.333333e-05 -7.139406e-05 3.326746e-04 -3.328492e-04 2.004902e-03 0.008 33 1.000000e-05 -3.717020e-05 -6.037256e-06 7.564754e-07 2.450980e-06 0.006 34 2.083333e-05 -9.554050e-05 -1.521195e-05 1.056544e-04 -8.823529e-05 0.009 35 -8.333333e-06 2.095763e-05 -6.141999e-05 -1.830671e-04 1.568627e-04 0.009 36 -5.833333e-06 1.416390e-05 -5.185251e-05 3.043553e-04 -3.333333e-04 0.009 37 2.583333e-05 2.461175e-05 -1.514697e-04 -2.118131e-05 1.470588e-05 0.011 38 -6.000000e-05 1.657242e-04 -2.155761e-04 2.713225e-04 -3.014706e-04 0.012 39 1.166667e-05 -5.778739e-05 8.216002e-05 -7.388243e-05 -1.715686e-05 0.008 40 5.833333e-06 1.463995e-04 -2.473481e-04 4.437989e-05 -3.676471e-05 0.010 41 -3.083333e-05 -6.433614e-05 1.070558e-04 -1.338962e-04 2.450980e-05 0.010 42 -3.500000e-05 6.492265e-05 -1.538894e-04 1.364177e-04 1.872549e-03 0.009 43 -1.083333e-05 -1.025334e-04 7.889651e-06 -8.800331e-05 7.598039e-05 0.011 44 -5.583333e-05 -2.309634e-04 5.418886e-04 -6.455257e-05 -4.730392e-04 0.017 45 2.333333e-05 -9.577326e-05 1.063090e-04 -7.690834e-05 -2.696078e-05 0.009 46 -6.916667e-05 2.306558e-04 -1.302205e-04 -1.275922e-04 -6.617647e-05 0.013 47 1.666667e-05 -2.073106e-05 -7.388243e-05 -9.783749e-05 8.578431e-05 0.010 48 -1.833333e-05 -3.540338e-05 1.255264e-04 -1.094368e-04 -7.352941e-06 0.007 49 5.083333e-05 -9.169876e-05 3.488758e-04 -2.698096e-05 -3.235294e-04 0.008 50 -2.750000e-05 1.171818e-05 2.328102e-05 -1.802933e-04 -1.470588e-05 0.014 51 -4.916667e-05 -9.458385e-05 2.318646e-04 -2.967905e-04 6.617647e-05 0.012 52 -3.000000e-05 3.039993e-05 2.505776e-04 1.961793e-04 -5.171569e-04 0.009 53 -2.000000e-05 8.439938e-05 3.371165e-05 -1.916404e-04 7.352941e-05 0.005 54 -4.166667e-05 6.733356e-04 -3.239218e-04 -2.539236e-04 1.691176e-03 0.015 55 -6.666667e-06 -4.739267e-05 1.045973e-05 -1.217925e-04 1.053922e-04 0.008 56 -5.333333e-05 5.980099e-05 2.514699e-04 -4.130356e-04 -4.901961e-06 0.013 57 3.666667e-05 6.065657e-05 -1.650086e-04 -1.613814e-05 -6.617647e-05 0.013 58 -1.250000e-05 -8.437592e-05 2.012613e-04 -9.884612e-05 -9.803922e-05 0.010 59 2.583333e-05 -5.693357e-05 8.966174e-06 -7.816913e-06 2.450980e-06 0.006 60 -2.500000e-06 -1.498464e-04 6.787427e-05 -1.185145e-05 -6.617647e-05 0.013 61 -1.750000e-05 -3.458305e-05 1.970764e-04 -1.853365e-04 -1.715686e-05 0.008 62 -1.750000e-05 4.257744e-05 1.703524e-05 -8.245582e-05 -1.715686e-05 0.008 63 2.916667e-05 -6.255784e-05 8.141324e-05 -1.689462e-05 -6.862745e-05 0.007 64 -1.333333e-05 2.055746e-05 -5.666777e-05 -1.669289e-04 5.637255e-05 0.013 65 1.916667e-05 5.642459e-05 -8.195150e-05 -2.319858e-05 2.205882e-05 0.004 66 -6.833333e-05 2.667442e-04 -1.014404e-04 -3.487352e-04 3.676471e-05 0.015 67 -1.750000e-05 -7.102122e-05 -4.357492e-05 1.326354e-04 -9.803922e-05 0.010 68 -6.666667e-06 -4.743632e-05 -3.185925e-05 1.043936e-04 -7.843137e-05 0.008 69 1.666667e-05 -3.039067e-05 -6.548362e-05 -3.530219e-05 2.450980e-05 0.010 70 7.500000e-06 -1.322328e-04 1.104939e-04 9.582022e-06 -1.078431e-04 0.011 71 8.333333e-07 -1.385686e-05 -8.709166e-05 4.437989e-05 -3.676471e-05 0.010 72 -1.833333e-05 -1.558031e-04 1.128846e-04 -3.757161e-05 -6.617647e-05 0.013 73 -8.333333e-06 -1.920472e-05 -9.384660e-05 -8.321230e-06 1.470588e-05 0.011 74 8.333333e-07 -1.358530e-05 5.678415e-06 -2.748527e-05 2.205882e-05 0.004 75 -2.083333e-05 -2.228144e-05 2.211236e-06 -6.051803e-05 5.392157e-05 0.007 76 1.308333e-04 -6.386117e-05 -2.246005e-04 5.000303e-04 -5.049020e-04 0.014 77 8.333333e-07 -1.006479e-04 1.295610e-04 -1.467562e-04 2.450980e-05 0.010 78 -7.166667e-05 4.098412e-05 -2.073519e-05 9.329864e-04 -1.046569e-03 0.013 79 -3.750000e-05 -9.569512e-05 2.254103e-04 -1.018720e-04 -1.078431e-04 0.011 80 -7.083333e-05 2.541460e-04 -1.647419e-04 -3.341100e-04 9.803922e-05 0.015 81 2.916667e-05 -7.220290e-05 1.039184e-04 -2.975470e-05 -6.862745e-05 0.007 82 -1.583333e-05 2.363774e-04 3.227628e-05 -2.985556e-04 -3.676471e-05 0.010 83 8.333333e-07 9.972725e-05 -4.058782e-05 -9.531590e-05 -1.715686e-05 0.008 84 -5.750000e-05 1.129128e-04 -5.890810e-05 4.034536e-06 -9.803922e-05 0.010 85 -5.833333e-06 9.139713e-05 -1.613620e-04 3.025902e-05 -2.696078e-05 0.009 86 3.250000e-05 -4.053808e-05 -1.011980e-04 1.197753e-04 -9.803922e-05 0.010 87 2.583333e-05 -4.617689e-05 -4.285724e-05 1.860930e-04 -2.303922e-04 0.011 88 1.250000e-05 4.294346e-05 -1.002718e-04 7.615186e-05 -5.882353e-05 0.006 89 -8.666667e-05 1.806738e-04 3.780437e-05 -4.498507e-04 9.803922e-05 0.015 90 5.833333e-06 -1.758105e-05 1.211331e-04 -9.884612e-05 -9.803922e-05 0.010 91 5.083333e-05 -1.914300e-04 2.161823e-04 8.999536e-04 -1.098039e-03 0.012 92 1.166667e-05 -6.744700e-05 9.055884e-05 -1.134713e-05 -7.843137e-05 0.008 93 2.333333e-05 -1.022033e-04 1.213125e-04 -8.548172e-05 -2.696078e-05 0.009 94 -2.916667e-05 -7.223398e-05 1.626762e-04 -1.575990e-04 -6.617647e-05 0.013 95 -3.750000e-05 1.148488e-04 -1.538603e-04 2.597232e-05 -2.696078e-05 0.009 96 -4.166667e-06 -4.768363e-05 7.555541e-05 -2.773743e-06 -7.843137e-05 0.008 97 -3.583333e-05 1.883193e-04 5.256534e-06 -5.093601e-04 1.691176e-04 0.014 98 -1.750000e-05 2.389081e-04 -1.643200e-04 1.477649e-04 -3.823529e-04 0.014 99 -7.500000e-06 -1.807394e-06 -2.570077e-06 3.378924e-05 -2.941176e-05 0.003 $overall 1 2 3 4 5 -0.0009025000 0.0012405435 0.0006614117 -0.0037743081 0.0066323529 6 0.9860000000 $F [,1] [,2] [,3] [,4] [,5] [1,] -0.0009117474 0.0003415072 0.001009696 -0.002803285 0.003897026 [2,] 0.0000000000 0.0012521130 0.001919693 -0.001889815 0.004804393 [3,] 0.0000000000 0.0000000000 0.000668417 -0.003145867 0.003556733 [4,] 0.0000000000 0.0000000000 0.000000000 -0.003816835 0.002890248 [5,] 0.0000000000 0.0000000000 0.000000000 0.000000000 0.006681580 > varcomp(exhier[,c(1:4,6)],diploid=FALSE) $df [1] 4 15 20 120 340 $k [,1] [,2] [,3] [,4] [,5] [1,] 100 25 12.52 3.160000 1 [2,] 0 25 12.52 3.160000 1 [3,] 0 0 12.48 3.147692 1 [4,] 0 0 0.00 3.115385 1 [5,] 0 0 0.00 0.000000 1 $res 1 2 3 4 5 1 -7.000000e-05 1.960299e-04 8.127262e-06 -0.0002955297 0.01593137 2 1.070000e-03 1.778615e-03 -3.814304e-03 0.0068768660 0.05955882 3 2.166667e-04 -7.434174e-04 1.530699e-03 -0.0021998305 0.03970588 4 -2.533333e-04 1.197912e-04 1.930564e-04 -0.0022865731 0.05514706 5 -1.700000e-04 1.747854e-06 -9.340532e-04 -0.0015563221 0.04656863 6 6.466667e-04 4.413210e-04 -2.103545e-03 0.0072783023 0.05563725 7 -7.666667e-05 2.923781e-04 -7.373890e-04 -0.0008714597 0.04534314 8 -2.600000e-04 2.105010e-03 -3.174889e-03 -0.0009007101 0.06397059 9 -7.633333e-04 2.213874e-03 7.638074e-04 0.0005870249 0.04656863 10 -7.000000e-05 5.605953e-04 -1.087191e-03 -0.0008008553 0.05612745 11 4.000000e-05 -7.953600e-04 1.263663e-03 0.0031348342 0.04215686 12 -4.333333e-05 -9.607420e-04 1.026867e-04 -0.0009097878 0.05294118 13 -4.333333e-05 -3.628163e-04 -5.533909e-05 0.0017338417 0.03161765 14 1.433333e-04 3.151956e-04 -8.986540e-04 0.0001795368 0.05147059 15 -6.500000e-04 1.018072e-03 2.176322e-05 0.0007201646 0.05000000 16 6.000000e-05 -2.225905e-04 -5.030756e-04 -0.0014362947 0.04240196 17 -3.633333e-04 2.383776e-04 -2.487834e-04 -0.0000625353 0.04068627 18 -2.266667e-04 1.025896e-03 -2.006891e-03 -0.0012880255 0.06421569 19 -3.833333e-04 9.535849e-04 -6.827288e-04 0.0027495360 0.05735294 20 -1.166667e-04 -2.116590e-04 5.835141e-04 -0.0011306786 0.02622549 $overall 1 2 3 4 5 -0.001313333 0.007963903 -0.011779525 0.009521504 0.943627451 $F [,1] [,2] [,3] [,4] [1,] -0.001385343 0.007015221 -0.005410176 0.004633393 [2,] 0.000000000 0.008388943 -0.004019264 0.006010410 [3,] 0.000000000 0.000000000 -0.012513180 -0.002398656 [4,] 0.000000000 0.000000000 0.000000000 0.009989524 > > > > > cleanEx(); ..nameEx <- "g.stats" > > ### * g.stats > > flush(stderr()); flush(stdout()) > > ### Name: g.stats > ### Title: Calculates likelihood-ratio G-statistic on contingency table > ### Aliases: g.stats > ### Keywords: univar > > ### ** Examples > > data(gtrunchier) > attach(gtrunchier) > g.stats(data.frame(Patch,L21.V)) $obs y x 1 2 3 4 5 6 7 1 0 14 0 0 0 0 12 2 0 22 0 0 0 0 2 3 0 10 2 9 0 9 0 4 0 0 2 19 0 9 0 5 0 26 0 0 0 0 0 6 0 0 0 0 24 0 0 7 0 0 0 0 30 0 0 8 0 0 0 0 26 0 0 9 0 0 0 0 26 0 0 10 0 0 0 0 30 0 0 11 0 14 0 0 0 8 0 12 0 14 0 0 0 8 0 13 1 6 0 0 0 19 0 14 1 8 0 0 1 16 0 15 0 2 0 0 26 0 0 16 0 2 0 0 28 0 0 17 0 0 0 0 30 0 0 18 0 0 0 0 30 0 0 19 0 10 0 0 16 0 0 20 0 5 23 0 0 0 0 21 0 2 23 0 5 0 0 22 0 0 0 0 28 0 0 23 0 16 12 0 0 0 0 24 0 0 0 0 30 0 0 25 0 4 0 0 20 0 0 26 0 8 0 0 8 0 0 27 0 3 0 0 17 0 0 28 0 3 0 0 7 0 0 29 0 3 0 0 7 0 0 $expe 1 2 3 4 5 6 7 [1,] 0.07065217 6.076087 2.1902174 0.9891304 13.741848 2.4375 0.4945652 [2,] 0.06521739 5.608696 2.0217391 0.9130435 12.684783 2.2500 0.4565217 [3,] 0.08152174 7.010870 2.5271739 1.1413043 15.855978 2.8125 0.5706522 [4,] 0.08152174 7.010870 2.5271739 1.1413043 15.855978 2.8125 0.5706522 [5,] 0.07065217 6.076087 2.1902174 0.9891304 13.741848 2.4375 0.4945652 [6,] 0.06521739 5.608696 2.0217391 0.9130435 12.684783 2.2500 0.4565217 [7,] 0.08152174 7.010870 2.5271739 1.1413043 15.855978 2.8125 0.5706522 [8,] 0.07065217 6.076087 2.1902174 0.9891304 13.741848 2.4375 0.4945652 [9,] 0.07065217 6.076087 2.1902174 0.9891304 13.741848 2.4375 0.4945652 [10,] 0.08152174 7.010870 2.5271739 1.1413043 15.855978 2.8125 0.5706522 [11,] 0.05978261 5.141304 1.8532609 0.8369565 11.627717 2.0625 0.4184783 [12,] 0.05978261 5.141304 1.8532609 0.8369565 11.627717 2.0625 0.4184783 [13,] 0.07065217 6.076087 2.1902174 0.9891304 13.741848 2.4375 0.4945652 [14,] 0.07065217 6.076087 2.1902174 0.9891304 13.741848 2.4375 0.4945652 [15,] 0.07608696 6.543478 2.3586957 1.0652174 14.798913 2.6250 0.5326087 [16,] 0.08152174 7.010870 2.5271739 1.1413043 15.855978 2.8125 0.5706522 [17,] 0.08152174 7.010870 2.5271739 1.1413043 15.855978 2.8125 0.5706522 [18,] 0.08152174 7.010870 2.5271739 1.1413043 15.855978 2.8125 0.5706522 [19,] 0.07065217 6.076087 2.1902174 0.9891304 13.741848 2.4375 0.4945652 [20,] 0.07608696 6.543478 2.3586957 1.0652174 14.798913 2.6250 0.5326087 [21,] 0.08152174 7.010870 2.5271739 1.1413043 15.855978 2.8125 0.5706522 [22,] 