fitVsDatCorrelation=0.828565113279189 cont.fitVsDatCorrelation=0.229030138869561 fstatistic=11070.0267101610,53,715 cont.fstatistic=3653.31011390141,53,715 residuals=-0.403610329231343,-0.0814484447976373,-0.00728290744384743,0.0748946270082013,0.830179677591763 cont.residuals=-0.54391129883333,-0.178354442443702,-0.0257504762120033,0.159435011530201,1.35283573474962 predictedValues: Include Exclude Both Lung 58.624065068122 72.2076392926264 56.3478068038327 cerebhem 56.519351568157 59.7242700170038 56.5623096226398 cortex 57.4964192808877 65.2060661989432 66.7814560627097 heart 55.4605319759636 68.9380334837466 48.9295191452238 kidney 57.8888057685521 68.2238919569109 53.122257191841 liver 55.6505013563562 68.3284953131617 50.8013034447912 stomach 64.493162300218 75.9489858757556 56.6886343456786 testicle 58.8080864730303 65.704162006095 57.6841847668285 diffExp=-13.5835742245044,-3.20491844884678,-7.70964691805547,-13.4775015077830,-10.3350861883588,-12.6779939568055,-11.4558235755375,-6.89607553306468 diffExpScore=0.987552996284983 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=-1,0,0,-1,0,-1,0,0 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 63.6247353484095 58.2745215864549 65.010007648217 cerebhem 59.0059359618402 68.6564644842872 64.6137834641211 cortex 59.3360928152559 58.4681954217133 58.260308091678 heart 60.2369754113214 56.7465045331039 60.5656698502459 kidney 61.7881946778215 64.4045373575463 61.352802483416 liver 58.4003755987119 62.752312064915 61.3272375971611 stomach 56.920337331605 66.0370738396804 62.084424422346 testicle 60.9126502075386 61.9861723152025 61.2574478184712 cont.diffExp=5.35021376195463,-9.65052852244703,0.867897393542542,3.49047087821759,-2.61634267972475,-4.35193646620307,-9.11673650807538,-1.07352210766387 cont.diffExpScore=2.01749565440622 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=0.647950443942189 cont.tran.correlation=-0.466761589754112 tran.covariance=0.00211647942851915 cont.tran.covariance=-0.00108150593337829 tran.mean=63.0764042459707 cont.tran.mean=61.096942434713 weightedLogRatios: wLogRatio Lung -0.87014438993581 cerebhem -0.224050044046864 cortex -0.517744156405913 heart -0.89721580765439 kidney -0.680189423739102 liver -0.845922245023246 stomach -0.694610112742621 testicle -0.457915427591428 cont.weightedLogRatios: wLogRatio Lung 0.360930977262027 cerebhem -0.62914205658162 cortex 0.06005696317707 heart 0.242854606523463 kidney -0.17187758158103 liver -0.294913942181393 stomach -0.611478086608822 testicle -0.0719464262539412 varWeightedLogRatios=0.0552384318607993 cont.varWeightedLogRatios=0.132675300671476 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.11414827634972 0.0759311528865383 54.182613063934 2.42322765105526e-255 *** df.mm.trans1 -0.305172138716262 0.0674283332805626 -4.52587397417153 7.04297877883873e-06 *** df.mm.trans2 0.21436849713643 0.0613348375290513 3.49505282434137 0.000503200932249244 *** df.mm.exp2 -0.230169107676079 0.082688475523713 -2.78356936947123 0.00551843110122227 ** df.mm.exp3 -0.291298069083308 0.082688475523713 -3.5228375809126 0.000454095145275376 *** df.mm.exp4 0.0393509821128667 