fitVsDatCorrelation=0.864275515675649 cont.fitVsDatCorrelation=0.269440376099966 fstatistic=15058.4586562646,49,623 cont.fstatistic=4099.23145996999,49,623 residuals=-0.425652848445406,-0.0781986759451756,-0.0033590088989137,0.0769284822294114,0.389835521821970 cont.residuals=-0.525890109902516,-0.169665522843139,-0.0290346011491044,0.132350871909988,1.01782136419640 predictedValues: Include Exclude Both Lung 50.3188943820684 66.0271172791436 66.7302655313794 cerebhem 53.5356373391895 64.0787384329575 62.6705281035846 cortex 54.9257500274234 65.7913987182953 66.532388786275 heart 52.6190660689687 65.7479746988798 61.4101605615192 kidney 50.9683614270186 62.0855066285171 68.7832853691632 liver 52.7432727907157 66.6499269501056 64.2851621629959 stomach 50.0829916112531 78.2573515904823 59.5949055079189 testicle 52.1299959878189 63.630447523469 62.1368591563546 diffExp=-15.7082228970752,-10.5431010937680,-10.8656486908718,-13.1289086299111,-11.1171452014986,-13.9066541593898,-28.1743599792292,-11.5004515356500 diffExpScore=0.991375183235235 diffExp1.5=0,0,0,0,0,0,-1,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,0,0,-1,0 diffExp1.4Score=0.5 diffExp1.3=-1,0,0,0,0,0,-1,0 diffExp1.3Score=0.666666666666667 diffExp1.2=-1,0,0,-1,-1,-1,-1,-1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 60.3304963250877 60.9107821314506 60.2024999592073 cerebhem 59.2714851125171 68.8517899218816 66.0527838440115 cortex 63.2279846757725 60.5731562701437 62.5293333919607 heart 60.8847894154652 61.8392174770505 59.7684425539019 kidney 62.9657810390901 68.812524860352 58.2291062395476 liver 57.5195882667307 63.8069883734139 55.4829055107638 stomach 59.4714770217593 63.8201032537005 61.4755429945538 testicle 61.1797919393719 59.8491336867765 61.3480138355308 cont.diffExp=-0.580285806362951,-9.58030480936447,2.65482840562882,-0.954428061585254,-5.84674382126192,-6.28740010668324,-4.34862623194115,1.33065825259543 cont.diffExpScore=1.28323125832591 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=-0.411805265777406 cont.tran.correlation=-0.114436030297585 tran.covariance=-0.000906332662270129 cont.tran.covariance=-0.000224240773067370 tran.mean=59.3495269660192 cont.tran.mean=62.0821931106602 weightedLogRatios: wLogRatio Lung -1.10147113245508 cerebhem -0.731685173618303 cortex -0.73939855073919 heart -0.90758594907027 kidney -0.795121362198795 liver -0.955364477899778 stomach -1.84636080755404 testicle -0.808054898895297 cont.weightedLogRatios: wLogRatio Lung -0.039291456574427 cerebhem -0.62284087317036 cortex 0.176955417814347 heart -0.0640336169454665 kidney -0.371780952146605 liver -0.425736715732101 stomach -0.290809470112730 testicle 0.0902209757024507 varWeightedLogRatios=0.136318275116323 cont.varWeightedLogRatios=0.0768281825521403 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.94187810761232 0.0590974359134548 66.7013390121527 7.09159404349249e-286 *** df.mm.trans1 -0.0303136835878897 0.049778150277109 -0.608975693534955 0.54276253986414 df.mm.trans2 0.183170820939815 0.0458699086372809 3.99326762100727 7.29435650226776e-05 *** df.mm.exp2 0.094781315063418 0.0599051832109073 1.58218888555474 0.114113959732132 df.mm.exp3 0.0869949465489083 0.0599051832109073 1.45221067503669 0.146946455593666 df.mm.exp4 0.123544531642029 0.0599051832109074 2.06233459310303 0.0395899300978557 * df.mm.exp5 -0.079030679911548 0.0599051832109073 -1.31926280290815 0.187565920553751 df.mm.exp6 0.093773779872455 0.0599051832109073 1.56537005391181 0.118003940985400 df.mm.exp7 0.278326584514267 0.0599051832109073 4.64611857598976 4.12926327407391e-06 *** df.mm.exp8 0.0697056943755946 0.0599051832109074 1.16360038713483 0.245031444503439 df.mm.trans1:exp2 -0.0328144048416851 0.0522895307779392 -0.627552099884771 0.53052733312509 df.mm.trans2:exp2 -0.124734225382082 0.0433561885135412 -2.87696473464508 0.00415263526890182 ** df.mm.trans1:exp3 0.000606686700585309 0.0522895307779392 0.0116024506542573 0.990746505808896 df.mm.trans2:exp3 -0.0905713608381435 