fitVsDatCorrelation=0.718707754405206 cont.fitVsDatCorrelation=0.259866272143530 fstatistic=10591.1078599601,61,899 cont.fstatistic=5484.09428310134,61,899 residuals=-0.514696349478289,-0.0903946192106008,-0.00606905010534582,0.0703641146297792,1.71499957248489 cont.residuals=-0.523891287791798,-0.146052539031997,-0.0277138512999605,0.112790423429780,1.79566832675917 predictedValues: Include Exclude Both Lung 64.2845217960303 67.0556876088991 66.5250998350095 cerebhem 64.3634513340927 60.976884766735 67.0223893804653 cortex 60.3712126443376 66.120971439173 63.4447079521477 heart 62.3535343176457 62.7934061414443 64.995290700535 kidney 74.4622988258942 65.794014986614 79.3967444451104 liver 60.5047946054023 61.9471789138776 59.3544858576482 stomach 65.6974806769762 72.3520203648568 66.86008820104 testicle 62.7418295767568 62.9855511921514 66.2765968158377 diffExp=-2.7711658128688,3.38656656735773,-5.7497587948353,-0.439871823798619,8.66828383928015,-1.44238430847533,-6.6545396878806,-0.24372161539457 diffExpScore=4.69956964655969 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 66.269075424023 67.196544809709 67.7466971543039 cerebhem 65.3623753576917 66.5457625807888 66.2876986547077 cortex 63.9970600653503 66.0597954659543 69.498390436275 heart 67.783471655894 65.867763820895 65.5029098278615 kidney 72.1352408504519 66.4173719793883 60.8057782049936 liver 66.8285504073792 62.2923606036526 69.7475130679519 stomach 64.8327328841662 68.5496156776998 71.2132722411041 testicle 69.0959633558984 69.293002357582 72.6485922355902 cont.diffExp=-0.927469385685995,-1.18338722309717,-2.06273540060405,1.91570783499908,5.7178688710635,4.5361898037266,-3.71688279353364,-0.197039001683606 cont.diffExpScore=3.98588608034543 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=0.282619408520881 cont.tran.correlation=0.0503751701825532 tran.covariance=0.00109773980065968 cont.tran.covariance=5.81396810012212e-05 tran.mean=64.6753024494304 cont.tran.mean=66.7829179560328 weightedLogRatios: wLogRatio Lung -0.176601607027454 cerebhem 0.223637495925527 cortex -0.377176409887751 heart -0.0290772559030676 kidney 0.525800602702064 liver -0.096935442410321 stomach -0.408440822473466 testicle -0.0160544885307437 cont.weightedLogRatios: wLogRatio Lung -0.058382944407277 cerebhem -0.0751620863658859 cortex -0.132434710835862 heart 0.120467641793161 kidney 0.349929337811107 liver 0.292903993387046 stomach -0.234120579188201 testicle -0.0120651103960472 varWeightedLogRatios=0.0945020232244848 cont.varWeightedLogRatios=0.0423467119321331 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.75425038641855 0.0745651808337277 50.3485721410657 7.32218902366758e-264 *** df.mm.trans1 0.307689241407687 0.0641395067877103 4.7971875185458 1.88308147859965e-06 *** df.mm.trans2 0.446990183161174 0.056418612781649 7.92274324239614 6.8461104561403e-15 *** df.mm.exp2 -0.101248931812716 0.0720137308810309 -1.40596703675834 0.16007943636536 df.mm.exp3 -0.0294334399211965 0.0720137308810309 -0.408719831080847 0.68284261890579 df.mm.exp4 -0.0729073892826258 0.0720137308810309 -1.01240955565920 0.311614669398871 df.mm.exp5 -0.0488985462825385 0.0720137308810309 -0.679016983071194 0.497301869247908 df.mm.exp6 -0.0257860194455253 0.0720137308810309 -0.358070872457986 0.720374332993875 df.mm.exp7 0.0927387576460468 0.0720137308810309 1.28779270996603 0.198149522186005 df.mm.exp8 -0.0831661267884058 