fitVsDatCorrelation=0.927404700391384 cont.fitVsDatCorrelation=0.239235728183563 fstatistic=10831.1807969666,52,692 cont.fstatistic=1596.17552855419,52,692 residuals=-0.668307406982637,-0.0943159013290568,-0.000280754991608616,0.0902982162618411,0.592678099484798 cont.residuals=-0.9109168314613,-0.301517962209852,-0.0482707480490194,0.265560214580688,1.37583775465419 predictedValues: Include Exclude Both Lung 73.6389254432828 82.5540909108796 158.957123834260 cerebhem 65.4653455065974 62.1195405795781 134.618756955801 cortex 65.80773482396 92.367805648036 151.675904151707 heart 67.7635065403885 75.9584606014376 149.962455966267 kidney 75.3467770850822 89.7809075242211 140.428758458131 liver 73.789824184587 89.9866638288717 171.747378914292 stomach 69.9748788207421 67.7615249203614 187.39110210529 testicle 70.4464842237434 83.1967013995569 171.076613863900 diffExp=-8.91516546759678,3.34580492701932,-26.5600708240760,-8.19495406104907,-14.4341304391389,-16.1968396442847,2.21335390038068,-12.7502171758134 diffExpScore=1.12265784341703 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,-1,0,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,-1,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,0,-1,0,0,-1,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 82.3347762820807 82.330063299434 74.4698220111756 cerebhem 81.8868711891086 69.637193515334 91.9205536611928 cortex 76.855557835659 75.1954762885713 70.89987532557 heart 80.7440854124372 87.5869177842538 74.2689304824192 kidney 85.1698431632282 89.9241040749822 76.5882209599615 liver 85.2614979456144 78.4618645891839 69.5318197598865 stomach 78.8669848399856 89.1330243688113 84.3157302201322 testicle 79.009932927338 70.4323621732139 76.0291348835685 cont.diffExp=0.00471298264668008,12.2496776737745,1.66008154708773,-6.84283237181657,-4.75426091175403,6.79963335643045,-10.2660395288257,8.57757075412417 cont.diffExpScore=6.06923475169119 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,0,0,0,0,0 cont.diffExp1.4Score=0 cont.diffExp1.3=0,0,0,0,0,0,0,0 cont.diffExp1.3Score=0 cont.diffExp1.2=0,0,0,0,0,0,0,0 cont.diffExp1.2Score=0 tran.correlation=0.477559629499758 cont.tran.correlation=0.264586457962303 tran.covariance=0.00377223865243667 cont.tran.covariance=0.00101305987017626 tran.mean=75.3724482525829 cont.tran.mean=80.8019097305773 weightedLogRatios: wLogRatio Lung -0.497839367804129 cerebhem 0.217987899134184 cortex -1.47695053667124 heart -0.487829746846669 kidney -0.772899807382352 liver -0.873226365381703 stomach 0.136025984062049 testicle -0.721650324333147 cont.weightedLogRatios: wLogRatio Lung 0.000252486915057701 cerebhem 0.70071200775739 cortex 0.0945750165575303 heart -0.360526203688954 kidney -0.242902320018027 liver 0.366031600539564 stomach -0.541957813409237 testicle 0.495551199627198 varWeightedLogRatios=0.30130777047653 cont.varWeightedLogRatios=0.189922382582518 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 2.98250395449403 0.0851677269006994 35.0191799526533 2.30547161145268e-155 *** df.mm.trans1 1.22703921585466 0.0764930736017881 16.0411806988231 1.80116760907910e-49 *** df.mm.trans2 1.25520494661091 0.0703457698888957 