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("polspline-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('polspline') > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .CheckExEnv) > cleanEx(); ..nameEx <- "beta.polyclass" > > ### * beta.polyclass > > flush(stderr()); flush(stdout()) > > ### Name: beta.polyclass > ### Title: Polyclass: polychotomous regression and multiple classification > ### Aliases: beta.polyclass > ### Keywords: smooth nonlinear > > ### ** Examples > > data(iris) > fit.iris <- polyclass(iris[,5], iris[,1:4]) warning - model size was reduced > beta.polyclass(fit.iris) > > > > cleanEx(); ..nameEx <- "clspec" > > ### * clspec > > flush(stderr()); flush(stdout()) > > ### Name: clspec > ### Title: Lspec: logspline estimation of a spectral distribution > ### Aliases: clspec dlspec plspec rlspec > ### Keywords: ts smooth > > ### ** Examples > > data(co2) > co2.detrend <- lm(co2~c(1:length(co2)))$residuals > fit <- lspec(co2.detrend) > clspec(0:12,fit) [1] 6.7817938 6.0529438 4.2934039 2.1641521 0.3314690 -0.8276671 [7] -1.2230327 -0.8610954 0.2640960 2.0614426 4.1516680 5.8612366 [13] 6.5106901 > plspec((0:314)/100, fit) [1] 3.390897 3.409057 4.475383 4.492632 4.509038 4.524401 4.538592 4.551521 [9] 4.563165 4.573529 4.582664 4.590637 4.597541 4.603475 4.608544 4.612849 [17] 4.616491 4.619561 4.622142 4.624310 4.626128 4.627656 4.628940 4.630024 [25] 4.630939 4.631718 4.632383 4.632955 4.633450 4.633883 4.634265 4.634604 [33] 4.634911 4.635190 4.635448 4.635690 4.635921 4.636144 4.636363 4.636583 [41] 4.636808 4.637042 4.637292 4.637564 4.637867 4.638213 4.638618 4.639104 [49] 4.639704 4.640467 4.641445 4.642664 4.644063 6.601564 6.602957 6.604198 [57] 6.605247 6.606097 6.606771 6.607301 6.607721 6.608062 6.608350 6.608606 [65] 6.608844 6.609075 6.609301 6.609526 6.609747 6.609967 6.610184 6.610399 [73] 6.610612 6.610823 6.611031 6.611237 6.611442 6.611644 6.611844 6.612042 [81] 6.612238 6.612432 6.612624 6.612815 6.613003 6.613190 6.613375 6.613558 [89] 6.613739 6.613918 6.614096 6.614272 6.614447 6.614619 6.614790 6.614960 [97] 6.615128 6.615294 6.615459 6.615623 6.615784 6.615945 6.616104 6.616261 [105] 6.616417 6.763807 6.763960 6.764112 6.764263 6.764412 6.764560 6.764707 [113] 6.764852 6.764996 6.765139 6.765281 6.765422 6.765561 6.765699 6.765836 [121] 6.765972 6.766107 6.766240 6.766373 6.766504 6.766635 6.766764 6.766892 [129] 6.767020 6.767146 6.767271 6.767395 6.767519 6.767641 6.767763 6.767883 [137] 6.768003 6.768121 6.768239 6.768356 6.768472 6.768587 6.768701 6.768815 [145] 6.768928 6.769039 6.769150 6.769261 6.769370 6.769479 6.769587 6.769694 [153] 6.769800 6.769906 6.770011 6.770115 6.770218 6.770321 6.770423 6.770525 [161] 6.770626 6.770726 6.770825 6.770924 6.771022 6.771120 6.771217 6.771313 [169] 6.771409 6.771504 6.771598 6.771692 6.771786 6.771879 6.771971 6.772063 [177] 6.772154 6.772244 6.772334 6.772424 6.772513 6.772602 6.772690 6.772777 [185] 6.772864 6.772951 6.773037 6.773123 6.773208 6.773292 6.773377 6.773461 [193] 6.773544 6.773627 6.773709 6.773791 6.773873 6.773954 6.774035 6.774116 [201] 6.774196 6.774275 6.774355 6.774434 6.774512 6.774590 6.774668 6.774745 [209] 6.774823 6.774899 6.774976 6.775052 6.775127 6.775203 6.775278 6.775353 [217] 6.775427 6.775501 6.775575 6.775648 6.775722 6.775795 6.775867 6.775940 [225] 6.776012 6.776083 6.776155 6.776226 6.776297 6.776368 6.776438 6.776508 [233] 6.776578 6.776648 6.776718 6.776787 6.776856 6.776924 6.776993 6.777061 [241] 6.777129 6.777197 6.777265 6.777332 6.777400 6.777467 6.777534 6.777600 [249] 6.777667 6.777733 6.777799 6.777865 6.777931 6.777996 6.778062 6.778127 [257] 6.778192 6.778257 6.778322 6.778386 6.778451 6.778515 6.778579 6.778643 [265] 6.778707 6.778771 6.778834 6.778898 6.778961 6.779024 6.779087 6.779150 [273] 6.779213 6.779276 6.779338 6.779401 6.779463 6.779526 6.779588 6.779650 [281] 6.779712 6.779774 6.779836 6.779897 6.779959 6.780021 6.780082 6.780144 [289] 6.780205 6.780266 6.780327 6.780389 6.780450 6.780511 6.780572 6.780633 [297] 6.780694 6.780754 6.780815 6.780876 6.780937 6.780997 6.781058 6.781119 [305] 6.781179 6.781240 6.781300 6.781361 6.781421 6.781482 6.781542 6.781603 [313] 6.781663 6.781724 6.781784 > dlspec((0:314)/100, fit) $d [1] 1.821346068 1.805687876 1.759598048 1.686426435 1.591209328 1.479513432 [7] 1.356962627 1.228853005 1.099868479 0.973903762 0.853987464 0.742288630 [13] 0.640185272 0.548372671 0.466991394 0.395758817 0.334092452 0.281217776 [19] 0.236256960 0.198297796 0.166444057 0.139849695 0.117739796 0.099421258 [25] 0.084285942 0.071808631 0.061541696 0.053107918 0.046192535 0.040535237 [31] 0.035922597 0.032181222 0.029171773 0.026783930 0.024932319 0.023553400 [37] 0.022603351 0.022056978 0.021907805 0.022169560 0.022879491 0.024104181 [43] 0.025949015 0.028573110 0.032212765 0.037218429 0.044113682 0.053690756 [49] 0.067167991 0.086229798 0.110051540 0.132670561 0.145031115 0.144329437 [55] 0.132822372 0.114822137 0.094718235 0.075737443 0.059631023 0.046960633 [61] 0.037576056 0.031032696 0.026868482 0.024540090 0.023350517 0.022809993 [67] 0.022542056 0.022304665 0.022070718 0.021840158 0.021612930 0.021388981 [73] 0.021168258 0.020950707 0.020736279 0.020524923 0.020316590 0.020111231 [79] 0.019908798 0.019709246 0.019512528 0.019318600 0.019127416 0.018938934 [85] 0.018753111 0.018569905 0.018389275 0.018211180 0.018035581 0.017862439 [91] 0.017691716 0.017523373 0.017357374 0.017193682 0.017032262 0.016873079 [97] 0.016716098 0.016561286 0.016408609 0.016258034 0.016109530 0.015963064 [103] 0.015818606 0.015676126 0.015535593 0.015396979 0.015260253 0.015125388 [109] 0.014992356 0.014861129 0.014731680 0.014603983 0.014478012 0.014353742 [115] 0.014231146 0.014110200 0.013990881 0.013873163 0.013757024 0.013642440 [121] 0.013529390 0.013417849 0.013307797 0.013199212 0.013092072 0.012986358 [127] 0.012882047 0.012779121 0.012677559 0.012577341 0.012478450 0.012380865 [133] 0.012284568 0.012189542 0.012095768 0.012003228 0.011911905 0.011821782 [139] 0.011732843 0.011645071 0.011558449 0.011472963 0.011388595 0.011305331 [145] 0.011223155 0.011142053 0.011062011 0.010983012 0.010905044 0.010828092 [151] 0.010752143 0.010677183 0.010603199 0.010530177 0.010458106 0.010386972 [157] 0.010316762 0.010247465 0.010179069 0.010111562 0.010044932 0.009979167 [163] 0.009914257 0.009850190 0.009786955 0.009724542 0.009662940 0.009602139 [169] 0.009542128 0.009482898 0.009424438 0.009366739 0.009309791 0.009253585 [175] 0.009198111 0.009143361 0.009089325 0.009035995 0.008983362 0.008931417 [181] 0.008880152 0.008829559 0.008779630 0.008730356 0.008681729 0.008633743 [187] 0.008586388 0.008539659 0.008493547 0.008448045 0.008403146 0.008358843 [193] 0.008315129 0.008271997 0.008229441 