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> ### > 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("hier.part-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('hier.part') Loading required package: gtools > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .CheckExEnv) > cleanEx(); ..nameEx <- "all.regs" > > ### * all.regs > > flush(stderr()); flush(stdout()) > > ### Name: all.regs > ### Title: Goodness of fit measures for a regression hierarchy > ### Aliases: all.regs > > > ### ** Examples > > #linear regression of log(electrical conductivity) in streams > #against seven independent variables describing catchment > #characteristics (from Hatt et al. 2004) > data(urbanwq) > env <- urbanwq[,2:8] > all.regs(urbanwq$lec, env, fam = "gaussian", gof = "Rsqu", + print.vars = TRUE) regressions done: formatting results variable.combination gof 1 Theta 0.00000000 2 fimp 0.59047318 3 sconn 0.82682075 4 sdensep 0.01098174 5 unsealden 0.39635905 6 fcarea 0.12282709 7 selev 0.62445471 8 amgeast 0.33033972 9 fimp sconn 0.83410378 10 fimp sdensep 0.59758915 11 fimp unsealden 0.62061433 12 fimp fcarea 0.65284362 13 fimp selev 0.77159448 14 fimp amgeast 0.60702109 15 sconn sdensep 0.82683793 16 sconn unsealden 0.82691037 17 sconn fcarea 0.83582029 18 sconn selev 0.85403927 19 sconn amgeast 0.84727070 20 sdensep unsealden 0.41798147 21 sdensep fcarea 0.13980912 22 sdensep selev 0.76026824 23 sdensep amgeast 0.33080645 24 unsealden fcarea 0.39744819 25 unsealden selev 0.66503431 26 unsealden amgeast 0.52650125 27 fcarea selev 0.67608730 28 fcarea amgeast 0.43330271 29 selev amgeast 0.64633406 30 fimp sconn sdensep 0.83447868 31 fimp sconn unsealden 0.83411192 32 fimp sconn fcarea 0.83965414 33 fimp sconn selev 0.85551898 34 fimp sconn amgeast 0.84727151 35 fimp sdensep unsealden 0.62061622 36 fimp sdensep fcarea 0.65949970 37 fimp sdensep selev 0.82672399 38 fimp sdensep amgeast 0.62672410 39 fimp unsealden fcarea 0.65445206 40 fimp unsealden selev 0.77226833 41 fimp unsealden amgeast 0.62512912 42 fimp fcarea selev 0.77811174 43 fimp fcarea amgeast 0.66312305 44 fimp selev amgeast 0.82026934 45 sconn sdensep unsealden 0.82691040 46 sconn sdensep fcarea 0.84141253 47 sconn sdensep selev 0.87751308 48 sconn sdensep amgeast 0.84785650 49 sconn unsealden fcarea 0.83932285 50 sconn unsealden selev 0.85513956 51 sconn unsealden amgeast 0.84985538 52 sconn fcarea selev 0.85490360 53 sconn fcarea amgeast 0.85107746 54 sconn selev amgeast 0.87685251 55 sdensep unsealden fcarea 0.43348171 56 sdensep unsealden selev 0.81801707 57 sdensep unsealden amgeast 0.55827372 58 sdensep fcarea selev 0.76851361 59 sdensep fcarea amgeast 0.50536622 60 sdensep selev amgeast 0.77107209 61 unsealden fcarea selev 0.73969961 62 unsealden fcarea amgeast 0.53654123 63 unsealden selev amgeast 0.68316625 64 fcarea selev amgeast 0.67713148 65 fimp sconn sdensep unsealden 0.83451931 66 fimp sconn sdensep fcarea 0.84541929 67 fimp sconn sdensep selev 0.87865868 68 fimp sconn sdensep amgeast 0.84793008 69 fimp sconn unsealden fcarea 0.84176310 70 fimp sconn unsealden selev 0.85633390 71 fimp sconn unsealden amgeast 0.85010056 72 fimp sconn fcarea selev 0.85662169 73 fimp sconn fcarea amgeast 0.85116200 74 fimp sconn selev amgeast 0.88236321 75 fimp sdensep unsealden fcarea 0.66297682 76 fimp sdensep unsealden selev 0.84039075 77 fimp sdensep unsealden amgeast 0.62908864 78 fimp sdensep fcarea selev 0.82701685 79 fimp sdensep fcarea amgeast 0.66414800 80 fimp sdensep selev amgeast 0.85116957 81 fimp unsealden fcarea selev 