0.082688475523713 0.475894395967934 0.634294984203938 df.mm.exp5 -0.0104249504612415 0.082688475523713 -0.126075011000195 0.899707990368862 df.mm.exp6 -0.00365184605431051 0.082688475523713 -0.0441639059274137 0.964786083572263 df.mm.exp7 0.139899537692036 0.082688475523713 1.69188676905666 0.0911032808571284 . df.mm.exp8 -0.114689207035196 0.082688475523713 -1.38700352508381 0.16587290724138 df.mm.trans1:exp2 0.193606913843925 0.0785165868603323 2.46580909315785 0.0139041059424479 * df.mm.trans2:exp2 0.0403617307319706 0.0661168830145314 0.610460277189711 0.541750934034426 df.mm.trans1:exp3 0.271875462597697 0.0785165868603323 3.46265003955556 0.000566722297970019 *** df.mm.trans2:exp3 0.189304725870397 0.0661168830145315 2.86318285495689 0.00431685419739352 ** df.mm.trans1:exp4 -0.0948246288795229 0.0785165868603323 -1.20770187130268 0.227561285717878 df.mm.trans2:exp4 -0.0856887941496049 0.0661168830145314 -1.29601987030713 0.195386827134145 df.mm.trans1:exp5 -0.00219629995656849 0.0785165868603323 -0.0279724328882932 0.977691943358551 df.mm.trans2:exp5 -0.0463260717675796 0.0661168830145315 -0.7006693246171 0.483737346518994 df.mm.trans1:exp6 -0.0484023461397564 0.0785165868603323 -0.616460140146641 0.537787089568409 df.mm.trans2:exp6 -0.051567113955716 0.0661168830145315 -0.779938672311311 0.435685072827648 df.mm.trans1:exp7 -0.0444856093331704 0.0785165868603323 -0.566575944167093 0.57118012232561 df.mm.trans2:exp7 -0.0893835089426855 0.0661168830145315 -1.35190143375392 0.176834335204617 df.mm.trans1:exp8 0.117823298561629 0.0785165868603323 1.50061666296342 0.133896177748824 df.mm.trans2:exp8 0.0203056314874524 0.0661168830145314 0.307117192487576 0.758843597084078 df.mm.trans1:probe2 0.100736628752372 0.0430053057617177 2.34242326541125 0.0194323693728226 * df.mm.trans1:probe3 0.125457941375744 0.0430053057617177 2.91726658266023 0.0036418426395585 ** df.mm.trans1:probe4 0.400079613034157 0.0430053057617177 9.30302914833124 1.63315653889921e-19 *** df.mm.trans1:probe5 0.287606926878692 0.0430053057617177 6.68770798822485 4.57236630081985e-11 *** df.mm.trans1:probe6 0.315032337807146 0.0430053057617177 7.32542955403378 6.44109231643025e-13 *** df.mm.trans1:probe7 0.0588496910546976 0.0430053057617177 1.36842861624493 0.171607775262655 df.mm.trans1:probe8 -0.0103144797890239 0.0430053057617177 -0.239842028938800 0.81052146484984 df.mm.trans1:probe9 0.267471594367086 0.0430053057617177 6.21950221326371 8.48253196721201e-10 *** df.mm.trans1:probe10 0.102137553577276 0.0430053057617177 2.37499889300162 0.0178118360228299 * df.mm.trans1:probe11 0.480624719132671 0.0430053057617177 11.1759400525065 7.72345420743814e-27 *** df.mm.trans1:probe12 0.338372825410980 0.0430053057617177 7.86816462335656 1.33195372210015e-14 *** df.mm.trans1:probe13 0.152457179428746 0.0430053057617177 3.54507837413075 0.000418056448877761 *** df.mm.trans1:probe14 0.097025845557283 0.0430053057617177 2.25613662869601 0.0243633928622293 * df.mm.trans1:probe15 0.334349388328654 0.0430053057617177 7.77460786306706 2.64157099186791e-14 *** df.mm.trans1:probe16 0.330526445502265 0.0430053057617177 7.685713184639 