0.008187453 0.008146028 0.008105159 [199] 0.008064840 0.008025065 0.007985827 0.007947121 0.007908940 0.007871279 [205] 0.007834132 0.007797495 0.007761360 0.007725723 0.007690578 0.007655920 [211] 0.007621745 0.007588046 0.007554819 0.007522059 0.007489762 0.007457922 [217] 0.007426535 0.007395596 0.007365100 0.007335044 0.007305423 0.007276233 [223] 0.007247469 0.007219128 0.007191204 0.007163695 0.007136596 0.007109904 [229] 0.007083614 0.007057723 0.007032227 0.007007123 0.006982407 0.006958076 [235] 0.006934126 0.006910554 0.006887356 0.006864530 0.006842071 0.006819978 [241] 0.006798247 0.006776874 0.006755858 0.006735195 0.006714882 0.006694916 [247] 0.006675295 0.006656016 0.006637077 0.006618474 0.006600205 0.006582269 [253] 0.006564662 0.006547381 0.006530426 0.006513792 0.006497479 0.006481484 [259] 0.006465804 0.006450439 0.006435384 0.006420640 0.006406203 0.006392071 [265] 0.006378244 0.006364718 0.006351493 0.006338566 0.006325936 0.006313601 [271] 0.006301559 0.006289810 0.006278350 0.006267179 0.006256296 0.006245698 [277] 0.006235385 0.006225355 0.006215607 0.006206140 0.006196951 0.006188041 [283] 0.006179408 0.006171051 0.006162968 0.006155159 0.006147623 0.006140359 [289] 0.006133365 0.006126641 0.006120186 0.006114000 0.006108080 0.006102428 [295] 0.006097041 0.006091919 0.006087062 0.006082468 0.006078138 0.006074071 [301] 0.006070266 0.006066722 0.006063440 0.006060419 0.006057658 0.006055157 [307] 0.006052916 0.006050935 0.006049213 0.006047749 0.006046545 0.006045599 [313] 0.006044912 0.006044483 0.006044313 $modfreq [1] 0.00000000 0.01342561 0.01342561 0.02685122 0.04027683 0.05370244 [7] 0.05370244 0.06712805 0.08055366 0.09397927 0.09397927 0.10740488 [13] 0.12083049 0.13425610 0.13425610 0.14768171 0.16110732 0.17453293 [19] 0.17453293 0.18795853 0.20138414 0.21480975 0.21480975 0.22823536 [25] 0.24166097 0.25508658 0.25508658 0.26851219 0.28193780 0.29536341 [31] 0.29536341 0.30878902 0.32221463 0.33564024 0.33564024 0.34906585 [37] 0.36249146 0.37591707 0.37591707 0.38934268 0.40276829 0.41619390 [43] 0.41619390 0.42961951 0.44304512 0.45647073 0.45647073 0.46989634 [49] 0.48332195 0.48332195 0.49674756 0.51017317 0.52359878 0.52359878 [55] 0.53702439 0.55044999 0.56387560 0.56387560 0.57730121 0.59072682 [61] 0.60415243 0.60415243 0.61757804 0.63100365 0.64442926 0.64442926 [67] 0.65785487 0.67128048 0.68470609 0.68470609 0.69813170 0.71155731 [73] 0.72498292 0.72498292 0.73840853 0.75183414 0.76525975 0.76525975 [79] 0.77868536 0.79211097 0.80553658 0.80553658 0.81896219 0.83238780 [85] 0.84581341 0.84581341 0.85923902 0.87266463 0.88609024 0.88609024 [91] 0.89951585 0.91294145 0.92636706 0.92636706 0.93979267 0.95321828 [97] 0.96664389 0.96664389 0.98006950 0.99349511 0.99349511 1.00692072 [103] 1.02034633 1.03377194 1.03377194 1.04719755 1.06062316 1.07404877 [109] 1.07404877 1.08747438 1.10089999 1.11432560 1.11432560 1.12775121 [115] 1.14117682 1.15460243 1.15460243 1.16802804 1.18145365 1.19487926 [121] 1.19487926 1.20830487 1.22173048 1.23515609 1.23515609 1.24858170 [127] 1.26200731 1.27543291 1.27543291 1.28885852 1.30228413 1.31570974 [133] 1.31570974 1.32913535 1.34256096 1.35598657 1.35598657 1.36941218 [139] 1.38283779 1.39626340 1.39626340 1.40968901 1.42311462 1.43654023 [145] 1.43654023 1.44996584 1.46339145 1.46339145 1.47681706 1.49024267 [151] 1.50366828 1.50366828 1.51709389 1.53051950 1.54394511 1.54394511 [157] 1.55737072 1.57079633 1.58422194 1.58422194 1.59764755 1.61107316 [163] 1.62449877 1.62449877 1.63792437 1.65134998 1.66477559 1.66477559 [169] 1.67820120 1.69162681 1.70505242 1.70505242 1.71847803 1.73190364 [175] 1.74532925 1.74532925 1.75875486 1.77218047 1.78560608 1.78560608 [181] 1.79903169 1.81245730 1.82588291 1.82588291 1.83930852 1.85273413 [187] 1.86615974 1.86615974 1.87958535 1.89301096 1.90643657 1.90643657 [193] 1.91986218 1.93328779 1.93328779 1.94671340 1.96013901 1.97356462 [199] 1.97356462 1.98699023 2.00041583 2.01384144 2.01384144 2.02726705 [205] 2.04069266 2.05411827 2.05411827 2.06754388 2.08096949 2.09439510 [211] 2.09439510 2.10782071 2.12124632 2.13467193 2.13467193 2.14809754 [217] 2.16152315 2.17494876 2.17494876 2.18837437 2.20179998 2.21522559 [223] 2.21522559 2.22865120 2.24207681 2.25550242 2.25550242 2.26892803 [229] 2.28235364 2.29577925 2.29577925 2.30920486 2.32263047 2.33605608 [235] 2.33605608 2.34948169 2.36290730 2.37633290 2.37633290 2.38975851 [241] 2.40318412 2.41660973 2.41660973 2.43003534 2.44346095 2.44346095 [247] 2.45688656 2.47031217 2.48373778 2.48373778 2.49716339 2.51058900 [253] 2.52401461 2.52401461 2.53744022 2.55086583 2.56429144 2.56429144 [259] 2.57771705 2.59114266 2.60456827 2.60456827 2.61799388 2.63141949 [265] 2.64484510 2.64484510 2.65827071 2.67169632 2.68512193 2.68512193 [271] 2.69854754 2.71197315 2.72539876 2.72539876 2.73882436 2.75224997 [277] 2.76567558 2.76567558 2.77910119 2.79252680 2.80595241 2.80595241 [283] 2.81937802 2.83280363 2.84622924 2.84622924 2.85965485 2.87308046 [289] 2.88650607 2.88650607 2.89993168 2.91335729 2.91335729 2.92678290 [295] 2.94020851 2.95363412 2.95363412 2.96705973 2.98048534 2.99391095 [301] 2.99391095 3.00733656 3.02076217 3.03418778 3.03418778 3.04761339 [307] 3.06103900 3.07446461 3.07446461 3.08789022 3.10131582 3.11474143 [313] 3.11474143 3.12816704 3.14159265 $m [1] NA 1.0484739 1.0484739 0.0000000 0.0000000 0.0000000 0.0000000 [8] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [15] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [22] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [29] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [36] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [43] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [50] 0.0000000 0.0000000 0.0000000 1.9560434 1.9560434 0.0000000 0.0000000 [57] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [64] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [71] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [78] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [85] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [92] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [99] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [106] 0.1472354 