17.8433607108627 7.16662191498967e-59 *** df.mm.exp2 -0.235858217558028 0.0963562835675655 -2.44777204791883 0.0146216610464825 * df.mm.exp3 0.0467768555268729 0.0963562835675656 0.485457240513772 0.62750569554059 df.mm.exp4 -0.108167536661437 0.0963562835675656 -1.12257896067141 0.262005689109544 df.mm.exp5 0.230780204538782 0.0963562835675656 2.39507166522210 0.0168820019346947 * df.mm.exp6 0.0108646624588893 0.0963562835675656 0.112755100722321 0.910257416369515 df.mm.exp7 -0.413060015148353 0.0963562835675656 -4.28679894922176 2.06997293527393e-05 *** df.mm.exp8 -0.110043443216997 0.0963562835675656 -1.14204740098589 0.253829324491144 df.mm.trans1:exp2 0.118205379720399 0.0922541178902438 1.28130193452209 0.200516824198708 df.mm.trans2:exp2 -0.0485349057837744 0.0802969029729713 -0.604443060526402 0.545747235702307 df.mm.trans1:exp3 -0.159213237821476 0.0922541178902439 -1.72581171943885 0.0848276827188298 . df.mm.trans2:exp3 0.0655479127821852 0.0802969029729713 0.816319314385628 0.41459844301927 df.mm.trans1:exp4 0.025017570848232 0.0922541178902438 0.271181074843681 0.78633264010955 df.mm.trans2:exp4 0.0249004305872757 0.0802969029729713 0.310104495507846 0.756574860300997 df.mm.trans1:exp5 -0.207852817207982 0.092254117890244 -2.2530464976671 0.0245685930701126 * df.mm.trans2:exp5 -0.146861588826800 0.0802969029729713 -1.82898198298177 0.0678325168512914 . df.mm.trans1:exp6 -0.00881758827043853 0.0922541178902439 -0.0955793461808279 0.92388234043523 df.mm.trans2:exp6 0.0753430913101 0.0802969029729713 0.938306317187117 0.348414387111829 df.mm.trans1:exp7 0.362022554564169 0.0922541178902438 3.92418856570579 9.57133582936018e-05 *** df.mm.trans2:exp7 0.21560084406443 0.0802969029729713 2.68504557563077 0.00742569585916174 ** df.mm.trans1:exp8 0.0657230115228844 0.0922541178902439 0.71241276840429 0.47644933890796 df.mm.trans2:exp8 0.117797417715892 0.0802969029729713 1.46702317716467 0.142824056703541 df.mm.trans1:probe2 -0.173489648810261 0.0461270589451220 -3.76112530861038 0.000183461460983562 *** df.mm.trans1:probe3 0.154774572201268 0.0461270589451219 3.35539650133353 0.000835788722314003 *** df.mm.trans1:probe4 0.241249853490809 0.0461270589451219 5.23011566329922 2.24706076302517e-07 *** df.mm.trans1:probe5 0.0596226028039332 0.0461270589451219 1.29257325672697 0.196590112082043 df.mm.trans1:probe6 -0.156678770268091 0.0461270589451219 -3.39667808551363 0.000721105120437796 *** df.mm.trans1:probe7 0.177656193282914 0.0461270589451220 3.85145286401793 0.000128316332050404 *** df.mm.trans1:probe8 0.558587317651089 0.0461270589451219 12.1097535898755 9.46109530506538e-31 *** df.mm.trans1:probe9 0.611448777051676 0.0461270589451219 13.2557503347249 6.67311317739373e-36 *** df.mm.trans1:probe10 -0.098488531725046 0.0461270589451219 -2.13515741036556 0.0330987201215417 * df.mm.trans1:probe11 0.191992941647596 0.0461270589451219 4.16226280275127 3.54849117046962e-05 *** df.mm.trans1:probe12 0.21328875243517 0.0461270589451219 4.62393998908369 4.49325673604691e-06 *** df.mm.trans1:probe13 0.569121126946613 0.0461270589451219 12.3381186653089 9.38290898857626e-32 *** df.mm.trans1:probe14 0.612895157269103 