0.0720137308810309 -1.15486485384015 0.248452646081505 df.mm.trans1:exp2 0.102475994253662 0.0662429977660692 1.54697096613209 0.122222179592731 df.mm.trans2:exp2 0.00622035340861327 0.0475452864826287 0.130830075256481 0.895939000722005 df.mm.trans1:exp3 -0.0333730642997514 0.0662429977660692 -0.503797615222746 0.614527033284643 df.mm.trans2:exp3 0.0153959723166738 0.0475452864826287 0.32381700596754 0.746151897105317 df.mm.trans1:exp4 0.0424088612312914 0.0662429977660692 0.640201419945609 0.522204787152678 df.mm.trans2:exp4 0.00723402698450116 0.0475452864826287 0.152150244948975 0.879102574199052 df.mm.trans1:exp5 0.195872603852746 0.0662429977660692 2.95688013009995 0.003188978232843 ** df.mm.trans2:exp5 0.0299039903147444 0.0475452864826287 0.628958042469051 0.529536301955395 df.mm.trans1:exp6 -0.034810252679172 0.0662429977660692 -0.525493317831132 0.599369879904511 df.mm.trans2:exp6 -0.0534553441414966 0.0475452864826287 -1.12430375534759 0.261184292975032 df.mm.trans1:exp7 -0.0709970624485097 0.0662429977660693 -1.07176705225855 0.284112305059388 df.mm.trans2:exp7 -0.016718812593291 0.0475452864826287 -0.351639748756156 0.725190852482236 df.mm.trans1:exp8 0.0588756066771965 0.0662429977660693 0.888782341721763 0.374357812096232 df.mm.trans2:exp8 0.0205480482078347 0.0475452864826287 0.432178449809998 0.665715359971875 df.mm.trans1:probe2 0.446383198486295 0.0468408729265129 9.52977966885055 1.41937049616978e-20 *** df.mm.trans1:probe3 0.147871371463796 0.0468408729265129 3.15688761171864 0.00164776648290869 ** df.mm.trans1:probe4 0.367656905494131 0.0468408729265129 7.84906178138346 1.18887515555764e-14 *** df.mm.trans1:probe5 0.109401455892985 0.0468408729265129 2.33559814447996 0.0197312969634371 * df.mm.trans1:probe6 0.270531420221243 0.0468408729265129 5.77554181463849 1.05630299840575e-08 *** df.mm.trans1:probe7 0.0686495121658638 0.0468408729265129 1.46558994051127 0.143109684283143 df.mm.trans1:probe8 0.103563738563476 0.0468408729265129 2.21096943957372 0.0272889860605292 * df.mm.trans1:probe9 -0.105452156679914 0.0468408729265129 -2.25128504426794 0.0246082889916891 * df.mm.trans1:probe10 0.334927352393143 0.0468408729265129 7.15032260219829 1.79270484550598e-12 *** df.mm.trans1:probe11 0.107140830091371 0.0468408729265129 2.28733632397204 0.0224077522525349 * df.mm.trans1:probe12 0.0646657655930555 0.0468408729265129 1.38054142787876 0.167763142888750 df.mm.trans1:probe13 0.0556327667921458 0.0468408729265129 1.18769705422498 0.235266483271870 df.mm.trans1:probe14 -0.00654904440670818 0.0468408729265129 -0.139814738657470 0.888837684542327 df.mm.trans1:probe15 -0.00230269941030189 0.0468408729265129 -0.0491600447736002 0.960802671716171 df.mm.trans1:probe16 0.278000967689212 0.0468408729265129 5.9350082592474 4.19169452685371e-09 *** df.mm.trans1:probe17 0.234071222007387 0.0468408729265129 4.99715755457021 6.99178403917864e-07 *** df.mm.trans1:probe18 0.311108180382038 0.0468408729265129 6.64181004632695 5.35875956094442e-11 *** df.mm.trans1:probe19 0.155699067000139 0.0468408729265129 3.3240001151219 0.00092322816179267 *** df.mm.trans1:probe20 0.169598059242377 0.0468408729265129 3.62072798063465 0.000310200048218867 *** df.mm.trans1:probe21 0.0382439243778758 0.0468408729265129 0.816464809224957 0.414450632506029 df.mm.trans1:probe22 0.298052862262438 0.0468408729265129 6.36309367526185 3.14611502897649e-10 *** df.mm.trans2:probe2 