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [113] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [120] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [127] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [134] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [141] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [148] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [155] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [162] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [169] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [176] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [183] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [190] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [197] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [204] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [211] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [218] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [225] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [232] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [239] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [246] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [253] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [260] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [267] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [274] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [281] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [288] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [295] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [302] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [309] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 > rlspec(length(co2),fit) [1] -2.01645246 -1.17056896 -0.81423247 -2.17896468 -4.14905295 -5.76465561 [7] -6.26887413 -5.36140978 -3.72297262 -2.60586848 -2.32985996 -1.92636696 [13] -2.27356866 -1.59326724 -1.79192375 -2.88285921 -4.34480578 -6.08369375 [19] -6.36223699 -5.81279366 -3.93350467 -2.39214465 -2.50233943 -2.72246742 [25] -2.06875435 -1.72893461 -1.20163034 -2.37881666 -4.85549505 -6.21079073 [31] -6.38951549 -4.96203339 -2.88679993 -2.08133775 -1.75638651 -1.74369125 [37] -2.06557497 -1.41279206 -1.46559405 -2.29134236 -4.17854603 -5.75324303 [43] -6.01833255 -5.25016515 -4.16765949 -3.34701852 -3.00009185 -3.16641044 [49] -3.16098836 -3.00314653 -2.83309469 -4.13211381 -6.04849753 -7.17113890 [55] -7.60651780 -7.09700085 -4.98538202 -3.77088898 -3.39731885 -4.27686086 [61] -4.01484087 -3.63110880 -3.46792486 -4.43539519 -5.96205777 -7.74374819 [67] -7.37008112 -6.02911693 -4.63320918 -3.58373496 -3.24288545 -3.32903821 [73] -3.36114019 -2.74268094 -2.65698154 -3.37931903 -5.04579091 -6.64157873 [79] -6.48689245 -5.63064687 -4.04831669 -2.96101050 -3.10867984 -2.91946015 [85] -2.91474159 -2.22563578 -1.86595241 -3.05798470 -4.37045028 -6.06683239 [91] -6.12903320 -4.88821069 -3.18250070 -2.05919996 -1.83485060 -2.26203183 [97] -2.25132559 -1.91608112 -1.32672975 -2.32109148 -3.81776595 -4.93585254 [103] -5.40720450 -4.84797221 -3.13581556 -2.63785712 -2.05019482 -2.82202031 [109] -2.34632785 -1.71809243 -0.85668474 -1.51903724 -3.34495668 -5.03848494 [115] -5.45435592 -4.40084035 -2.87689090 -1.65760941 -1.87879122 -2.19158885 [121] -2.06030490 -1.37697190 -1.02281929 -2.20965057 -3.70886306 -5.04843600 [127] -5.05956311 -4.03955754 -2.01932465 -0.94935988 -1.12873865 -1.63017196 [133] -1.16998729 -0.69670180 0.06925670 -0.65955534 -2.47372556 -3.87171775 [139] -4.23455919 -3.16983823 -1.07398181 0.05662763 -0.04306391 -0.74048488 [145] -0.48403194 0.79720807 0.91202999 0.21923051 -2.40006266 -3.71120176 [151] -3.68316932 -2.64588949 -1.03917528 0.09328754 0.27193144 -0.15212769 [157] 0.04255846 0.40819369 0.45588037 -0.50416276 -2.26398898 -3.93318840 [163] -4.06389796 -3.00311665 -1.22159512 -0.06437439 -0.47864345 0.23053122 [169] -0.05986512 0.73592610 1.57329008 0.87354469 -0.92005715 -2.56654178 [175] -2.87672484 -1.93328658 -0.08485845 0.66199652 1.19990652 1.11922940 [181] 1.16054096 2.48293303 2.77586779 1.89378118 -0.16192035 -1.72933220 [187] -2.03605395 -0.46332024 1.00006687 2.14324905 2.27476981 2.30617854 [193] 2.99799224 3.87492623 4.52147359 3.79042692 1.62841733 0.20289391 [199] -0.31690489 0.85624829 2.69246631 3.80758635 3.81499108 3.78503198 [205] 3.92020761 5.05917509 4.96207471 4.41572696 2.71257944 1.03785208 [211] 0.26865034 1.94081805 2.52087228 4.11822294 4.86088274 4.11756413 [217] 5.02421310 5.51266443 5.97218116 5.33356180 3.51978888 1.10814062 [223] 0.94341843 1.87121685 4.07795401 5.00793254 4.86520889 5.10618168 [229] 5.39273500 6.22540584 6.14079163 5.65344940 3.72450999 2.20872493 [235] 1.29223998 2.31505499 4.52645877 4.99486087 5.19017875 5.28590355 [241] 5.42727387 6.37731772 6.16556669 5.12034591 2.54650662 1.69419145 [247] 0.98216150 1.60881724 3.16366582 4.34909644 4.07370147 4.34851332 [253] 4.51689447 5.41427675 5.84116071 4.77282032 3.22436416 1.36219358 [259] 0.58932564 1.63571568 3.66100167 4.73821620 5.26951256 5.44597447 [265] 6.13210909 6.38194349 6.45031240 5.27178036 3.38428909 1.62993193 [271] 1.28572260 2.39319332 3.38955845 4.47244163 4.91567190 4.51401859 [277] 4.87923836 5.31230508 5.57685707 4.60862133 2.52763468 0.57653146 [283] -0.13329286 1.94745742 2.96251783 3.83734008 4.21039216 4.39317111 [289] 4.52444910 5.61389620 5.42894695 4.65919355 3.02653228 1.47621103 [295] 0.76224910 2.23677644 3.75876863 4.96417207 5.07486927 4.89119809 [301] 4.81681080 5.13052944 5.71011712 4.76642370 2.93119534 0.66134640 [307] 0.95441368 1.93092970 3.06614448 4.46315667 4.48326200 3.95204915 [313] 4.23391793 5.19465520 5.00980795 4.51343711 2.14735429 0.44571097 [319] 0.06502586 1.40856798 3.09971139 3.89527231 4.09495191 4.47088986 [325] 4.51496205 5.07854594 5.06730651 4.15530204 2.15972791 1.07316030 [331] 0.09369363 1.10871313 2.67486691 4.06839561 4.41947890 4.71084596 [337] 4.64321668 5.55981581 5.47796715 4.16037234 1.94150035 0.49032569 [343] 0.20465711 1.00211206 2.66264960 3.85915937 4.26200487 3.90878629 [349] 3.51233177 3.85542807 4.43526480 3.51023497 1.63798363 -0.33492503 [355] -1.55454231 -0.25833063 1.02258641 1.73367668 1.78975524 1.74155752 [361] 1.73877961 2.26816062 3.02093955 2.14850547 -0.42311141 -2.16331200 [367] -2.90650232 -1.52508559 0.04284023 0.91068608 0.91213179 1.04563772 [373] 1.48112480 2.08069428 2.74485318 1.77873864 0.14671793 -1.25488195 [379] -2.38461971 -1.30551379 0.25122888 1.28526062 1.57799337 1.55638068 [385] 1.79649264 1.97086878 2.07007030 1.27442496 -0.38105142 -2.13865455 [391] -2.15223408 -1.96103844 -0.25946140 0.43154428 0.99038765 0.42622773 [397] 0.91659306 1.47468735 1.95280811 0.49394797 -1.20895788 -3.49209652 [403] -3.22854298 -2.71262329 -1.28104403 -0.14000159 -0.34951432 -0.19901909 [409] 0.42781339 1.07972517 1.01949285 0.39502943 -1.90650433 -4.27386386 [415] -3.90840034 -3.76030174 -2.12850561 -0.82090208 -0.39564952 -0.09989117 [421] -0.24396404 0.16485221 0.31154148 -0.99393343 -2.42821360 -4.65433374 [427] -5.23023452 -3.53327060 -2.34092300 -1.54204337 -1.33379138 -1.82134717 [433] -1.87308222 -0.83990390 -0.43747206 -1.13707906 -2.49154755 -4.48491260 [439] -5.01616433 -4.08648449 -3.03934167 -2.37372992 -2.27766419 -2.30443756 [445] -2.20634342 -1.80705294 -1.16388630 -1.47982842 -3.38571172 -5.37046783 [451] -5.52978335 -5.13909591 -3.49890164 -2.57997199 -2.11010113 -2.51173386 [457] -2.19242853 -1.52998095 -1.28026819 -2.09843693 -4.22641072 -6.69710874 [463] -6.93414247 -5.56265717 -3.96027419 -3.27276255 -2.93004295 -3.16362032 > > > > cleanEx(); ..nameEx <- "cpolyclass" > > ### * cpolyclass > > flush(stderr()); flush(stdout()) > > ### Name: cpolyclass > ### Title: Polyclass: polychotomous regression and multiple classification > ### Aliases: cpolyclass ppolyclass rpolyclass > ### Keywords: smooth nonlinear > > ### ** Examples > > data(iris) > fit.iris <- polyclass(iris[,5], iris[,1:4]) warning - model size was reduced > class.iris <- cpolyclass(iris[,1:4], fit.iris) > table(class.iris, iris[,5]) class.iris setosa versicolor virginica 1 50 0 0 2 0 47 3 3 0 3 47 > prob.setosa <- ppolyclass(1, iris[,1:4], fit.iris) > prob.correct <- ppolyclass(iris[,5], iris[,1:4], fit.iris) > rpolyclass(100, iris[64,1:4], fit.iris) [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [38] 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 2 2 2 2 2 2 2 2 2 2 2 2 [75] 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 2 > > > > cleanEx(); ..nameEx <- "design.polymars" > > ### * design.polymars > > flush(stderr()); flush(stdout()) > > ### Name: design.polymars > ### Title: Polymars: multivariate adaptive polynomial spline regression > ### Aliases: design.polymars > ### Keywords: smooth nonlinear > > ### ** Examples > > data(state) > state.pm <- polymars(state.region, state.x77, knots = 15, classify = TRUE, gcv = 1) > desmat <- design.polymars(state.pm, state.x77) > # compute traditional summary of the fit for the first class > summary(lm(((state.region=="Northeast")*1) ~ desmat -1)) Call: lm(formula = ((state.region == "Northeast") * 1) ~ desmat - 1) Residuals: Min 1Q Median 3Q Max -0.48038 -0.15150 -0.03209 0.15423 0.61920 Coefficients: Estimate Std. Error t value Pr(>|t|) desmat1 -1.126e+01 4.366e+00 -2.579 0.014016 * desmat2 2.856e-02 1.814e-02 1.574 0.124019 desmat3 1.688e-02 2.078e-02 0.812 0.421796 desmat4 9.753e-03 9.071e-03 1.075 0.289267 desmat5 1.313e-01 6.662e-02 1.972 0.056167 . desmat6 -4.784e-01 1.599e-01 -2.992 0.004912 ** desmat7 5.156e-01 2.947e-01 1.749 0.088513 . desmat8 -7.859e-06 1.386e-06 -5.671 1.75e-06 *** desmat9 7.083e-06 1.718e-06 4.124 0.000202 *** desmat10 -8.877e-05 1.563e-04 -0.568 0.573379 desmat11 -4.027e-05 3.176e-05 -1.268 0.212752 desmat12 5.097e-05 2.429e-05 2.099 0.042741 * desmat13 -2.906e-01 4.068e-01 -0.714 0.479453 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2528 on 37 degrees of freedom Multiple R-Squared: 0.7372, Adjusted R-squared: 0.6449 F-statistic: 7.984 on 13 and 37 DF, p-value: 2.951e-07 > > > > cleanEx(); ..nameEx <- "dhare" > > ### * dhare > > flush(stderr()); flush(stdout()) > > ### Name: dhare > ### Title: Hare: hazard regression > ### Aliases: dhare hhare phare qhare rhare > ### Keywords: distribution smooth survival > > ### ** Examples > > fit <- hare(testhare[,1], testhare[,2], testhare[,3:8]) > dhare(0:10, testhare[117,3:8], fit) [1] 0.002266272 0.152470629 0.287974746 0.129592312 0.059521788 0.029785596 [7] 0.019564779 0.018506767 0.017505968 0.016559291 0.015663807 > hhare(0:10, testhare[1:11,3:8], fit) [1] 0.001552441 0.070309445 0.269493885 0.145167330 0.054224645 0.066780728 [7] 0.046037322 0.070017965 0.040146964 0.021650719 0.093524484 > phare(10, testhare[1:25,3:8], fit) [1] 0.5123317 0.3576253 0.7043853 0.5752294 0.3917284 0.6544619 0.3770862 [8] 0.4427595 0.4709139 0.2394673 0.7899024 0.3089936 0.2685960 0.8907437 [15] 0.5178234 0.7405306 0.8203810 0.5384936 0.6828870 0.3309411 0.4975142 [22] 0.2711855 0.6696873 0.4463595 0.4508892 > qhare((1:19)/20, testhare[117,3:8], fit) [1] 0.911168 1.217716 1.466174 1.682398 1.875730 2.052263 2.228593 [8] 2.440543 2.705280 3.051709 3.539549 4.326802 6.098353 8.871119 [15] 12.150606 16.164372 21.339017 28.632270 41.100168 > rhare(10, testhare[117,3:8], fit) [1] 1.932056 2.317089 3.844393 30.172758 1.689233 28.344924 39.279899 [8] 6.662019 5.130468 0.995287 > > > > cleanEx(); ..nameEx <- "dheft" > > ### * dheft > > flush(stderr()); flush(stdout()) > > ### Name: dheft > ### Title: Heft: hazard estimation with flexible tails > ### Aliases: dheft hheft pheft qheft rheft > ### Keywords: distribution smooth survival > > ### ** Examples > > fit <- heft(testhare[,1],testhare[,2]) > dheft(0:10,fit) [1] 0.00000000 0.11950647 0.13341915 0.08061400 0.04657502 0.03742625 [7] 0.03376190 0.03045046 0.02745516 0.02479511 0.02245161 > hheft(0:10,fit) [1] 0.00000000 0.12882896 0.16789413 0.11758136 0.07449399 0.06406334 [7] 0.06153308 0.05894438 0.05629845 0.05371861 0.05126222 > pheft(0:10,fit) [1] 0.00000000 0.07236336 0.20533766 0.31439809 0.37478149 0.41579301 [7] 0.45132115 0.48340360 0.51232840 0.53842604 0.56202427 > qheft((1:19)/20,fit) [1] 0.8031157 1.2216213 1.5946184 1.9601485 2.3522512 2.8311603 [7] 3.5203602 4.5900333 5.9750102 7.5837148 9.5526890 11.9731408 [13] 15.1415017 19.0423923 25.3238716 33.0818519 49.3013025 79.9747980 [19] 175.0957234 > rheft(10,fit) [1] 2.4875099 3.9436471 10.5267101 88.5843586 1.9726737 78.3688640 [7] 155.9703420 15.8850430 13.7662233 0.9095447 > > > > cleanEx(); ..nameEx <- "dlogspline" > > ### * dlogspline > > flush(stderr()); flush(stdout()) > > ### Name: dlogspline > ### Title: Logspline Density Estimation > ### Aliases: dlogspline plogspline qlogspline rlogspline > ### Keywords: distribution smooth > > ### ** Examples > > x <- rnorm(100) > fit <- logspline(x) > qq <- qlogspline((1:99)/100, fit) > plot(qnorm((1:99)/100), qq) # qq plot of the fitted density > pp <- plogspline((-250:250)/100, fit) > plot((-250:250)/100, pp, type = "l") > lines((-250:250)/100,pnorm((-250:250)/100)) # asses the fit of the distribution > dd <- dlogspline((-250:250)/100, fit) > plot((-250:250)/100, dd, type = "l") > lines((-250:250)/100, dnorm((-250:250)/100)) # asses the fit of the density > rr <- rlogspline(100, fit) # random sample from fit > > > > cleanEx(); ..nameEx <- "doldlogspline" > > ### * doldlogspline > > flush(stderr()); flush(stdout()) > > ### Name: doldlogspline > ### Title: Logspline Density Estimation - 1992 version > ### Aliases: doldlogspline poldlogspline qoldlogspline roldlogspline > ### Keywords: distribution smooth > > ### ** Examples > > x <- rnorm(100) > fit <- oldlogspline(x) > qq <- qoldlogspline((1:99)/100, fit) > plot(qnorm((1:99)/100), qq) # qq plot of the fitted density > pp <- poldlogspline((-250:250)/100, fit) > plot((-250:250)/100, pp, type = "l") > lines((-250:250)/100, pnorm((-250:250)/100)) # asses the fit of the distribution > dd <- doldlogspline((-250:250)/100, fit) > plot((-250:250)/100, dd, type = "l") > lines((-250:250)/100, dnorm((-250:250)/100)) # asses the fit of