0.07608696 6.543478 2.3586957 1.0652174 14.798913 2.6250 0.5326087 [23,] 0.07608696 6.543478 2.3586957 1.0652174 14.798913 2.6250 0.5326087 [24,] 0.08152174 7.010870 2.5271739 1.1413043 15.855978 2.8125 0.5706522 [25,] 0.06521739 5.608696 2.0217391 0.9130435 12.684783 2.2500 0.4565217 [26,] 0.04347826 3.739130 1.3478261 0.6086957 8.456522 1.5000 0.3043478 [27,] 0.05434783 4.673913 1.6847826 0.7608696 10.570652 1.8750 0.3804348 [28,] 0.02717391 2.336957 0.8423913 0.3804348 5.285326 0.9375 0.1902174 [29,] 0.02717391 2.336957 0.8423913 0.3804348 5.285326 0.9375 0.1902174 $x.squared [1] 1958.605 $g.stats [1] 1376.066 > > > > cleanEx(); ..nameEx <- "g.stats.glob" > > ### * g.stats.glob > > flush(stderr()); flush(stdout()) > > ### Name: g.stats.glob > ### Title: Likelihood ratio G-statistic over loci > ### Aliases: g.stats.glob > ### Keywords: univar > > ### ** Examples > > data(gtrunchier) > attach(gtrunchier) > nperm<-99 > nobs<-length(Patch) > gglobs.o<-vector(length=(nperm+1)) > gglobs.p<-vector(length=(nperm+1)) > gglobs.l<-vector(length=(nperm+1)) > > gglobs.o[nperm+1]<-g.stats.glob(data.frame(Patch,gtrunchier[,-c(1,2)]))$g.stats > gglobs.p[nperm+1]<-g.stats.glob(data.frame(Patch,gtrunchier[,-c(1,2)]))$g.stats > gglobs.l[nperm+1]<-g.stats.glob(data.frame(Locality,gtrunchier[,-c(1,2)]))$g.stats > > for (i in 1:nperm) #careful, might take a while + { + gglobs.o[i]<-g.stats.glob(data.frame(Patch,gtrunchier[sample(Patch),-c(1,2)]))$g.stats + gglobs.p[i]<-g.stats.glob(data.frame(Patch,gtrunchier[samp.within(Locality),-c(1,2)]))$g.stats + gglobs.l[i]<-g.stats.glob(data.frame(Locality,gtrunchier[samp.between(Patch),-c(1,2)]))$g.stats + } > #p-value of first test (among patches) > p.globs.o<-sum(gglobs.o>=gglobs.o[nperm+1])/(nperm+1) > > #p-value of second test (among patches within localities) > p.globs.p<-sum(gglobs.p>=gglobs.p[nperm+1])/(nperm+1) > > #p-value of third test (among localities) > p.globs.l<-sum(gglobs.l>=gglobs.l[nperm+1])/(nperm+1) > > #Are alleles associated at random among patches > p.globs.o [1] 0.01 > > #Are alleles associated at random among patches within localities? > #Tests differentiation among patches within localities > p.globs.p [1] 0.01 > > #Are alleles associated at random among localities, keeping patches as one unit? > #Tests differentiation among localities > p.globs.l [1] 0.01 > > > > cleanEx(); ..nameEx <- "genot2al" > > ### * genot2al > > flush(stderr()); flush(stdout()) > > ### Name: genot2al > ### Title: Separates diploid genotypes in its constituant alleles > ### Aliases: genot2al > ### Keywords: manip > > ### ** Examples > > data(gtrunchier) > genot2al(gtrunchier[,4]) [1] 1 1 1 1 2 1 1 2 1 2 2 2 1 2 2 1 1 2 2 1 1 1 1 1 1 [26] 1 1 3 3 3 2 1 3 1 1 3 2 1 3 1 2 3 2 1 1 1 2 2 1 3 [51] 1 3 1 1 1 1 2 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 [76] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [101] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 NA NA 1 1 1 [126] 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 [151] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [176] 2 2 2 2 2 2 2 2 2 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 [201] 2 2 3 3 3 3 3 3 2 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 [226] 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 [251] 3 2 3 3 2 3 3 1 3 1 1 1 1 3 1 1 3 1 1 1 1 1 1 1 