0.334639048034921 0.0468408729265129 7.14416762812951 1.87036856520594e-12 *** df.mm.trans2:probe3 0.0965786854375088 0.0468408729265129 2.06184640472068 0.0395093085800987 * df.mm.trans2:probe4 -0.148480192793332 0.0468408729265129 -3.16988526294713 0.00157659318407363 ** df.mm.trans2:probe5 -0.11269749607489 0.0468408729265129 -2.40596489847013 0.0163307851825528 * df.mm.trans2:probe6 -0.0929485118245705 0.0468408729265129 -1.98434627745718 0.0475209343618056 * df.mm.trans3:probe2 -0.457949907974599 0.0468408729265129 -9.77671591844715 1.60049119392501e-21 *** df.mm.trans3:probe3 -0.283285281765866 0.0468408729265129 -6.04782242658677 2.15106335278142e-09 *** df.mm.trans3:probe4 -0.173739903014932 0.0468408729265129 -3.70915169082155 0.000220709238581340 *** df.mm.trans3:probe5 -0.53669319510729 0.0468408729265129 -11.4577966117175 1.82160667199608e-28 *** df.mm.trans3:probe6 -0.329430542718077 0.0468408729265129 -7.03297189262271 4.00248622559593e-12 *** df.mm.trans3:probe7 -0.413958618971357 0.0468408729265129 -8.83755133301642 5.07140808101753e-18 *** df.mm.trans3:probe8 -0.468476210389821 0.0468408729265129 -10.0014406461809 2.11397788287890e-22 *** df.mm.trans3:probe9 -0.244418301902836 0.0468408729265129 -5.21805608290639 2.24584021732465e-07 *** df.mm.trans3:probe10 -0.368944772063609 0.0468408729265129 -7.87655628541412 9.6808713491167e-15 *** df.mm.trans3:probe11 -0.100674494796140 0.0468408729265129 -2.14928733190102 0.0318779716371898 * df.mm.trans3:probe12 -0.358634692222958 0.0468408729265129 -7.65644766666941 4.92945579317689e-14 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.17515009911712 0.103555578633882 40.3179640749075 9.00450813609053e-204 *** df.mm.trans1 0.00996729484984822 0.089076478651666 0.111895923600947 0.910930906862391 df.mm.trans2 0.0444827494033002 0.078353757437438 0.567716863340188 0.570368986827038 df.mm.exp2 -0.00173715260660916 0.100012143571395 -0.0173694167985617 0.986145761324018 df.mm.exp3 -0.0774755994691305 0.100012143571395 -0.774661923067606 0.438743146766485 df.mm.exp4 0.0363034955855101 0.100012143571395 0.362990875798941 0.716697028361357 df.mm.exp5 0.181247054059562 0.100012143571395 1.8122504686661 0.0702810074569442 . df.mm.exp6 -0.0964820549993793 0.100012143571395 -0.964703400547596 0.334952816542188 df.mm.exp7 -0.0518802724200571 0.100012143571395 -0.518739730671021 0.604069911398706 df.mm.exp8 0.00263674822426772 0.100012143571395 0.0263642806774303 0.978972635083885 df.mm.trans1:exp2 -0.0120394091709073 0.0919977915617874 -0.130866284576205 0.89591036436792 df.mm.trans2:exp2 -0.00799480697992462 0.06603054667032 -0.121077401037453 0.903656784576004 df.mm.trans1:exp3 0.0425893909439625 0.0919977915617874 0.462939275182043 0.643519932451251 df.mm.trans2:exp3 0.0604140935559618 0.0660305466703201 0.914941592981195 0.36046762830282 df.mm.trans1:exp4 -0.0137084655958956 0.0919977915617874 -0.149008637742013 0.881580235583202 df.mm.trans2:exp4 -0.0562761715638018 0.0660305466703201 -0.8522748091846 0.394288569424248 df.mm.trans1:exp5 -0.0964277061334708 0.0919977915617874 -1.04815240123137 0.294850099968879 df.mm.trans2:exp5 -0.192910235284773 0.0660305466703201 -2.92153018583873 0.00357034091352132 ** df.mm.trans1:exp6 0.104889091189302 0.0919977915617874 1.14012618573411 0.254537391371884 df.mm.trans2:exp6 0.0206990208546548 0.0660305466703201 0.313476442319971 0.753991431664559 