the density > rr <- roldlogspline(100, fit) # random sample from fit > > > > cleanEx(); ..nameEx <- "hare" > > ### * hare > > flush(stderr()); flush(stdout()) > > ### Name: hare > ### Title: Hare: hazard regression > ### Aliases: hare > ### Keywords: distribution smooth survival > > ### ** Examples > > fit <- hare(testhare[,1], testhare[,2], testhare[,3:8]) > > > > cleanEx(); ..nameEx <- "heft" > > ### * heft > > flush(stderr()); flush(stdout()) > > ### Name: heft > ### Title: Heft: hazard estimation with flexible tails > ### Aliases: heft > ### Keywords: distribution smooth survival > > ### ** Examples > > fit1 <- heft(testhare[,1], testhare[,2]) > # modify tail behavior > fit2 <- heft(testhare[,1], testhare[,2], leftlog = FALSE, rightlog = FALSE, + leftlin = TRUE) > fit3 <- heft(testhare[,1], testhare[,2], penalty = 0) # select largest model > > > > cleanEx(); ..nameEx <- "logspline" > > ### * logspline > > flush(stderr()); flush(stdout()) > > ### Name: logspline > ### Title: Logspline Density Estimation > ### Aliases: logspline > ### Keywords: distribution smooth > > ### ** Examples > > y <- rnorm(100) > fit <- logspline(y) > plot(fit) > # as (4 == length(-2, -1, 0, 1, 2) -1), this forces these initial knots, > # and does no knot selection > fit <- logspline(y, knots = c(-2, -1, 0, 1, 2), maxknots = 4, penalty = 0) > > > > cleanEx(); ..nameEx <- "lspec" > > ### * lspec > > flush(stderr()); flush(stdout()) > > ### Name: lspec > ### Title: Lspec: logspline estimation of a spectral distribution > ### Aliases: lspec > ### Keywords: ts smooth > > ### ** Examples > > data(co2) > co2.detrend <- unstrip(lm(co2~c(1:length(co2)))$residuals) > fit <- lspec(co2.detrend) > > > > cleanEx(); ..nameEx <- "oldlogspline" > > ### * oldlogspline > > flush(stderr()); flush(stdout()) > > ### Name: oldlogspline > ### Title: Logspline Density Estimation - 1992 version > ### Aliases: oldlogspline > ### Keywords: distribution smooth > > ### ** Examples > > # A simple example > y <- rnorm(100) > fit <- oldlogspline(y) > plot(fit) > # An example involving censoring and a lower bound > y <- rlnorm(1000) > censoring <- rexp(1000) * 4 > delta <- 1 * (y <= censoring) > y[delta == 0] <- censoring[delta == 0] > fit <- oldlogspline(y[delta == 1], y[delta == 0], lbound = 0) > > > > cleanEx(); ..nameEx <- "plot.hare" > > ### * plot.hare > > flush(stderr()); flush(stdout()) > > ### Name: plot.hare > ### Title: Hare: hazard regression > ### Aliases: plot.hare > ### Keywords: distribution smooth survival > > ### ** Examples > > fit <- hare(testhare[,1], testhare[,2], testhare[,3:8]) > # hazard curve for covariates like case 1 > plot(fit, testhare[1,3:8], what = "h") > # survival function as a function of covariate 2, for covariates as case 1 at t=3 > plot(fit, testhare[1,3:8], which = 2, what = "s", time = 3) > > > > cleanEx(); ..nameEx <- "plot.heft" > > ### * plot.heft > > flush(stderr()); flush(stdout()) > > ### Name: plot.heft > ### Title: Heft: hazard estimation with flexible tails > ### Aliases: plot.heft > ### Keywords: distribution smooth survival > > ### ** Examples > > fit1 <- heft(testhare[,1], testhare[,2]) > plot(fit1, what = "h") > # modify tail behavior > fit2 <- heft(testhare[,1], testhare[,2], leftlog = FALSE, rightlog = FALSE, + leftlin = TRUE) > plot(fit2, what = "h", add = TRUE,lty = 2) > fit3 <- heft(testhare[,1], testhare[,2], penalty = 0) # select largest model > plot(fit3, what = "h", add = TRUE,lty = 3) > > > > cleanEx(); ..nameEx <- "plot.logspline" > > ### * plot.logspline > > flush(stderr()); flush(stdout()) > > ### Name: plot.logspline > ### Title: Logspline Density Estimation > ### Aliases: plot.logspline > ### Keywords: distribution smooth > > ### ** Examples > > y <- rnorm(100) > fit <- logspline(y) > plot(fit) > > > > cleanEx(); ..nameEx <- "plot.lspec" > > ### * plot.lspec > > flush(stderr()); flush(stdout()) > > ### Name: plot.lspec > ### Title: Lspec: logspline estimation of a spectral distribution > ### Aliases: plot.lspec > ### Keywords: ts smooth > > ### ** Examples > > data(co2) > co2.detrend <- lm(co2~c(1:length(co2)))$residuals > fit <- lspec(co2.detrend) > plot(fit) > > > > cleanEx(); ..nameEx <- "plot.oldlogspline" > > ### * plot.oldlogspline > > flush(stderr()); flush(stdout()) > > ### Name: plot.oldlogspline > ### Title: Logspline Density Estimation - 1992 version > ### Aliases: plot.oldlogspline > ### Keywords: distribution smooth > > ### ** Examples > > y <- rnorm(100) > fit <- oldlogspline(y) > plot(fit) > > > > cleanEx(); ..nameEx <- "plot.polyclass" > > ### * plot.polyclass > > flush(stderr()); flush(stdout()) > > ### Name: plot.polyclass > ### Title: Polyclass: polychotomous regression and multiple classification > ### Aliases: plot.polyclass > ### Keywords: smooth nonlinear > > ### ** Examples > > data(iris) > fit.iris <- polyclass(iris[,5], iris[,1:4]) warning - model size was reduced > plot(fit.iris, iris[64,1:4], which=c(3,4), data=2, what=1) > plot(fit.iris,iris[64,1:4], which=c(3,4), what=5) > plot(fit.iris,iris[64,1:4], which=4, what=7) > > > > cleanEx(); ..nameEx <- "plot.polymars" > > ### * plot.polymars > > flush(stderr()); flush(stdout()) > > ### Name: plot.polymars > ### Title: Polymars: multivariate adaptive polynomial spline regression > ### Aliases: plot.polymars > ### Keywords: smooth nonlinear > > ### ** Examples > > data(state) > state.pm <- polymars(state.region, state.x77, knots = 15, classify = TRUE, gcv = 1) > plot(state.pm, 3, 4) > > > > cleanEx(); ..nameEx <- "polyclass" > > ### * polyclass > > flush(stderr()); flush(stdout()) > > ### Name: polyclass > ### Title: Polyclass: polychotomous regression and multiple classification > ### Aliases: polyclass > ### Keywords: smooth nonlinear > > ### ** Examples > > data(iris) > fit.iris <- polyclass(iris[,5], iris[,1:4]) warning - model size was reduced > > > > cleanEx(); ..nameEx <- "polymars" > > ### * polymars > > flush(stderr()); flush(stdout()) > > ### Name: polymars > ### Title: Polymars: multivariate adaptive polynomial spline regression > ### Aliases: polymars > ### Keywords: smooth nonlinear > > ### ** Examples > > data(state) > state.pm <- polymars(state.region, state.x77, knots = 15, classify = TRUE) > state.pm2 <- polymars(state.x77[, 2], state.x77[,-2], gcv = 2) > plot(fitted(state.pm2), residuals(state.pm2)) > > > > cleanEx(); ..nameEx <- "predict.polymars" > > ### * predict.polymars > > flush(stderr()); flush(stdout()) > > ### Name: predict.polymars > ### Title: Polymars: multivariate adaptive polynomial spline regression > ### Aliases: predict.polymars > ### Keywords: smooth nonlinear > > ### ** Examples > > data(state) > state.pm <- polymars(state.region, state.x77, knots = 15, classify = TRUE, gcv = 1) > table(predict(state.pm, x = state.x77, classify = TRUE), state.region) state.region Northeast South North Central West 1 8 0 0 0 2 0 16 0 0 3 1 0 12 0 4 0 0 0 13 > > > > cleanEx(); ..nameEx <- "summary.hare" > > ### * summary.hare > > flush(stderr()); flush(stdout()) > > ### Name: summary.hare > ### Title: Hare: hazard regression > ### Aliases: summary.hare print.hare > ### Keywords: distribution smooth survival > > ### ** Examples > > fit <- hare(testhare[,1], testhare[,2], testhare[,3:8]) > summary(fit) dim A/D loglik AIC penalty min max 1 Add -3008.39 6024.39 159.71 Inf 2 Add -2928.54 5872.28 75.52 159.71 3 Add -2892.40 5807.61 NA NA 4 Add -2853.02 5736.44 29.25 75.52 5 Add -2838.40 5714.80 28.65 29.25 6 Add -2825.40 5696.41 NA NA 7 Add -2810.03 5673.26 NA NA 8 Add -2795.43 5651.66 21.86 28.65 9 Add -2784.50 5637.41 17.77 21.86 10 Add -2775.72 5627.44 NA NA 11 Add -2766.73 5617.07 9.48 17.77 12 Add -2763.38 5617.97 NA NA 13 Del -2757.75 5614.31 NA NA 14 Del -2756.19 5618.80 NA NA 15 Del -2747.78 5609.57 9.37 9.48 16 Del -2743.09 5607.80 6.71 9.37 17 Del -2740.08 5609.38 NA NA 18 Del -2736.38 5609.57 4.78 6.71 19 Del -2734.80 5614.01 NA NA 20 Del -2731.85 5615.71 NA NA 21 Del -2729.24 5618.09 NA NA 22 Del -2726.82 5620.85 4.38 4.78 23 Del -2724.63 5624.08 4.15 4.38 24 Del -2722.55 5627.53 3.50 4.15 25 Del -2720.80 5631.63 2.20 3.50 26 Del -2719.70 5637.02 0.04 2.20 27 Add -2719.68 5644.58 0.00 0.04 the present optimal number of dimensions is 16. penalty(AIC) was 7.60, the default (BIC), would have been 7.60. dim1 dim2 beta SE Wald Constant -2.9 0.43 -6.71 Co-2 linear 0.16 0.051 3.12 Time 1 -1.1 0.37 -3.04 Time 5.6 -0.46 0.14 -3.32 Time 2.1 1.4 0.4 3.49 Co-3 linear 0.03 0.0059 5.09 Co-6 linear 0.49 0.1 4.76 Co-1 linear -0.094 0.087 -1.08 Time 2.1 Co-1 linear -0.61 0.13 -4.87 Time 5.6 Co-2 linear 0.061 0.014 4.51 Co-2 4.5 -0.29 0.068 -4.19 Time 5.6 Co-4 linear 0.011 0.0027 4.13 Co-4 linear -0.021 0.0077 -2.76 Time 2.1 Co-4 linear -0.033 0.0076 -4.30 Co-4 62 0.082 0.02 4.12 Time 0.35 -6.6 2.1 -3.14 > > > > cleanEx(); ..nameEx <- "summary.heft" > > ### * summary.heft > > flush(stderr()); flush(stdout()) > > ### Name: summary.heft > ### Title: Heft: hazard estimation with flexible tails > ### Aliases: summary.heft print.heft > ### Keywords: distribution smooth survival > > ### ** Examples > > fit1 <- heft(testhare[,1], testhare[,2]) > summary(fit1) knots A(0)/D(1) loglik AIC minimum penalty maximum penalty 3 1 -2954.28 5931.37 82.52 Inf 4 0 -2913.02 5856.45 2.79 82.52 5 0 -2912.87 5863.75 NA NA 6 1 -2910.63 5866.87 NA NA 7 1 -2910.36 5873.92 NA NA 8 1 -2907.45 5875.71 2.64 2.79 9 1 -2906.50 5881.40 NA NA 10 1 -2905.58 5887.16 NA NA 11 1 -2903.97 5891.54 NA NA 12 1 -2902.83 5896.87 NA NA 13 1 -2900.86 5900.53 1.68 2.64 14 1 -2900.64 5907.69 NA NA 15 1 -2899.18 5912.37 0.75 1.68 16 1 -2899.17 5919.95 NA NA 17 1 -2898.45 5926.11 NA NA 18 1 -2898.40 5933.62 NA NA 19 1 -2897.68 5939.78 0.37 0.75 20 1 -2897.50 5947.01 0.20 0.37 21 1 -2897.49 5954.60 NA NA 22 1 -2897.30 5961.82 0.05 0.20 23 0 -2897.28 5969.37 0.00 0.05 the present optimal number of knots is 4 penalty(AIC) was the default: BIC=log(samplesize): log( 2000 )= 7.6 theta SE t left tail 1.01 0.09 11.01 right tail -1.00 NA NA > # modify tail behavior > fit2 <- heft(testhare[,1], testhare[,2], leftlog = FALSE, rightlog = FALSE, + leftlin = TRUE) > summary(fit2) knots A(0)/D(1) loglik AIC minimum penalty maximum penalty 2 0 -3008.39 6024.39 86.12 Inf 3 0 -2995.90 6007.00 NA NA 4 1 -2922.28 5867.36 13.67 86.12 5 0 -2915.45 5861.29 5.85 13.67 6 1 -2913.06 5864.13 NA NA 7 0 -2912.23 5870.07 NA NA 8 1 -2906.66 5866.54 2.90 5.85 9 1 -2906.43 5873.67 NA NA 10 1 -2903.77 5875.94 1.48 2.90 11 1 -2903.60 5883.21 NA NA 12 1 -2903.04 5889.68 NA NA 13 1 -2902.29 5895.79 NA NA 14 1 -2901.38 5901.56 NA NA 15 1 -2901.04 5908.50 NA NA 16 1 -2899.76 5913.54 NA NA 17 1 -2899.31 5920.24 NA NA 18 1 -2897.85 5924.91 0.86 1.48 19 1 -2897.42 5931.65 0.23 0.86 20 1 -2897.31 5939.03 0.13 0.23 21 1 -2897.24 5946.50 0.03 0.13 22 1 -2897.23 5954.08 0.02 0.03 23 0 -2897.22 5961.66 0.00 0.02 the present optimal number of knots is 5 penalty(AIC) was the default: BIC=log(samplesize): log( 2000 )= 7.6 theta SE t left tail 0 NA NA right tail 0 NA NA > fit3 <- heft(testhare[,1], testhare[,2], penalty = 0) # select largest model > summary(fit3) knots A(0)/D(1) loglik AIC minimum penalty maximum penalty 3 0 -2954.28 5908.56 82.52 Inf 4 0 -2913.02 5826.05 2.48 82.52 5 0 -2912.87 5825.75 NA NA 6 0 -2912.36 5824.72 NA NA 7 0 -2911.57 5823.14 NA NA 8 0 -2909.73 5819.46 NA NA 9 0 -2907.57 5815.14 NA NA 10 0 -2906.92 5813.84 NA NA 11 0 -2904.50 5809.00 NA NA 12 0 -2903.10 5806.20 1.84 2.48 13 0 -2902.18 5804.35 1.10 1.84 14 0 -2901.63 5803.25 0.99 1.10 15 0 -2901.24 5802.47 NA NA 16 0 -2900.94 5801.89 NA NA 17 0 -2900.62 5801.23 NA NA 18 0 -2899.82 5799.64 NA NA 19 0 -2899.18 5798.35 NA NA 20 0 -2898.65 5797.29 0.91 0.99 21 0 -2898.42 5796.85 NA NA 22 0 -2897.94 5795.89 NA NA 23 0 -2897.28 5794.55 0.00 0.91 the present optimal number of knots is 23 penalty(AIC) was 0 , the default (BIC) would have been 7.6 models with fewer than 3 knots can be fitted, but they are not optimal for the present choice of penalty - choose penalty in heft larger to see these fits theta SE t left tail 0.20 0.53 0.38 right tail -0.66 0.55 1.20 > > > > cleanEx(); ..nameEx <- "summary.logspline" > > ### * summary.logspline > > flush(stderr()); flush(stdout()) > > ### Name: summary.logspline > ### Title: Logspline Density Estimation > ### Aliases: summary.logspline print.logspline > ### Keywords: distribution smooth > > ### ** Examples > > y <- rnorm(100) > fit <- logspline(y) > summary(fit) knots A(1)/D(2) loglik AIC minimum penalty maximum penalty 3 2 -132.83 274.87 2.11 Inf 4 2 -132.63 279.08 NA NA 5 2 -130.72 279.85 1.21 2.11 6 2 -130.54 284.11 NA NA 7 2 -129.68 286.99 NA NA 8 2 -129.58 291.40 NA NA 9 2 -128.30 293.44 0.47 1.21 10 2 -128.25 297.94 NA NA 11 2 -127.82 301.70 0.06 0.47 12 1 -127.80 306.25 0.00 0.06 the present optimal number of knots is 3 penalty(AIC) was the default: BIC=log(samplesize): log( 100 )= 4.61 > > > > cleanEx(); ..nameEx <- "summary.lspec" > > ### * summary.lspec > > flush(stderr()); flush(stdout()) > > ### Name: summary.lspec > ### Title: Lspec: logspline estimation of a spectral distribution > ### Aliases: summary.lspec print.lspec > ### Keywords: ts smooth > > ### ** Examples > > data(co2) > co2.detrend <- lm(co2~c(1:length(co2)))$residuals > fit <- lspec(co2.detrend) > summary(fit) Logspline Spectral Estimation ============================= The fit was obtained by the command: lspec(data = co2.detrend) A spline with 5 knots, was fitted; there were also 3 lines in the model. The log-likelihood of the model was 721.72 which corresponds to an AIC value of -1399.8 . The program went though 2 updown