0.0433561885135412 -2.08900652809589 0.0371123529506705 * df.mm.trans1:exp4 -0.0788466451929786 0.0522895307779392 -1.50788588116847 0.132090650561019 df.mm.trans2:exp4 -0.127781189626590 0.0433561885135412 -2.94724222786976 0.00332614702534355 ** df.mm.trans1:exp5 0.0918551155429885 0.0522895307779392 1.75666360314218 0.0794664101171759 . df.mm.trans2:exp5 0.0174777284781797 0.0433561885135412 0.403119579404933 0.686998561213871 df.mm.trans1:exp6 -0.0467181860940534 0.0522895307779392 -0.893452004617409 0.371960160681534 df.mm.trans2:exp6 -0.0843853547799086 0.0433561885135412 -1.94632779478651 0.0520645660779772 . df.mm.trans1:exp7 -0.283025763271461 0.0522895307779392 -5.4126659593371 8.87031552447839e-08 *** df.mm.trans2:exp7 -0.108389334978068 0.0433561885135412 -2.49997379138195 0.0126762307614562 * df.mm.trans1:exp8 -0.0343458129718026 0.0522895307779392 -0.656839188663995 0.511526878882397 df.mm.trans2:exp8 -0.106679129449798 0.0433561885135412 -2.46052831457912 0.0141435056847944 * df.mm.trans1:probe2 -0.0581095531777812 0.0358001944105474 -1.62316306194865 0.105060410746144 df.mm.trans1:probe3 -0.0119480375746602 0.0358001944105474 -0.333742253956589 0.738686363275387 df.mm.trans1:probe4 -0.0226163774481408 0.0358001944105474 -0.631738956185043 0.52778920284121 df.mm.trans1:probe5 -0.0582487700286554 0.0358001944105474 -1.62705177968235 0.104231732489847 df.mm.trans1:probe6 -0.0606464211291696 0.0358001944105474 -1.69402491041507 0.0907603633405991 . df.mm.trans1:probe7 0.166789938835306 0.0358001944105474 4.65891153893195 3.88891363045975e-06 *** df.mm.trans1:probe8 -0.0129583282385761 0.0358001944105474 -0.361962510314143 0.717502783627045 df.mm.trans1:probe9 0.00785340939875518 0.0358001944105474 0.219367786350384 0.826435413311752 df.mm.trans1:probe10 0.146889161913072 0.0358001944105474 4.1030269341162 4.6197462629458e-05 *** df.mm.trans1:probe11 0.00497831446361783 0.0358001944105474 0.13905830807866 0.88944901196969 df.mm.trans1:probe12 0.0479734234974923 0.0358001944105474 1.340032485504 0.180723311081943 df.mm.trans2:probe2 0.482319135702823 0.0358001944105474 13.4725283938883 1.72040346735361e-36 *** df.mm.trans2:probe3 0.134645506776536 0.0358001944105474 3.76102725120585 0.000185166038996199 *** df.mm.trans2:probe4 -0.0745569565323793 0.0358001944105474 -2.08258524178331 0.0376964079314527 * df.mm.trans2:probe5 0.262497448887949 0.0358001944105474 7.3322911567936 7.06178646274499e-13 *** df.mm.trans2:probe6 0.235360416477302 0.0358001944105474 6.57427760805567 1.03485826675150e-10 *** df.mm.trans3:probe2 -0.0945795091504404 0.0358001944105474 -2.64187138387649 0.00845196124056725 ** df.mm.trans3:probe3 0.253133867805545 0.0358001944105474 7.07074003293587 4.1501408820168e-12 *** df.mm.trans3:probe4 -0.017434182975645 0.0358001944105474 -0.486985706717518 0.626439748544734 df.mm.trans3:probe5 -0.150784948050643 0.0358001944105474 -4.21184718500352 2.90684294324981e-05 *** df.mm.trans3:probe6 0.0409608229003738 0.0358001944105474 1.14415085098828 0.253000486726695 df.mm.trans3:probe7 0.257713443956763 0.0358001944105474 7.19866045981123 1.75677417075958e-12 *** df.mm.trans3:probe8 0.290845286011033 0.0358001944105474 8.12412588254953 2.42653275004213e-15 *** df.mm.trans3:probe9 0.0281290235491454 0.0358001944105474 0.785722647943442 0.432328712594563 df.mm.trans3:probe10 0.451249807292007 0.0358001944105474 12.604674771237 1.31261180572100e-32 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.07477263012066 0.113140599903349 36.0151230734287 2.07864877880712e-154 *** df.mm.trans1 0.0274746684966821 0.0952990548131197 0.288299485766775 0.773213389916934 df.mm.trans2 0.0590934558618802 0.0878168215002404 0.672917270886631 0.50124944556545 df.mm.exp2 0.0120963310824621 0.114687012406556 0.105472545047922 0.91603480375959 df.mm.exp3 0.00342900105708273 0.114687012406556 0.0298987739337667 0.976157358963966 df.mm.exp4 0.0315092794770769 0.114687012406557 0.274741479579039 0.78360594368485 df.mm.exp5 