5.03191171502633e-14 *** df.mm.trans1:probe17 0.500103099784555 0.0430053057617177 11.6288697621523 9.45685329446043e-29 *** df.mm.trans1:probe18 0.607956269509677 0.0430053057617177 14.136773561807 3.42704086625534e-40 *** df.mm.trans1:probe19 0.756844252925864 0.0430053057617177 17.598857618159 7.18032885563851e-58 *** df.mm.trans1:probe20 0.689352242205615 0.0430053057617177 16.0294696199849 1.24322349481627e-49 *** df.mm.trans1:probe21 0.461740836690056 0.0430053057617177 10.7368341768910 4.91347924453688e-25 *** df.mm.trans1:probe22 0.419986763319097 0.0430053057617177 9.76592901457658 3.12105116551365e-21 *** df.mm.trans2:probe2 -0.194764790695299 0.0430053057617177 -4.52885492256339 6.94709651208341e-06 *** df.mm.trans2:probe3 -0.179185214662743 0.0430053057617177 -4.16658390142757 3.47041369081352e-05 *** df.mm.trans2:probe4 -0.350184680303647 0.0430053057617177 -8.14282503289101 1.71748267123678e-15 *** df.mm.trans2:probe5 0.220404437790994 0.0430053057617177 5.1250522205842 3.83183246699769e-07 *** df.mm.trans2:probe6 0.0140209897959069 0.0430053057617177 0.326029301444661 0.74449758352772 df.mm.trans3:probe2 -0.192791798307969 0.0430053057617177 -4.48297703953514 8.57131527328839e-06 *** df.mm.trans3:probe3 0.0201418960969902 0.0430053057617177 0.468358397649624 0.639671057957029 df.mm.trans3:probe4 0.205444638199743 0.0430053057617177 4.77719282681218 2.15768081695831e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.00269805692472 0.132012526750474 30.320592715345 1.75288920746109e-130 *** df.mm.trans1 0.0916072798207043 0.117229678630604 0.781434197302227 0.43480583126225 df.mm.trans2 0.0581953069324284 0.106635637313962 0.545739758286312 0.585415056590921 df.mm.exp2 0.0946995188833867 0.143760685463863 0.658730295962532 0.510281041349023 df.mm.exp3 0.0431535985708115 0.143760685463863 0.300176633351258 0.764129678868012 df.mm.exp4 -0.0104738635888029 0.143760685463863 -0.0728562440767974 0.941940889158385 df.mm.exp5 0.128629488962557 0.143760685463863 0.894747326416236 0.371223268081659 df.mm.exp6 0.0466675985396826 0.143760685463863 0.324620033558574 0.745563613139368 df.mm.exp7 0.0597477970077905 0.143760685463863 0.415605955237389 0.677823065818379 df.mm.exp8 0.0776406993015 0.143760685463863 0.540069067220861 0.589317569720373 df.mm.trans1:exp2 -0.170063785824302 0.136507515416550 -1.24581994848676 0.213238571305749 df.mm.trans2:exp2 0.0692507994246329 0.114949614957858 0.602444814191167 0.547069095562019 df.mm.trans1:exp3 -0.112938145214566 0.136507515416550 -0.827340127537574 0.408320338104013 df.mm.trans2:exp3 -0.0398356354824162 0.114949614957858 -0.346548663925673 0.729032378994241 df.mm.trans1:exp4 -0.0442420784640297 0.136507515416550 -0.324099946651478 0.745957152273505 df.mm.trans2:exp4 -0.0160970513804880 0.114949614957858 -0.140035713789815 0.888671210567647 df.mm.trans1:exp5 -0.157919482685710 0.136507515416550 -1.15685559292338 0.247717715210827 df.mm.trans2:exp5 -0.0286103777751046 0.114949614957858 -0.248894942237027 0.80351352401606 df.mm.trans1:exp6 -0.132347592549941 0.136507515416550 -0.9695260524381 0.332610632685871 df.mm.trans2:exp6 0.027362849145963 