cycles, and reached a stable solution. Both penalty (AIC) and minmass were the default values. For penalty this was log(n)=log( 234 )= 5.46 (as in BIC) and for minmass this was 0.1086 . The locations of the knots were: 0.013 0.483 0.51 0.618 0.658 The locations and the mass in each line were: angular frequency period mass % of total mass 0.013 468 1.04847 15.46 0.524 12 1.95604 28.84 1.047 6 0.14724 2.17 > > > > cleanEx(); ..nameEx <- "summary.oldlogspline" > > ### * summary.oldlogspline > > flush(stderr()); flush(stdout()) > > ### Name: summary.oldlogspline > ### Title: Logspline Density Estimation - 1992 version > ### Aliases: summary.oldlogspline print.oldlogspline > ### Keywords: distribution smooth > > ### ** Examples > > y <- rnorm(100) > fit <- oldlogspline(y) > summary(fit) knots loglik AIC minimum penalty maximum penalty 3 -130.97 275.76 0.37 Inf 4 -130.97 280.35 NA NA 5 -130.70 284.43 NA NA 6 -130.51 288.65 NA NA 7 -130.23 292.69 0.31 0.37 8 -130.14 297.12 NA NA 9 -129.92 301.28 0.03 0.31 10 -129.90 305.86 0.01 0.03 11 -129.90 310.45 0.00 0.01 the present optimal number of knots is 3 penalty(AIC) was the default: BIC=log(samplesize): log( 100 )= 4.61 > > > > cleanEx(); ..nameEx <- "summary.polyclass" > > ### * summary.polyclass > > flush(stderr()); flush(stdout()) > > ### Name: summary.polyclass > ### Title: Polyclass: polychotomous regression and multiple classification > ### Aliases: summary.polyclass print.polyclass > ### Keywords: smooth nonlinear > > ### ** Examples > > data(iris) > fit.iris <- polyclass(iris[,5], iris[,1:4]) warning - model size was reduced > summary(fit.iris) ========================POLYCLASS summary======================= The fit was obtained with polyclass(data = iris[, 5], cov = iris[, 1:4]) There were 3 classes and 4 covariates. There were 150 cases. The model selection was carried out using AIC. The penalty was the default, log(150 )=5.01 . The model had dimension 6 , log-likelihood -10.28 and AIC -50.63 . The locations of the knots: Number 1 0 2 0 3 0 4 0 There are 3 basis functions, summarized below: dim1 knot1 dim2 knot2 Class 1 Class 2 Class 3 1 NA NA NA NA 150.293 45.263 0 2 3 NA NA NA -48.334 -5.752 0 3 4 NA NA NA -11.723 -10.449 0 The first basis function is the constant function. For all others, the first column and the third column indicate on which covariates that basis function depends. If the third column is NA, the basis function depends on only one covariate. For the nonconstant basis functions the second and the fourth column indicate on which knot the function depend. If these columns are NA, the basis function is linear in this covariate. The remaining columns give the coefficients. ================================================================ The influence of the penalty parameter is summarized below: dim AIC l-lik-trn loss-trn sq-err-trn A/D pen-min pen-max 2 339.605 -1.099 0.667 0.444 1 148.081 Inf 4 53.464 -0.111 0.040 0.033 0 6.429 148.081 6 50.627 -0.069 0.040 0.023 0 3.649 6.429 8 53.351 -0.044 0.020 0.015 0 0.000 3.649 10 63.372 -0.044 0.020 0.015 1 0.000 0.000 ================================================================ The importance-anova decomposition is: Cov-1 Cov-2 Percentage NA NA 29.13 3 NA 70.10 4 NA 0.78 ================================================================ > > > > cleanEx(); ..nameEx <- "summary.polymars" > > ### * summary.polymars > > flush(stderr()); flush(stdout()) > > ### Name: summary.polymars > ### Title: Polymars: multivariate adaptive polynomial spline regression > ### Aliases: summary.polymars print.polymars > ### Keywords: smooth nonlinear > > ### ** Examples > > data(state) > state.pm <- polymars(state.region, state.x77, knots = 15, classify = TRUE) > summary(state.pm) Call: polymars(responses = state.region, predictors = state.x77, knots = 15, classify = TRUE) Model fitting 0/1 size RSS 1 RSS 2 RSS 3 RSS 4 GCV 1 1 1 10.880000 9.620000 7.380000 9.120000 0.8742911 2 1 2 4.728607 5.439872 7.361317 9.030611 0.7528460 3 1 3 4.249251 3.407904 6.639464 7.422262 0.7520388 4 1 4 3.643535 3.033199 6.353973 6.689362 0.8529441 5 1 5 3.396585 2.818353 6.341048 5.972349 1.0293520 6 1 6 3.391316 2.752116 5.949608 5.315560 1.2876184 7 1 7 3.332588 2.593224 5.525557 4.663557 1.6647652 8 1 8 3.312567 2.591668 4.520665 3.852843 2.2033552 9 1 9 3.262781 1.867988 3.022214 3.496976 2.9719283 10 1 10 2.740424 1.333166 3.021035 3.495135 5.2948804 11 1 11 2.542810 1.112874 2.650159 3.402174 13.4833582 12 1 12 2.377489 1.105779 2.397724 2.721891 107.5360370 13 1 13 1.924453 1.101098 2.365098 1.871584 90.7779191 14 0 12 2.285647 1.495495 2.385731 1.900707 100.8447419 15 0 11 2.581900 1.546766 2.434914 2.885754 13.1240743 16 0 10 3.008675 1.703315 2.650459 3.406872 5.3846607 17 0 9 3.262781 1.867988 3.022214 3.496976 2.9719283 18 0 8 3.880536 1.874898 3.854869 3.498859 2.0230189 19 0 7 3.883019 2.007287 4.570340 5.085272 1.6059832 20 0 6 4.045564 2.096548 4.574305 5.670350 1.2120389 21 0 5 4.191242 2.568143 4.589619 7.091592 1.0244776 22 0 4 4.211419 3.330597 6.233355 7.395129 0.9156791 23 0 3 4.249251 3.407904 6.639464 7.422262 0.7520388 24 0 2 4.728607 5.439872 7.361317 9.030611 0.7528460 25 0 1 10.880000 9.620000 7.380000 9.120000 0.8742911 Model produced pred1 knot1 pred2 knot2 Coefs 1 Coefs 2 Coefs 3 Coefs 4 1 0 NA 0 NA 3.47651598 0.0418565968 -0.96302069 -1.55535189 2 6 NA 0 NA -0.06191473 -0.0009962812 0.02456459 0.03834643 3 6 53.3 0 NA 0.04230451 0.0870995844 -0.05191371 -0.07749038 SE 1 SE 2 SE 3 SE 4 1 0.458820454 0.410894414 0.57352596 0.60639375 2 0.009471801 0.008482425 0.01183976 0.01251828 3 0.018372360 0.016453277 0.02296547 0.02428158 RESPONSES : 4 Rsquared : 0.609 0.646 0.1 0.186 > > > > cleanEx(); ..nameEx <- "testhare" > > ### * testhare > > flush(stderr()); flush(stdout()) > > ### Name: testhare > ### Title: Fake survival data for Hare and Heft > ### Aliases: testhare > ### Keywords: survival datasets > > ### ** Examples > > harefit <- hare(testhare[,1], testhare[,2], testhare[,3:8]) > heftfit <- heft(testhare[,1], testhare[,2]) > > > > cleanEx(); ..nameEx <- "unstrip" > > ### * unstrip > > flush(stderr()); flush(stdout()) > > ### Name: unstrip > ### Title: Reformat data as vector or matrix > ### Aliases: unstrip > ### Keywords: utilities classes > > ### ** Examples > > data(co2) > unstrip(co2) [1] 315.42 316.31 316.50 317.56 318.13 318.00 316.39 314.65 313.68 313.18 [11] 314.66 315.43 316.27 316.81 317.42 318.87 319.87 319.43 318.01 315.74 [21] 314.00 313.68 314.84 316.03 316.73 317.54 318.38 319.31 320.42 319.61 [31] 318.42 316.63 314.83 315.16 315.94 316.85 317.78 318.40 319.53 320.42 [41] 320.85 320.45 319.45 317.25 316.11 315.27 316.53 317.53 318.58 318.92 [51] 319.70 321.22 322.08 321.31 319.58 317.61 316.05 315.83 316.91 318.20 [61] 319.41 320.07 320.74 