0.0461270589451219 13.2871067717167 4.78035157845281e-36 *** df.mm.trans1:probe15 0.51466750183731 0.0461270589451219 11.1576049634905 1.06668386493074e-26 *** df.mm.trans1:probe16 0.0637209722214267 0.0461270589451220 1.38142282813297 0.167594853581975 df.mm.trans1:probe17 -0.148372712207286 0.0461270589451219 -3.21660898397636 0.00135755860590226 ** df.mm.trans1:probe18 -0.262708496316914 0.0461270589451219 -5.69532292595247 1.82100430249002e-08 *** df.mm.trans1:probe19 -0.305010076598131 0.0461270589451220 -6.61238942116396 7.56381647366055e-11 *** df.mm.trans1:probe20 -0.129889517032984 0.0461270589451219 -2.8159071920782 0.00500243321297811 ** df.mm.trans1:probe21 -0.283056295020936 0.0461270589451219 -6.13644792219882 1.42028261707397e-09 *** df.mm.trans1:probe22 -0.170566873739740 0.0461270589451219 -3.69776173986437 0.000234737175795122 *** df.mm.trans2:probe2 0.278431184761642 0.0461270589451219 6.03617900488509 2.57272265103562e-09 *** df.mm.trans2:probe3 0.643137279810916 0.0461270589451220 13.9427332788779 4.03501625371903e-39 *** df.mm.trans2:probe4 0.0471452266439726 0.0461270589451219 1.02207311114420 0.307103466783206 df.mm.trans2:probe5 0.504502917902323 0.0461270589451220 10.9372444166132 8.59010253957889e-26 *** df.mm.trans2:probe6 0.108486814058103 0.0461270589451220 2.35191266339289 0.0189564826984418 * df.mm.trans3:probe2 -0.482418992210683 0.0461270589451219 -10.4584814909753 7.22647502847746e-24 *** df.mm.trans3:probe3 -0.705921845362196 0.0461270589451220 -15.3038555135727 9.41917489706134e-46 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.58119550176852 0.221073336381752 20.7225148755961 1.39860809143157e-74 *** df.mm.trans1 -0.0439333232678238 0.198556185618984 -0.221263936607489 0.824952200960965 df.mm.trans2 -0.202573692874468 0.182599379079512 -1.1093887279116 0.267647902566427 df.mm.exp2 -0.383423101569636 0.250115928472114 -1.53298154144703 0.125737431895434 df.mm.exp3 -0.110385747749236 0.250115928472114 -0.441338336280904 0.659105938627483 df.mm.exp4 0.0450877249878214 0.250115928472114 0.180267307497165 0.856995496770775 df.mm.exp5 0.094034218634999 0.250115928472114 0.375962535490749 0.707059975742575 df.mm.exp6 0.0554152655875377 0.250115928472114 0.221558322678821 0.824723092974193 df.mm.exp7 -0.0878117728557937 0.250115928472114 -0.351084288762457 0.725631973991176 df.mm.exp8 -0.218026129250437 0.250115928472114 -0.871700297467239 0.383674224448332 df.mm.trans1:exp2 0.3779682031379 0.239467770000847 1.57836774083027 0.114938136319108 df.mm.trans2:exp2 0.215985585602672 0.208429940393429 1.03625028724271 0.300447361545262 df.mm.trans1:exp3 0.0415199621873599 0.239467770000848 0.173384343902367 0.862400034120632 df.mm.trans2:exp3 0.0197404909983145 0.208429940393429 0.0947104382463129 0.924572243935812 df.mm.trans1:exp4 -0.0645965847336441 0.239467770000848 -0.269750642157045 0.787432516128579 df.mm.trans2:exp4 0.0168075912795388 0.208429940393429 0.0806390446968082 0.93575232954455 df.mm.trans1:exp5 -0.0601803744388671 0.239467770000848 -0.251308868991656 0.801649926159793 df.mm.trans2:exp5 -0.00580452228808637 0.208429940393429 -0.0278487931106724 0.977790778919488 