0.78348135 82 fimp unsealden fcarea amgeast 0.66312339 83 fimp unsealden selev amgeast 0.82956916 84 fimp fcarea selev amgeast 0.85200153 85 sconn sdensep unsealden fcarea 0.84338543 86 sconn sdensep unsealden selev 0.87900342 87 sconn sdensep unsealden amgeast 0.85317612 88 sconn sdensep fcarea selev 0.87755498 89 sconn sdensep fcarea amgeast 0.85162358 90 sconn sdensep selev amgeast 0.89200389 91 sconn unsealden fcarea selev 0.85535421 92 sconn unsealden fcarea amgeast 0.85816222 93 sconn unsealden selev amgeast 0.88318976 94 sconn fcarea selev amgeast 0.88611089 95 sdensep unsealden fcarea selev 0.83628377 96 sdensep unsealden fcarea amgeast 0.61132790 97 sdensep unsealden selev amgeast 0.82521005 98 sdensep fcarea selev amgeast 0.77285776 99 unsealden fcarea selev amgeast 0.74013871 100 fimp sconn sdensep unsealden fcarea 0.84631087 101 fimp sconn sdensep unsealden selev 0.88052535 102 fimp sconn sdensep unsealden amgeast 0.85779318 103 fimp sconn sdensep fcarea selev 0.87866729 104 fimp sconn sdensep fcarea amgeast 0.85162457 105 fimp sconn sdensep selev amgeast 0.89525987 106 fimp sconn unsealden fcarea selev 0.85679323 107 fimp sconn unsealden fcarea amgeast 0.86058475 108 fimp sconn unsealden selev amgeast 0.89708584 109 fimp sconn fcarea selev amgeast 0.89557323 110 fimp sdensep unsealden fcarea selev 0.84575908 111 fimp sdensep unsealden fcarea amgeast 0.66461591 112 fimp sdensep unsealden selev amgeast 0.85137732 113 fimp sdensep fcarea selev amgeast 0.86367597 114 fimp unsealden fcarea selev amgeast 0.85322499 115 sconn sdensep unsealden fcarea selev 0.87916623 116 sconn sdensep unsealden fcarea amgeast 0.85818567 117 sconn sdensep unsealden selev amgeast 0.89224695 118 sconn sdensep fcarea selev amgeast 0.89515882 119 sconn unsealden fcarea selev amgeast 0.88779139 120 sdensep unsealden fcarea selev amgeast 0.83642968 121 fimp sconn sdensep unsealden fcarea selev 0.88093891 122 fimp sconn sdensep unsealden fcarea amgeast 0.86223879 123 fimp sconn sdensep unsealden selev amgeast 0.89900950 124 fimp sconn sdensep fcarea selev amgeast 0.90101494 125 fimp sconn unsealden fcarea selev amgeast 0.90231612 126 fimp sdensep unsealden fcarea selev amgeast 0.86447417 127 sconn sdensep unsealden fcarea selev amgeast 0.89516613 128 fimp sconn sdensep unsealden fcarea selev amgeast 0.90324171 > > #logistic regression of an amphipod species occurrence in > #streams against four independent variables describing > #catchment characteristics (from Walsh et al. 2004) > data(amphipod) > env1 <- amphipod[,2:5] > all.regs(amphipod$australis, env1, fam = "binomial", + gof = "logLik", print.vars = TRUE) regressions done: formatting results variable.combination gof 1 Theta -36.68245 2 fimp -30.29727 3 fconn -25.58116 4 densep -36.31127 5 unseal -31.24099 6 fimp fconn -25.53568 7 fimp densep -28.91701 8 fimp unseal -29.44569 9 fconn densep -24.13048 10 fconn unseal -25.02936 11 densep unseal -31.21361 12 fimp fconn densep -23.40491 13 fimp fconn unseal -24.66242 14 fimp densep unseal -28.49188 15 fconn densep unseal -23.40579 16 fimp fconn densep unseal -23.17074 > > > > cleanEx(); ..nameEx <- "hier.part" > > ### * hier.part > > flush(stderr()); flush(stdout()) > > ### Name: hier.part > ### Title: Goodness of fit calculation and hierarchical partitioning > ### Aliases: hier.part > > > ### ** Examples > > #linear regression of log(electrical conductivity) in streams > #against seven independent variables describing catchment > #characteristics (from Hatt et al. 2004) > data(urbanwq) > env <- urbanwq[,2:8] > hier.part(urbanwq$lec, env, fam = "gaussian", gof = "Rsqu") $gfs [1] 0.00000000 0.59047318 0.82682075 0.01098174 0.39635905 0.12282709 [7] 0.62445471 0.33033972 0.83410378 0.59758915 0.62061433 0.65284362 [13] 0.77159448 0.60702109 0.82683793 0.82691037 0.83582029 0.85403927 [19] 0.84727070 0.41798147 0.13980912 0.76026824 0.33080645 0.39744819 [25] 0.66503431 0.52650125 0.67608730 0.43330271 0.64633406 0.83447868 [31] 0.83411192 0.83965414 0.85551898 0.84727151 0.62061622 0.65949970 [37] 0.82672399 0.62672410 0.65445206 0.77226833 0.62512912 0.77811174 [43] 0.66312305 0.82026934 0.82691040 0.84141253 0.87751308 0.84785650 [49] 0.83932285 0.85513956 0.84985538 0.85490360 0.85107746 0.87685251 [55] 0.43348171 0.81801707 0.55827372 0.76851361 0.50536622 0.77107209 [61] 0.73969961 0.53654123 0.68316625 0.67713148 0.83451931 0.84541929 [67] 0.87865868 0.84793008 0.84176310 0.85633390 0.85010056 0.85662169 [73] 0.85116200 0.88236321 0.66297682 0.84039075 0.62908864 0.82701685 [79] 0.66414800 0.85116957 0.78348135 0.66312339 0.82956916 0.85200153 [85] 0.84338543 0.87900342 0.85317612 0.87755498 0.85162358 0.89200389 [91] 0.85535421 0.85816222 0.88318976 0.88611089 0.83628377 0.61132790 [97] 0.82521005 0.77285776 0.74013871 0.84631087 0.88052535 0.85779318 [103] 0.87866729 0.85162457 0.89525987 0.85679323 0.86058475 0.89708584 [109] 0.89557323 0.84575908 0.66461591 0.85137732 0.86367597 0.85322499 [115] 0.87916623 0.85818567 0.89224695 0.89515882 0.88779139 0.83642968 [121] 0.88093891 0.86223879 0.89900950 0.90101494 0.90231612 0.86447417 [127] 0.89516613 0.90324171 $IJ I J Total fimp 0.16018583 0.430287348 0.59047318 sconn 0.28970489 0.537115864 0.82682075 sdensep 0.02090086 -0.009919125 0.01098174 unsealden 0.09181298 0.304546072 0.39635905 fcarea 0.03609565 0.086731439 0.12282709 selev 0.21589289 0.408561821 0.62445471 amgeast 0.08864861 0.241691110 0.33033972 $I.perc I fimp 17.734548 sconn 32.073905 sdensep 2.313983 unsealden 10.164829 fcarea 3.996234 selev 23.902006 amgeast 9.814495 > > #logistic regression of an amphipod species occurrence in > #streams against four independent variables describing > #catchment characteristics (from Walsh et al. 2004) > data(amphipod) > env1 <- amphipod[,2:5] > hier.part(amphipod$australis, env1, fam = "binomial", gof = "logLik") $gfs [1] -36.68245 -30.29727 -25.58116 -36.31127 -31.24099 -25.53568 -28.91701 [8] -29.44569 -24.13048 -25.02936 -31.21361 -23.40491 -24.66242 -28.49188 [15] -23.40579 -23.17074 $IJ I J Total fimp 2.742498 3.6426854 6.3851833 fconn 7.543709 3.5575841 11.1012927 densep 1.096251 -0.7250743 0.3711766 unseal 2.129250 3.3122091 5.4414589 $I.perc I fimp 20.297197 fconn 55.830906 densep 8.113341 unseal 15.758555 > > > > cleanEx(); ..nameEx <- "partition" > > ### * partition > > flush(stderr()); flush(stdout()) > > ### Name: partition > ### Title: Hierarchical partitioning from a list of goodness of fit > ### measures > ### Aliases: partition > > > ### ** Examples > > #linear regression of log(electrical conductivity) in streams > #against seven independent variables describing catchment > #characteristics (from Hatt et al. 2004) > data(urbanwq) > env <- urbanwq[,2:8] > gofs <- all.regs(urbanwq$lec, env, fam = "gaussian", + gof = "Rsqu", print.vars = TRUE) regressions done: formatting results > partition(gofs, pcan = 7, var.names = names(urbanwq[,2,8])) $gfs variable.combination gof 1 Theta 0.00000000 2 fimp 0.59047318 3 sconn 0.82682075 4 sdensep 0.01098174 5 unsealden 0.39635905 6 fcarea 0.12282709 7 selev 0.62445471 8 amgeast 0.33033972 9 