0.114949614957858 0.238042112241912 0.811916631420032 df.mm.trans1:exp7 -0.171097412696141 0.136507515416550 -1.25339188962630 0.210472818462811 df.mm.trans2:exp7 0.065303536850456 0.114949614957858 0.568105746803913 0.57014152505736 df.mm.trans1:exp8 -0.121202140487568 0.136507515416550 -0.88787888430701 0.374904478641167 df.mm.trans2:exp8 -0.0158943416082983 0.114949614957858 -0.138272247489697 0.890064189966755 df.mm.trans1:probe2 0.0910092502419904 0.0747682454626288 1.21721794698900 0.223922895122349 df.mm.trans1:probe3 0.0811068653299433 0.0747682454626288 1.08477689730573 0.278386105196873 df.mm.trans1:probe4 0.136107283207819 0.0747682454626288 1.82038888789826 0.0691177182734963 . df.mm.trans1:probe5 0.112332503781975 0.0747682454626288 1.50240925257664 0.133432956612223 df.mm.trans1:probe6 0.0247450846335999 0.0747682454626288 0.330957139364307 0.740773826365122 df.mm.trans1:probe7 -0.0095150144980951 0.0747682454626288 -0.127260101386904 0.898770318750147 df.mm.trans1:probe8 0.0927017919091716 0.0747682454626288 1.23985511945050 0.215435744603417 df.mm.trans1:probe9 0.0442729250149112 0.0747682454626288 0.592135401077988 0.553947115820848 df.mm.trans1:probe10 0.0806259753444178 0.0747682454626288 1.07834515636343 0.281243407694419 df.mm.trans1:probe11 0.0732833426088005 0.0747682454626288 0.980139926453532 0.327348573198409 df.mm.trans1:probe12 0.101810239633527 0.0747682454626288 1.36167752772017 0.173728568149482 df.mm.trans1:probe13 0.0644095596794375 0.0747682454626288 0.861456080464416 0.389275702127377 df.mm.trans1:probe14 0.0981450555757175 0.0747682454626288 1.3126569303378 0.189719754775455 df.mm.trans1:probe15 0.090662341812259 0.0747682454626288 1.21257816404926 0.225691636045368 df.mm.trans1:probe16 0.115735784938412 0.0747682454626288 1.54792698721625 0.122082359304865 df.mm.trans1:probe17 0.0639458478362072 0.0747682454626288 0.85525409136649 0.392696988546203 df.mm.trans1:probe18 -0.0165891025924799 0.0747682454626288 -0.221873637529339 0.824475608283453 df.mm.trans1:probe19 0.124094398477387 0.0747682454626288 1.65972061681470 0.0974091629398502 . df.mm.trans1:probe20 0.0502427376826407 0.0747682454626288 0.671979626802309 0.501813735476971 df.mm.trans1:probe21 0.0514267571728255 0.0747682454626288 0.687815487104483 0.49179204709165 df.mm.trans1:probe22 0.0555678148509053 0.0747682454626288 0.74320073324015 0.457604281979569 df.mm.trans2:probe2 -0.0117098080563151 0.0747682454626288 -0.156614723053359 0.875592709075979 df.mm.trans2:probe3 0.0412143576534784 0.0747682454626288 0.55122809688076 0.581649530053983 df.mm.trans2:probe4 0.067148360250014 0.0747682454626288 0.898086611963853 0.369441692590724 df.mm.trans2:probe5 -0.0702497166443743 0.0747682454626288 -0.939566205007273 0.347757412437673 df.mm.trans2:probe6 0.0163129212605203 0.0747682454626288 0.218179805605763 0.827351273202042 df.mm.trans3:probe2 0.079924939388121 0.0747682454626288 1.06896903750496 0.285444373082657 df.mm.trans3:probe3 0.0444651139538124 0.0747682454626288 0.594705863146639 0.552228244923572 df.mm.trans3:probe4 -0.036227740600743 0.0747682454626288 -0.484533780037016 0.628155489366341