1 [276] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [301] 1 3 1 3 1 1 1 1 1 1 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [326] 1 1 1 1 1 3 3 2 3 3 3 3 2 3 3 3 3 2 2 2 3 2 2 3 3 [351] 3 3 2 3 3 2 2 3 3 3 3 2 3 3 3 3 3 3 2 3 1 1 1 1 3 [376] 1 1 2 1 2 2 3 1 2 2 1 1 2 2 1 1 1 1 1 1 3 1 3 3 3 [401] 3 1 3 1 1 3 3 1 3 3 3 3 2 1 1 1 2 2 1 3 1 3 1 2 3 [426] 1 2 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [451] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [476] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 NA NA 1 1 1 1 1 1 1 1 [501] 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [526] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [551] 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 [576] 3 3 3 2 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 2 3 3 [601] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 [626] 3 3 1 3 1 1 1 3 3 1 1 3 1 3 1 1 1 1 1 1 1 1 1 1 1 [651] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 3 1 [676] 1 1 1 1 1 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [701] 3 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 3 3 [726] 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 > > > > cleanEx(); ..nameEx <- "prepdata" > > ### * prepdata > > flush(stderr()); flush(stdout()) > > ### Name: prepdata > ### Title: Sort and renumber and recode levels for the hierarchical > ### analysis if necessary > ### Aliases: prepdata > ### Keywords: manip > > ### ** Examples > > f1<-rep(c("B","C","A"),each=10) > f2<-rep(c("d","i","f","g","h","e"),each=5) > prepdata(data.frame(loc=f1,patch=f2,ind=1:30)) loc patch ind 1 1 1 1 2 1 1 2 3 1 1 3 4 1 1 4 5 1 1 5 6 1 2 6 7 1 2 7 8 1 2 8 9 1 2 9 10 1 2 10 11 2 3 11 12 2 3 12 13 2 3 13 14 2 3 14 15 2 3 15 16 2 4 16 17 2 4 17 18 2 4 18 19 2 4 19 20 2 4 20 21 3 5 21 22 3 5 22 23 3 5 23 24 3 5 24 25 3 5 25 26 3 6 26 27 3 6 27 28 3 6 28 29 3 6 29 30 3 6 30 > > > > cleanEx(); ..nameEx <- "read.fstat.data" > > ### * read.fstat.data > > flush(stderr()); flush(stdout()) > > ### Name: read.fstat.data > ### Title: Reads data from a FSTAT file > ### Aliases: read.fstat.data > ### Keywords: manip > > ### ** Examples > > read.fstat.data(paste(.path.package("hierfstat"),"/data/diploid.dat",sep="",collapse=""),nloc=5) Pop loc-1 loc-2 loc-3 loc-4 loc-5 1 1 44 43 43 33 44 2 1 44 44 43 33 44 3 1 44 44 43 43 44 4 1 44 44 NA 33 44 5 1 44 44 24 34 44 6 1 44 44 NA 43 44 7 1 44 44 43 43 44 8 1 44 44 NA 43 44 9 2 44 44 33 32 44 10 2 44 33 44 43 44 11 2 44 43 44 43 44 12 2 44 44 33 33 44 13 2 44 43 44 44 44 14 2 44 44 44 22 44 15 2 44 44 43 43 44 16 2 44 44 44 44 44 17 3 44 44 44 43 44 18 3 44 44 44 44 44 19 3 44 44 43 21 44 20 3 44 44 33 43 44 21 3 44 44 43 21 44 22 4 44 44 43 44 44 23 4 44 44 43 43 44 24 4 44 44 43 43 44 25 4 44 44 43 44 44 26 4 44 44 43 44 44 27 4 44 44 44 33 44 28 4 44 44 44 44 44 29 5 44 44 44 21 44 30 5 44 44 44 33 44 31 5 44 44 43 43 44 32 5 44 44 43 43 44 33 5 44 44 44 44 44 34 5 44 44 44 43 44 35 5 44 44 43 43 44 36 5 44 44 44 NA 44 37 5 44 43 44 43 44 38 6 44 44 44 43 44 39 6 44 44 43 33 44 40 6 44 44 44 32 44 41 6 44 44 43 41 44 42 6 44 44 44 44 44 43 6 44 44 44 42 44 44 6 44 44 44 43 44 > > > > cleanEx(); ..nameEx <- "samp.between" > > ### * samp.between > > flush(stderr()); flush(stdout()) > > ### Name: samp.between > ### Title: Shuffles a sequence among groups defined