0.198057813763070 0.114687012406557 1.72694195800455 0.084673763465104 . df.mm.exp6 0.0803792650107631 0.114687012406557 0.700857606490131 0.48365355853058 df.mm.exp7 0.0113915919520739 0.114687012406556 0.0993276545707862 0.920910073637214 df.mm.exp8 -0.0224529470134215 0.114687012406557 -0.195775847171148 0.844849452502998 df.mm.trans1:exp2 -0.0298057181204571 0.100107031539311 -0.297738507097302 0.766002010474286 df.mm.trans2:exp2 0.110449685628811 0.0830043656230425 1.33064911465512 0.183791312119778 df.mm.trans1:exp3 0.0434802783374396 0.100107031539311 0.434337904829045 0.664193468024996 df.mm.trans2:exp3 -0.00898737749778894 0.0830043656230425 -0.108275961515137 0.913811652419715 df.mm.trans1:exp4 -0.0223636185061448 0.100107031539311 -0.223397079728238 0.823299707525465 df.mm.trans2:exp4 -0.0163817348097476 0.0830043656230425 -0.197359918201699 0.843610265500906 df.mm.trans1:exp5 -0.155304112301040 0.100107031539311 -1.55138065641326 0.121318309549589 df.mm.trans2:exp5 -0.0760822435419626 0.0830043656230425 -0.916605325164267 0.359704165788481 df.mm.trans1:exp6 -0.128091428968439 0.100107031539311 -1.27954477321743 0.201181594191457 df.mm.trans2:exp6 -0.0339267505635742 0.0830043656230425 -0.408734532321465 0.682874948103282 df.mm.trans1:exp7 -0.0257324913803312 0.100107031539311 -0.25704978945686 0.797225208183834 df.mm.trans2:exp7 0.0352664412476862 0.0830043656230425 0.424874535007542 0.671074837406748 df.mm.trans1:exp8 0.0364321656795398 0.100107031539311 0.363932134629655 0.716032160927796 df.mm.trans2:exp8 0.00486969863365442 0.0830043656230425 0.0586679820645791 0.953235374677564 df.mm.trans1:probe2 -0.0394862394771761 0.0685385991737024 -0.576116815243089 0.564744286107183 df.mm.trans1:probe3 0.0736905524654102 0.0685385991737025 1.07516864006296 0.282715443759012 df.mm.trans1:probe4 -0.00878764762731303 0.0685385991737024 -0.128214578839609 0.898020539452603 df.mm.trans1:probe5 0.0659681563810842 0.0685385991737024 0.962496420650445 0.336173724666229 df.mm.trans1:probe6 0.0148351056525473 0.0685385991737024 0.216448918294195 0.828708693228754 df.mm.trans1:probe7 0.0281650140820084 0.0685385991737024 0.410936529511316 0.681260382555266 df.mm.trans1:probe8 0.0199231285080805 0.0685385991737024 0.290684792923589 0.771389142071796 df.mm.trans1:probe9 -0.0276635728795245 0.0685385991737024 -0.403620342595779 0.686630419828635 df.mm.trans1:probe10 0.00353419492289912 0.0685385991737025 0.0515650300050947 0.95889181284978 df.mm.trans1:probe11 -0.0965645790247675 0.0685385991737024 -1.40890797578218 0.159361281645031 df.mm.trans1:probe12 -0.086624858952255 0.0685385991737024 -1.26388429288896 0.206744255318857 df.mm.trans2:probe2 -0.100600810546569 0.0685385991737024 -1.46779787972627 0.142663795647856 df.mm.trans2:probe3 -0.120544945262725 0.0685385991737024 -1.75878915991876 0.079104230522314 . df.mm.trans2:probe4 -0.111544255018879 0.0685385991737024 -1.62746622142341 0.104143723501986 df.mm.trans2:probe5 -0.00944113824380551 0.0685385991737024 -0.137749215152153 0.890483129816548 df.mm.trans2:probe6 -0.0491629380340355 0.0685385991737025 -0.717302930417912 0.473456048569052 df.mm.trans3:probe2 -0.0593060988056431 0.0685385991737025 -0.865294877932057 0.387210033369307 df.mm.trans3:probe3 -0.0446082037813684 0.0685385991737024 -0.650847906422986 0.515384638433171 df.mm.trans3:probe4 -0.0148463996899799 0.0685385991737024 -0.21661370189889 0.828580317564847 df.mm.trans3:probe5 -0.0955797578004112 0.0685385991737024 -1.39453912033096 0.163651921085002 df.mm.trans3:probe6 -0.0649662618808167 0.0685385991737024 -0.947878460663719 0.343558850848104 df.mm.trans3:probe7 -0.0932053494187983 0.0685385991737024 -1.35989574549928 0.174354831074576 df.mm.trans3:probe8 -0.099467795011671 0.0685385991737024 -1.45126682206595 0.147208914601427 df.mm.trans3:probe9 -0.0114000437506461 0.0685385991737024 -0.166330270651638 0.867951011100363 df.mm.trans3:probe10 -0.152888844511738 0.0685385991737025 -2.23069695551058 0.0260564573106632 *