df.mm.trans1:exp6 -0.0204858587557355 0.239467770000848 -0.08554745699458 0.931850913052249 df.mm.trans2:exp6 -0.103538890410784 0.208429940393429 -0.496756321166461 0.619518669001401 df.mm.trans1:exp7 0.044780896543594 0.239467770000848 0.187001768728358 0.851714057851919 df.mm.trans2:exp7 0.167205352370819 0.208429940393429 0.802213693748627 0.422704759870202 df.mm.trans1:exp8 0.176806133571557 0.239467770000848 0.738329561305602 0.460564524832804 df.mm.trans2:exp8 0.0619426465895938 0.208429940393429 0.297186893939863 0.766413013445923 df.mm.trans1:probe2 -0.176183452270803 0.119733885000424 -1.47145857891590 0.141621734542939 df.mm.trans1:probe3 -0.147409363737504 0.119733885000424 -1.23114157480969 0.218688174021235 df.mm.trans1:probe4 -0.147308098298465 0.119733885000424 -1.23029582058532 0.219004406093234 df.mm.trans1:probe5 -0.176341803580371 0.119733885000424 -1.47278110603149 0.141264744289285 df.mm.trans1:probe6 -0.115214504467901 0.119733885000424 -0.96225479084299 0.336257748072692 df.mm.trans1:probe7 -0.213521987950231 0.119733885000424 -1.78330460044352 0.0749749300388279 . df.mm.trans1:probe8 -0.0820471899828674 0.119733885000424 -0.685246202297512 0.493418044054967 df.mm.trans1:probe9 -0.125964766788105 0.119733885000424 -1.05203941881330 0.293148686911132 df.mm.trans1:probe10 -0.135326004062965 0.119733885000424 -1.13022311154847 0.258773808785065 df.mm.trans1:probe11 -0.072553419614558 0.119733885000424 -0.605955612434201 0.544742930158768 df.mm.trans1:probe12 -0.0748254006858091 0.119733885000424 -0.624930868029082 0.532222435432152 df.mm.trans1:probe13 -0.146294435854592 0.119733885000424 -1.22182985922552 0.222188022024375 df.mm.trans1:probe14 -0.36567197078271 0.119733885000424 -3.05403913671903 0.00234446965650831 ** df.mm.trans1:probe15 -0.132861340092675 0.119733885000424 -1.10963859639403 0.267540247751142 df.mm.trans1:probe16 -0.171335075933823 0.119733885000424 -1.43096564463115 0.152891482036917 df.mm.trans1:probe17 -0.197421784745719 0.119733885000424 -1.64883804400918 0.0996346884261092 . df.mm.trans1:probe18 -0.128117433821788 0.119733885000424 -1.0700181809129 0.284984200360361 df.mm.trans1:probe19 -0.0313351695315788 0.119733885000424 -0.261706780260808 0.793625388917312 df.mm.trans1:probe20 -0.15937949004141 0.119733885000424 -1.33111432942183 0.183589767832454 df.mm.trans1:probe21 -0.174099830216101 0.119733885000424 -1.45405647044264 0.146384032393157 df.mm.trans1:probe22 -0.188502602567945 0.119733885000424 -1.57434633117666 0.115864455257459 df.mm.trans2:probe2 0.221915706639543 0.119733885000424 1.85340771861497 0.0642493771517826 . df.mm.trans2:probe3 0.00126674475564737 0.119733885000424 0.0105796680333465 0.991561852927192 df.mm.trans2:probe4 0.0376474563781966 0.119733885000424 0.314426082291269 0.753292221872004 df.mm.trans2:probe5 -0.0287800021925155 0.119733885000424 -0.240366394128226 0.81011741729872 df.mm.trans2:probe6 0.0569807858144667 0.119733885000424 0.475895238964851 0.63429921448574 df.mm.trans3:probe2 -0.173842483323164 0.119733885000424 -1.45190714660724 0.146980641377967 df.mm.trans3:probe3 0.100958935381014 0.119733885000424 0.84319435037673 0.399411111781134