fimp sconn 0.83410378 10 fimp sdensep 0.59758915 11 fimp unsealden 0.62061433 12 fimp fcarea 0.65284362 13 fimp selev 0.77159448 14 fimp amgeast 0.60702109 15 sconn sdensep 0.82683793 16 sconn unsealden 0.82691037 17 sconn fcarea 0.83582029 18 sconn selev 0.85403927 19 sconn amgeast 0.84727070 20 sdensep unsealden 0.41798147 21 sdensep fcarea 0.13980912 22 sdensep selev 0.76026824 23 sdensep amgeast 0.33080645 24 unsealden fcarea 0.39744819 25 unsealden selev 0.66503431 26 unsealden amgeast 0.52650125 27 fcarea selev 0.67608730 28 fcarea amgeast 0.43330271 29 selev amgeast 0.64633406 30 fimp sconn sdensep 0.83447868 31 fimp sconn unsealden 0.83411192 32 fimp sconn fcarea 0.83965414 33 fimp sconn selev 0.85551898 34 fimp sconn amgeast 0.84727151 35 fimp sdensep unsealden 0.62061622 36 fimp sdensep fcarea 0.65949970 37 fimp sdensep selev 0.82672399 38 fimp sdensep amgeast 0.62672410 39 fimp unsealden fcarea 0.65445206 40 fimp unsealden selev 0.77226833 41 fimp unsealden amgeast 0.62512912 42 fimp fcarea selev 0.77811174 43 fimp fcarea amgeast 0.66312305 44 fimp selev amgeast 0.82026934 45 sconn sdensep unsealden 0.82691040 46 sconn sdensep fcarea 0.84141253 47 sconn sdensep selev 0.87751308 48 sconn sdensep amgeast 0.84785650 49 sconn unsealden fcarea 0.83932285 50 sconn unsealden selev 0.85513956 51 sconn unsealden amgeast 0.84985538 52 sconn fcarea selev 0.85490360 53 sconn fcarea amgeast 0.85107746 54 sconn selev amgeast 0.87685251 55 sdensep unsealden fcarea 0.43348171 56 sdensep unsealden selev 0.81801707 57 sdensep unsealden amgeast 0.55827372 58 sdensep fcarea selev 0.76851361 59 sdensep fcarea amgeast 0.50536622 60 sdensep selev amgeast 0.77107209 61 unsealden fcarea selev 0.73969961 62 unsealden fcarea amgeast 0.53654123 63 unsealden selev amgeast 0.68316625 64 fcarea selev amgeast 0.67713148 65 fimp sconn sdensep unsealden 0.83451931 66 fimp sconn sdensep fcarea 0.84541929 67 fimp sconn sdensep selev 0.87865868 68 fimp sconn sdensep amgeast 0.84793008 69 fimp sconn unsealden fcarea 0.84176310 70 fimp sconn unsealden selev 0.85633390 71 fimp sconn unsealden amgeast 0.85010056 72 fimp sconn fcarea selev 0.85662169 73 fimp sconn fcarea amgeast 0.85116200 74 fimp sconn selev amgeast 0.88236321 75 fimp sdensep unsealden fcarea 0.66297682 76 fimp sdensep unsealden selev 0.84039075 77 fimp sdensep unsealden amgeast 0.62908864 78 fimp sdensep fcarea selev 0.82701685 79 fimp sdensep fcarea amgeast 0.66414800 80 fimp sdensep selev amgeast 0.85116957 81 fimp unsealden fcarea selev 0.78348135 82 fimp unsealden fcarea amgeast 0.66312339 83 fimp unsealden selev amgeast 0.82956916 84 fimp fcarea selev amgeast 0.85200153 85 sconn sdensep unsealden fcarea 0.84338543 86 sconn sdensep unsealden selev 0.87900342 87 sconn sdensep unsealden amgeast 0.85317612 88 sconn sdensep fcarea selev 0.87755498 89 sconn sdensep fcarea amgeast 0.85162358 90 sconn sdensep selev amgeast 0.89200389 91 sconn unsealden fcarea selev 0.85535421 92 sconn unsealden fcarea amgeast 0.85816222 93 sconn unsealden selev amgeast 0.88318976 94 sconn fcarea selev amgeast 0.88611089 95 sdensep unsealden fcarea selev 0.83628377 96 sdensep unsealden fcarea amgeast 0.61132790 97 sdensep unsealden selev amgeast 0.82521005 98 sdensep fcarea selev amgeast 0.77285776 99 unsealden fcarea selev amgeast 0.74013871 100 fimp sconn sdensep unsealden fcarea 0.84631087 101 fimp sconn sdensep unsealden selev 0.88052535 102 fimp sconn sdensep unsealden amgeast 0.85779318 103 fimp sconn sdensep fcarea selev 0.87866729 104 fimp sconn sdensep fcarea amgeast 