321.40 322.06 321.73 320.27 318.54 316.54 316.71 [71] 317.53 318.55 319.27 320.28 320.73 321.97 322.00 321.71 321.05 318.71 [81] 317.66 317.14 318.70 319.25 320.46 321.43 322.23 323.54 323.91 323.59 [91] 322.24 320.20 318.48 317.94 319.63 320.87 322.17 322.34 322.88 324.25 [101] 324.83 323.93 322.38 320.76 319.10 319.24 320.56 321.80 322.40 322.99 [111] 323.73 324.86 325.40 325.20 323.98 321.95 320.18 320.09 321.16 322.74 [121] 323.83 324.26 325.47 326.50 327.21 326.54 325.72 323.50 322.22 321.62 [131] 322.69 323.95 324.89 325.82 326.77 327.97 327.91 327.50 326.18 324.53 [141] 322.93 322.90 323.85 324.96 326.01 326.51 327.01 327.62 328.76 328.40 [151] 327.20 325.27 323.20 323.40 324.63 325.85 326.60 327.47 327.58 329.56 [161] 329.90 328.92 327.88 326.16 324.68 325.04 326.34 327.39 328.37 329.40 [171] 330.14 331.33 332.31 331.90 330.70 329.15 327.35 327.02 327.99 328.48 [181] 329.18 330.55 331.32 332.48 332.92 332.08 331.01 329.23 327.27 327.21 [191] 328.29 329.41 330.23 331.25 331.87 333.14 333.80 333.43 331.73 329.90 [201] 328.40 328.17 329.32 330.59 331.58 332.39 333.33 334.41 334.71 334.17 [211] 332.89 330.77 329.14 328.78 330.14 331.52 332.75 333.24 334.53 335.90 [221] 336.57 336.10 334.76 332.59 331.42 330.98 332.24 333.68 334.80 335.22 [231] 336.47 337.59 337.84 337.72 336.37 334.51 332.60 332.38 333.75 334.78 [241] 336.05 336.59 337.79 338.71 339.30 339.12 337.56 335.92 333.75 333.70 [251] 335.12 336.56 337.84 338.19 339.91 340.60 341.29 341.00 339.39 337.43 [261] 335.72 335.84 336.93 338.04 339.06 340.30 341.21 342.33 342.74 342.08 [271] 340.32 338.26 336.52 336.68 338.19 339.44 340.57 341.44 342.53 343.39 [281] 343.96 343.18 341.88 339.65 337.81 337.69 339.09 340.32 341.20 342.35 [291] 342.93 344.77 345.58 345.14 343.81 342.21 339.69 339.82 340.98 342.82 [301] 343.52 344.33 345.11 346.88 347.25 346.62 345.22 343.11 340.90 341.18 [311] 342.80 344.04 344.79 345.82 347.25 348.17 348.74 348.07 346.38 344.51 [321] 342.92 342.62 344.06 345.38 346.11 346.78 347.68 349.37 350.03 349.37 [331] 347.76 345.73 344.68 343.99 345.48 346.72 347.84 348.29 349.23 350.80 [341] 351.66 351.07 349.33 347.92 346.27 346.18 347.64 348.78 350.25 351.54 [351] 352.05 353.41 354.04 353.62 352.22 350.27 348.55 348.72 349.91 351.18 [361] 352.60 352.92 353.53 355.26 355.52 354.97 353.75 351.52 349.64 349.83 [371] 351.14 352.37 353.50 354.55 355.23 356.04 357.00 356.07 354.67 352.76 [381] 350.82 351.04 352.69 354.07 354.59 355.63 357.03 358.48 359.22 358.12 [391] 356.06 353.92 352.05 352.11 353.64 354.89 355.88 356.63 357.72 359.07 [401] 359.58 359.17 356.94 354.92 352.94 353.23 354.09 355.33 356.63 357.10 [411] 358.32 359.41 360.23 359.55 357.53 355.48 353.67 353.95 355.30 356.78 [421] 358.34 358.89 359.95 361.25 361.67 360.94 359.55 357.49 355.84 356.00 [431] 357.59 359.05 359.98 361.03 361.66 363.48 363.82 363.30 361.94 359.50 [441] 358.11 357.80 359.61 360.74 362.09 363.29 364.06 364.76 365.45 365.01 [451] 363.70 361.54 359.51 359.65 360.80 362.38 363.23 364.06 364.61 366.40 [461] 366.84 365.68 364.52 362.57 360.24 360.83 362.49 364.34 > data(iris) > unstrip(iris) [,1] [,2] [,3] [,4] [,5] [1,] 5.1 3.5 1.4 0.2 1 [2,] 4.9 3.0 1.4 0.2 1 [3,] 4.7 3.2 1.3 0.2 1 [4,] 4.6 3.1 1.5 0.2 1 [5,] 5.0 3.6 1.4 0.2 1 [6,] 5.4 3.9 1.7 0.4 1 [7,] 4.6 3.4 1.4 0.3 1 [8,] 5.0 3.4 1.5 0.2 1 [9,] 4.4 2.9 1.4 0.2 1 [10,] 4.9 3.1 1.5 0.1 1 [11,] 5.4 3.7 1.5 0.2 1 [12,] 4.8 3.4 1.6 0.2 1 [13,] 4.8 3.0 1.4 0.1 1 [14,] 4.3 3.0 1.1 0.1 1 [15,] 5.8 4.0 1.2 0.2 1 [16,] 5.7 4.4 1.5 0.4 1 [17,] 5.4 3.9 1.3 0.4 1 [18,] 5.1 3.5 1.4 0.3 1 [19,] 5.7 3.8 1.7 0.3 1 [20,] 5.1 3.8 1.5 0.3 1 [21,] 5.4 3.4 1.7 0.2 1 [22,] 5.1 3.7 1.5 0.4 1 [23,] 4.6 3.6 1.0 0.2 1 [24,] 5.1 3.3 1.7 0.5 1 [25,] 4.8 3.4 1.9 0.2 1 [26,] 5.0 3.0 1.6 0.2 1 [27,] 5.0 3.4 1.6 0.4 1 [28,] 5.2 3.5 1.5 0.2 1 [29,] 5.2 3.4 1.4 0.2 1 [30,] 4.7 3.2 1.6 0.2 1 [31,] 4.8 3.1 1.6 0.2 1 [32,] 5.4 3.4 1.5 0.4 1 [33,] 5.2 4.1 1.5 0.1 1 [34,] 5.5 4.2 1.4 0.2 1 [35,] 4.9 3.1 1.5 0.2 1 [36,] 5.0 3.2 1.2 0.2 1 [37,] 5.5 3.5 1.3 0.2 1 [38,] 4.9 3.6 1.4 0.1 1 [39,] 4.4 3.0 1.3 0.2 1 [40,] 5.1 3.4 1.5 0.2 1 [41,] 5.0 3.5 1.3 0.3 1 [42,] 4.5 2.3 1.3 0.3 1 [43,] 4.4 3.2 1.3 0.2 1 [44,] 5.0 3.5 1.6 0.6 1 [45,] 5.1 3.8 1.9 0.4 1 [46,] 4.8 3.0 1.4 0.3 1 [47,] 5.1 3.8 1.6 0.2 1 [48,] 4.6 3.2 1.4 0.2 1 [49,] 5.3 3.7 1.5 0.2 1 [50,] 5.0 3.3 1.4 0.2 1 [51,] 7.0 3.2 4.7 1.4 2 [52,] 6.4 3.2 4.5 1.5 2 [53,] 6.9 3.1 4.9 1.5 2 [54,] 5.5 2.3 4.0 1.3 2 [55,] 6.5 2.8 4.6 1.5 2 [56,] 5.7 2.8 4.5 1.3 2 [57,] 6.3 3.3 4.7 1.6 2 [58,] 4.9 2.4 3.3 1.0 2 [59,] 6.6 2.9 4.6 1.3 2 [60,] 5.2 2.7 3.9 1.4 2 [61,] 5.0 2.0 3.5 1.0 2 [62,] 5.9 3.0 4.2 1.5 2 [63,] 6.0 2.2 4.0 1.0 2 [64,] 6.1 2.9 4.7 1.4 2 [65,] 5.6 2.9 3.6 1.3 2 [66,] 6.7 3.1 4.4 1.4 2 [67,] 5.6 3.0 4.5 1.5 2 [68,] 5.8 2.7 4.1 1.0 2 [69,] 6.2 2.2 4.5 1.5 2 [70,] 5.6 2.5 3.9 1.1 2 [71,] 5.9 3.2 4.8 1.8 2 [72,] 6.1 2.8 4.0 1.3 2 [73,] 6.3 2.5 4.9 1.5 2 [74,] 6.1 2.8 4.7 1.2 2 [75,] 6.4 2.9 4.3 1.3 2 [76,] 6.6 3.0 4.4 1.4 2 [77,] 6.8 2.8 4.8 1.4 2 [78,] 6.7 3.0 5.0 1.7 2 [79,] 6.0 2.9 4.5 1.5 2 [80,] 5.7 2.6 3.5 1.0 2 [81,] 5.5 2.4 3.8 1.1 2 [82,] 5.5 2.4 3.7 1.0 2 [83,] 5.8 2.7 3.9 1.2 2 [84,] 6.0 2.7 5.1 1.6 2 [85,] 5.4 3.0 4.5 1.5 2 [86,] 6.0 3.4 4.5 1.6 2 [87,] 6.7 3.1 4.7 1.5 2 [88,] 6.3 2.3 4.4 1.3 2 [89,] 5.6 3.0 4.1 1.3 2 [90,] 5.5 2.5 4.0 1.3 2 [91,] 5.5 2.6 4.4 1.2 2 [92,] 6.1 3.0 4.6 1.4 2 [93,] 5.8 2.6 4.0 1.2 2 [94,] 5.0 2.3 3.3 1.0 2 [95,] 5.6 2.7 4.2 1.3 2 [96,] 5.7 3.0 4.2 1.2 2 [97,] 5.7 2.9 4.2 1.3 2 [98,] 6.2 2.9 4.3 1.3 2 [99,] 5.1 2.5 3.0 1.1 2 [100,] 5.7 2.8 4.1 1.3 2 [101,] 6.3 3.3 6.0 2.5 3 [102,] 5.8 2.7 5.1 1.9 3 [103,] 7.1 3.0 5.9 2.1 3 [104,] 6.3 2.9 5.6 1.8 3 [105,] 6.5 3.0 5.8 2.2 3 [106,] 7.6 3.0 6.6 2.1 3 [107,] 4.9 2.5 4.5 1.7 3 [108,] 7.3 2.9 6.3 1.8 3 [109,] 6.7 2.5 5.8 1.8 3 [110,] 7.2 3.6 6.1 2.5 3 [111,] 6.5 3.2 5.1 2.0 3 [112,] 6.4 2.7 5.3 1.9 3 [113,] 6.8 3.0 5.5 2.1 3 [114,] 5.7 2.5 5.0 2.0 3 [115,] 5.8 2.8 5.1 2.4 3 [116,] 6.4 3.2 5.3 2.3 3 [117,] 6.5 3.0 5.5 1.8 3 [118,] 7.7 3.8 6.7 2.2 3 [119,] 7.7 2.6 6.9 2.3 3 [120,] 6.0 2.2 5.0 1.5 3 [121,] 6.9 3.2 5.7 2.3 3 [122,] 5.6 2.8 4.9 2.0 3 [123,] 7.7 2.8 6.7 2.0 3 [124,] 6.3 2.7 4.9 1.8 3 [125,] 6.7 3.3 5.7 2.1 3 [126,] 7.2 3.2 6.0 1.8 3 [127,] 6.2 2.8 4.8 1.8 3 [128,] 6.1 3.0 4.9 1.8 3 [129,] 6.4 2.8 5.6 2.1 3 [130,] 7.2 3.0 5.8 1.6 3 [131,] 7.4 2.8 6.1 1.9 3 [132,] 7.9 3.8 6.4 2.0 3 [133,] 6.4 2.8 5.6 2.2 3 [134,] 6.3 2.8 5.1 1.5 3 [135,] 6.1 2.6 5.6 1.4 3 [136,] 7.7 3.0 6.1 2.3 3 [137,] 6.3 3.4 5.6 2.4 3 [138,] 6.4 3.1 5.5 1.8 3 [139,] 6.0 3.0 4.8 1.8 3 [140,] 6.9 3.1 5.4 2.1 3 [141,] 6.7 3.1 5.6 2.4 3 [142,] 6.9 3.1 5.1 2.3 3 [143,] 5.8 2.7 5.1 1.9 3 [144,] 6.8 3.2 5.9 2.3 3 [145,] 6.7 3.3 5.7 2.5 3 [146,] 6.7 3.0 5.2 2.3 3 [147,] 6.3 2.5 5.0 1.9 3 [148,] 6.5 3.0 5.2 2.0 3 [149,] 6.2 3.4 5.4 2.3 3 [150,] 5.9 3.0 5.1 1.8 3 > > > > ### *