by the input vector > ### Aliases: samp.between > ### Keywords: manip > > ### ** Examples > > samp.between(rep(1:4,each=4)) [1] 5 6 7 8 13 14 15 16 9 10 11 12 1 2 3 4 > #for an application see example in g.stats.glob > > > > cleanEx(); ..nameEx <- "samp.within" > > ### * samp.within > > flush(stderr()); flush(stdout()) > > ### Name: samp.within > ### Title: Shuffles a sequence within groups defined by the input vector > ### Aliases: samp.within > ### Keywords: manip > > ### ** Examples > > samp.within(rep(1:4,each=4)) [1] 2 4 3 1 5 7 6 8 11 9 12 10 15 14 16 13 > #for an application see example in g.stats.glob > > > > cleanEx(); ..nameEx <- "test.between" > > ### * test.between > > flush(stderr()); flush(stdout()) > > ### Name: test.between > ### Title: Tests the significance of the effect of test.lev on genetic > ### differentiation > ### Aliases: test.between > ### Keywords: nonparametric > > ### ** Examples > > data(gtrunchier) > attach(gtrunchier) > #test whether the locality level has a significant effect on genetic structuring > test.between(gtrunchier[,-c(1,2)],test.lev=Locality,rand.unit=Patch) $g.star [1] 1363.874 1879.495 1625.589 1774.626 1466.959 1583.018 1788.298 2171.599 [9] 2117.862 1883.630 1742.834 1841.748 1896.999 2163.769 1991.802 1738.573 [17] 1555.965 1611.851 1925.773 1596.558 1804.200 1433.937 1460.055 1760.100 [25] 1660.830 2018.963 2336.734 1717.867 1825.242 2197.448 1838.244 2216.688 [33] 1785.569 2509.914 1867.707 1767.743 2027.361 1764.266 2158.649 2090.782 [41] 2006.169 2092.316 2057.390 1689.447 1880.392 1696.793 2164.371 1890.733 [49] 1825.808 1588.977 1929.160 1755.196 2044.704 1423.107 2215.967 2497.740 [57] 1536.947 1324.047 1888.083 1730.845 1419.411 1995.346 2395.064 2126.215 [65] 1692.130 1700.085 2141.627 2157.086 1622.553 1588.206 1534.143 1825.127 [73] 2103.393 1560.107 1888.253 2132.172 1861.298 1770.781 1910.273 1807.942 [81] 1841.063 2047.398 1774.034 1722.902 1447.337 1880.368 1667.279 1436.332 [89] 1726.230 2052.053 1647.696 2299.840 1557.977 1985.278 1929.795 1283.091 [97] 1621.835 1976.283 1613.152 6370.574 $p.val [1] 0.01 > > > > cleanEx(); ..nameEx <- "test.between.within" > > ### * test.between.within > > flush(stderr()); flush(stdout()) > > ### Name: test.between.within > ### Title: Tests the significance of the effect of test.lev on genetic > ### differentiation > ### Aliases: test.between.within > ### Keywords: nonparametric > > ### ** Examples > > data(yangex) > attach(yangex) > #tests for the effect of spop on genetic structure > test.between.within(data.frame(genot),within=pop,test=spop,rand=sspop) $g.star [1] 102.99000 82.16776 108.96904 113.39613 91.66608 98.06094 75.58665 [8] 80.69557 88.57825 107.03960 90.21554 115.66679 89.89022 83.45727 [15] 101.49592 91.98633 96.78221 69.43542 86.74087 123.83519 97.78972 [22] 81.78740 80.73431 95.65331 91.62337 100.38248 100.16105 105.39733 [29] 79.05379 100.56349 85.75067 86.24751 77.36835 106.10437 101.30950 [36] 91.66711 79.01793 105.00900 76.85723 92.45827 102.22966 106.82361 [43] 96.33593 82.50146 82.25770 73.89678 91.32422 110.17662 100.27039 [50] 100.62398 82.15942 96.07545 98.22984 85.71313 98.84264 114.02160 [57] 106.93083 76.88536 97.10156 86.12621 84.52159 98.79326 88.40416 [64] 96.29824 79.67227 89.84671 85.23419 90.43587 106.01510 104.04577 [71] 97.35431 96.69475 110.42973 