0.85162457 105 fimp sconn sdensep selev amgeast 0.89525987 106 fimp sconn unsealden fcarea selev 0.85679323 107 fimp sconn unsealden fcarea amgeast 0.86058475 108 fimp sconn unsealden selev amgeast 0.89708584 109 fimp sconn fcarea selev amgeast 0.89557323 110 fimp sdensep unsealden fcarea selev 0.84575908 111 fimp sdensep unsealden fcarea amgeast 0.66461591 112 fimp sdensep unsealden selev amgeast 0.85137732 113 fimp sdensep fcarea selev amgeast 0.86367597 114 fimp unsealden fcarea selev amgeast 0.85322499 115 sconn sdensep unsealden fcarea selev 0.87916623 116 sconn sdensep unsealden fcarea amgeast 0.85818567 117 sconn sdensep unsealden selev amgeast 0.89224695 118 sconn sdensep fcarea selev amgeast 0.89515882 119 sconn unsealden fcarea selev amgeast 0.88779139 120 sdensep unsealden fcarea selev amgeast 0.83642968 121 fimp sconn sdensep unsealden fcarea selev 0.88093891 122 fimp sconn sdensep unsealden fcarea amgeast 0.86223879 123 fimp sconn sdensep unsealden selev amgeast 0.89900950 124 fimp sconn sdensep fcarea selev amgeast 0.90101494 125 fimp sconn unsealden fcarea selev amgeast 0.90231612 126 fimp sdensep unsealden fcarea selev amgeast 0.86447417 127 sconn sdensep unsealden fcarea selev amgeast 0.89516613 128 fimp sconn sdensep unsealden fcarea selev amgeast 0.90324171 $IJ I J Total 1 0.16018583 0.430287348 0.59047318 2 0.28970489 0.537115864 0.82682075 3 0.02090086 -0.009919125 0.01098174 4 0.09181298 0.304546072 0.39635905 5 0.03609565 0.086731439 0.12282709 6 0.21589289 0.408561821 0.62445471 7 0.08864861 0.241691110 0.33033972 $I.perc I 1 17.734548 2 32.073905 3 2.313983 4 10.164829 5 3.996234 6 23.902006 7 9.814495 > > #hierarchical partitioning of logistic and linear regression > #goodness of fit measures from Chevan and Sutherland (1991) > data(chevan) > partition(chevan$chisq, pcan = 4) $gfs [1] 207.5 191.6 113.8 68.5 185.5 104.4 58.6 173.3 27.7 104.8 53.4 20.2 [13] 97.0 46.3 19.3 13.6 $IJ I J Total 1 -9.891667 -6.008333 -15.9 2 -61.391667 -32.308333 -93.7 3 -109.591667 -29.408333 -139.0 4 -13.025000 -8.975000 -22.0 $I.perc I 1 5.101427 2 31.661509 3 56.519684 4 6.717380 > partition(chevan$rsqu, pcan = 4) $gfs [1] 0.0000 0.0100 0.0600 0.0860 0.0140 0.0650 0.0900 0.0220 0.1070 0.0650 [11] 0.0940 0.1100 0.0700 0.0097 0.1110 0.1140 $IJ I J Total 1 -0.0016916667 0.011691667 0.010 2 0.0587416667 0.001258333 0.060 3 0.0563083333 0.029691667 0.086 4 0.0006416667 0.013358333 0.014 $I.perc I 1 -1.4839181 2 51.5277778 3 49.3932749 4 0.5628655 > > > > cleanEx(); ..nameEx <- "rand.hp" > > ### * rand.hp > > flush(stderr()); flush(stdout()) > > ### Name: rand.hp > ### Title: Randomization test for hierarchical partitioning > ### Aliases: rand.hp > > > ### ** Examples > > #linear regression of log(electrical conductivity) in streams > #against four independent variables describing catchment > #characteristics (from Hatt et al. 2004) > data(urbanwq) > env <- urbanwq[,2:5] > rand.hp(urbanwq$lec, env, fam = "gaussian", gof = "Rsqu")$Iprobs Please wait: running 100 randomizations Obs Z.score sig95 fimp 0.24 1.46 sconn 0.46 3.12 * sdensep 0.01 -0.80 unsealden 0.14 0.83 > > #logistic regression of an amphipod species occurrence in > #streams against four independent variables describing > #catchment characteristics (from Walsh et al. 2004) > data(amphipod) > env1 <- amphipod[,2:5] > rand.hp(amphipod$australis, env1, fam = "binomial", + gof = "logLik")$Iprobs Please wait: running 100 randomizations Obs Z.score sig95 fimp 2.74 2.26 * fconn 7.54 8.78 * densep 1.10 0.51 unseal 2.13 3.74 * > > > > ### *