107.38878 109.17092 99.48662 72.86131 [78] 77.94068 95.28950 81.91146 77.66722 97.38841 86.58838 94.34541 [85] 103.25611 93.47672 85.84477 70.67013 83.28143 96.17490 103.28268 [92] 84.51355 103.71731 91.38353 96.57748 91.77098 83.90603 82.93396 [99] 97.06282 95.27060 $p.val [1] 0.49 > > > > cleanEx(); ..nameEx <- "test.g" > > ### * test.g > > flush(stderr()); flush(stdout()) > > ### Name: test.g > ### Title: Tests the significance of the effect of level on genetic > ### differentiation > ### Aliases: test.g > ### Keywords: nonparametric > > ### ** Examples > > data(gtrunchier) > attach(gtrunchier) > test.g(gtrunchier[,-c(1,2)],Locality) $g.star [1] 226.4816 226.2232 236.2465 245.0292 246.2474 272.0489 262.2080 [8] 241.9524 277.8260 207.6108 283.5525 224.5163 207.7151 283.3422 [15] 267.2308 314.5683 282.9554 306.5480 351.4194 209.6343 322.4516 [22] 282.9228 300.8524 252.0905 288.1475 338.2840 293.8043 273.8609 [29] 231.9741 246.2505 379.8039 236.6479 303.3696 260.6187 248.9199 [36] 278.1317 283.0131 206.6910 270.9777 205.1275 270.6076 215.2051 [43] 299.0240 194.7452 230.6204 264.8291 234.2284 247.6478 260.7362 [50] 314.8237 274.2481 235.4602 222.7745 295.5003 233.7590 279.2926 [57] 281.5568 240.1275 246.5155 381.2285 250.6412 239.5402 252.4227 [64] 255.7780 226.2962 314.8156 287.1103 262.8202 252.7426 248.8418 [71] 217.0070 287.3438 356.5122 264.8273 204.3587 303.5236 237.6482 [78] 251.9188 224.3738 226.2283 297.1832 390.8884 261.4319 335.7711 [85] 329.5671 292.3707 346.5682 314.2701 299.6983 314.8042 255.1618 [92] 194.6668 260.6041 252.2331 251.6641 248.3894 251.8908 305.7339 [99] 334.5060 6370.5745 $p.val [1] 0.01 > > > > cleanEx(); ..nameEx <- "test.within" > > ### * test.within > > flush(stderr()); flush(stdout()) > > ### Name: test.within > ### Title: Tests the significance of the effect of inner.level on genetic > ### differentiation within blocks defined by outer.level > ### Aliases: test.within > ### Keywords: nonparametric > > ### ** Examples > > data(gtrunchier) > attach(gtrunchier) > #tests whether the patch level has a significant effect on genetic structure > test.within(gtrunchier[,-c(1,2)],within=Locality,test.lev=Patch) $g.star [1] 6754.486 6792.148 6794.197 6778.932 6785.083 6783.604 6732.133 6758.529 [9] 6727.485 6719.877 6755.473 6790.996 6814.802 6718.809 6729.895 6828.947 [17] 6768.830 6772.992 6775.713 6833.504 6732.221 6776.072 6738.400 6815.301 [25] 6745.150 6748.067 6794.257 6818.836 6768.437 6780.348 6800.202 6745.235 [33] 6683.423 6795.812 6861.082 6750.820 6794.537 6826.200 6775.348 6710.772 [41] 6843.533 6701.315 6755.749 6808.834 6814.893 6740.497 6776.004 6807.685 [49] 6736.792 6783.839 6715.470 6832.259 6815.887 6727.516 6847.464 6800.502 [57] 6799.052 6804.848 6705.278 6786.443 6766.348 6767.586 6695.981 6804.614 [65] 6757.110 6752.470 6818.837 6767.668 6741.470 6738.674 6743.894 6725.244 [73] 6761.450 6801.599 6742.021 6850.004 6743.942 6813.125 6782.845 6731.862 [81] 6803.149 6767.029 6758.645 6747.454 6795.017 6816.209 6806.067 6799.847 [89] 6798.914 6756.083 6827.772 6808.274 6774.721 6895.160 6727.576 6656.622 [97] 6807.488 6793.575 6793.289 7941.267 $p.val [1] 0.01 > > > > cleanEx(); ..nameEx <- "varcomp" > > ### * varcomp > > flush(stderr()); flush(stdout()) > > ### Name: varcomp > ### Title: Estimates variance components for each allele of a locus > ### Aliases: varcomp > ### Keywords: univar > > ### ** Examples > > #load data set > data(gtrunchier) > attach(gtrunchier) > # > varcomp(data.frame(Locality,Patch,L21.V)) $df [1] 5 23 339 368 $k [,1] [,2] [,3] [,4] [1,] 121.6348 25.61814 2 1 [2,] 0.0000 25.27772 2 1 [3,] 0.0000 0.00000 2 1 [4,] 0.0000 0.00000 0 1 $res 1 2 3 4 1 4.710426e-05 -4.707914e-05 2.774736e-06 0.002717391 2 3.225138e-02 4.929319e-02 7.798995e-02 0.027173913 3 2.333692e-02 2.975503e-02 2.109248e-02 0.008152174 4 4.481889e-03 1.411145e-02 1.390011e-02 0.005434783 5 1.825764e-01 5.847996e-02 2.165225e-02 0.020380435 6 3.585824e-02 5.614791e-03 4.597235e-02 0.004076087 7 3.891868e-04 6.599498e-03 6.567080e-03 0.005434783 $overall 1 2 3 4 0.27894108 0.16380684 0.18717700 0.07336957 $F [,1] [,2] [,3] [1,] 0.3966206 0.6295342 0.8956773 [2,] 0.0000000 0.3860151 0.8271027 [3,] 0.0000000 0.0000000 0.7184013 > > > > cleanEx(); ..nameEx <- "varcomp.glob" > > ### * varcomp.glob > > flush(stderr()); flush(stdout()) > > ### Name: varcomp.glob > ### Title: Estimate variance components and hierarchical F-statistics over > ### all loci > ### Aliases: varcomp.glob > ### Keywords: univar > > ### ** Examples > > #load data set > data(gtrunchier) > attach(gtrunchier) > varcomp.glob(data.frame(Locality,Patch),gtrunchier[,-c(1,2)]) $loc 1 2 3 4 L21.V 0.2789411 0.16380684 0.18717700 0.07336957 L37.J 0.5039916 0.01278892 0.14414494 0.06267030 L20.B 0.3412817 0.05652272 0.09967252 0.05163043 L29.V 0.3716982 0.10611487 0.25460676 0.06478873 L36.B 0.3898175 0.12303525 0.28453463 0.05177112 L16.J 0.4157106 0.10364310 0.24608029 0.07377049 $overall Locality Patch Ind Error 2.3014407 0.5659117 1.2162161 0.3780006 $F Locality Patch Ind Total 0.5158366 0.6426780 0.9152763 Locality 0.0000000 0.2619806 0.8250101 Patch 0.0000000 0.0000000 0.7628926 > > > > cleanEx(); ..nameEx <- "yangex" > > ### * yangex > > flush(stderr()); flush(stdout()) > > ### Name: yangex > ### Title: Example data set from Yang (1998) appendix > ### Aliases: yangex > ### Keywords: datasets > > ### ** Examples > > data(yangex) > varcomp(yangex) $df [1] 3 7 20 201 232 $k [,1] [,2] [,3] [,4] [,5] [1,] 108.1322 46.43326 21.44105 2 1 [2,] 0.0000 38.55323 17.66791 2 1 [3,] 0.0000 0.00000 12.81128 2 1 [4,] 0.0000 0.00000 0.00000 2 1 [5,] 0.0000 0.00000 0.00000 0 1 $res 1 2 3 4 5 1 -1.195687e-03 2.581376e-03 -0.0012047119 -2.705473e-05 0.06034483 2 1.510756e-03 6.139308e-05 -0.0017643603 2.568551e-03 0.09267241 3 -9.114122e-04 -8.095354e-04 0.0048818206 -1.091282e-02 0.10560345 4 -2.104080e-05 3.136568e-03 -0.0016839347 -9.578093e-03 0.12284483 5 -7.630459e-04 2.165916e-03 -0.0024657278 -6.064910e-03 0.12931034 6 -1.233766e-04 -4.122509e-03 0.0083887803 -2.471645e-03 0.10775862 7 -5.212581e-04 -2.628762e-04 0.0002271596 -3.734017e-03 0.11853448 8 -2.362714e-04 -7.892154e-04 -0.0006734281 2.482683e-03 0.12284483 9 -1.682637e-04 -1.045355e-03 0.0021972820 -3.028016e-03 0.04525862 $overall 1 2 3 4 5 -0.0024295989 0.0009157613 0.0079028796 -0.0307653246 0.9051724138 $F [,1] [,2] [,3] [,4] [1,] -0.002758412 -0.001718715 0.007253713 -0.02767528 [2,] 0.000000000 0.001036837 0.009984583 -0.02484833 [3,] 0.000000000 0.000000000 0.008957033 -0.02591203 [4,] 0.000000000 0.000000000 0.000000000 -0.03518421 > #the k matrix should be the same as matrix (A2) in Yang's appendix, p. 956 > > > > ### *