df.mm.trans1:exp7 0.0299675309899920 0.0919977915617874 0.325741851855924 0.744695479214483 df.mm.trans2:exp7 0.0718162422753942 0.06603054667032 1.08762150090868 0.27705373680414 df.mm.trans1:exp8 0.0391362090666738 0.0919977915617874 0.425403788528871 0.670644155328512 df.mm.trans2:exp8 0.0280853471070528 0.0660305466703201 0.425338703422803 0.67069157629346 df.mm.trans1:probe2 -0.0165961794156102 0.0650522622675264 -0.255120711211528 0.798688219880183 df.mm.trans1:probe3 -0.03427968218675 0.0650522622675264 -0.526956034915055 0.598354119655874 df.mm.trans1:probe4 -0.0195093098763005 0.0650522622675264 -0.299902097117988 0.764321162493449 df.mm.trans1:probe5 0.0231086733636111 0.0650522622675264 0.355232432479857 0.72249879463136 df.mm.trans1:probe6 0.0146167022748060 0.0650522622675264 0.224691682738028 0.822270194368444 df.mm.trans1:probe7 -0.0200915589249579 0.0650522622675264 -0.308852578290539 0.757505229876155 df.mm.trans1:probe8 0.0082659848030194 0.0650522622675264 0.127066830804833 0.898915918398344 df.mm.trans1:probe9 -0.0403058590862846 0.0650522622675264 -0.619591966233662 0.53568345135282 df.mm.trans1:probe10 -0.00125969325931916 0.0650522622675264 -0.0193643267030237 0.98455476502188 df.mm.trans1:probe11 0.0234737082799216 0.0650522622675264 0.360843842499841 0.718300958991626 df.mm.trans1:probe12 -0.0102885228172754 0.0650522622675264 -0.158157802029452 0.874367972195914 df.mm.trans1:probe13 0.00373194413849843 0.0650522622675264 0.0573683990135634 0.954264483544008 df.mm.trans1:probe14 0.0736673951587714 0.0650522622675264 1.13243402444354 0.257753951151023 df.mm.trans1:probe15 0.0352062182800084 0.0650522622675264 0.541198984521451 0.588504516636481 df.mm.trans1:probe16 0.0997510774730307 0.0650522622675264 1.53339905479084 0.125529383094466 df.mm.trans1:probe17 0.142122880453195 0.0650522622675264 2.18474923852328 0.0291648386633143 * df.mm.trans1:probe18 -0.0350661242639577 0.0650522622675264 -0.53904542350501 0.589988972999611 df.mm.trans1:probe19 0.0275606535777735 0.0650522622675264 0.423669410057267 0.671908270891007 df.mm.trans1:probe20 0.00808166149218221 0.0650522622675264 0.124233365765921 0.901158276204217 df.mm.trans1:probe21 -0.0209698640654704 0.0650522622675264 -0.322354109365668 0.747259393273965 df.mm.trans1:probe22 0.0313825476522744 0.0650522622675264 0.482420542474206 0.629624667708479 df.mm.trans2:probe2 -0.0329720501963495 0.0650522622675264 -0.506854781786872 0.612381007534999 df.mm.trans2:probe3 -0.0661242025500793 0.0650522622675264 -1.01647813996298 0.309675228421466 df.mm.trans2:probe4 -0.0646356616191895 0.0650522622675264 -0.993595908369433 0.320686989168747 df.mm.trans2:probe5 -0.0416528052673587 0.0650522622675264 -0.640297567147814 0.522142318179347 df.mm.trans2:probe6 -0.0108136194038423 0.0650522622675264 -0.166229720949157 0.86801354792182 df.mm.trans3:probe2 -0.0466066990260381 0.0650522622675264 -0.716450087997997 0.473899546406436 df.mm.trans3:probe3 -0.0285078758426818 0.0650522622675264 -0.438230352780716 0.661324586961596 df.mm.trans3:probe4 -0.00595965524485813 0.0650522622675264 -0.0916133434429866 0.927025658684244 df.mm.trans3:probe5 -0.0171294127794378 0.0650522622675264 -0.263317710750679 0.792366048576234 df.mm.trans3:probe6 0.00531186371317766 0.0650522622675264 0.0816553264717022 0.934938979315535 df.mm.trans3:probe7 -0.076024085711928 0.0650522622675264 -1.16866167389045 0.242849767364451 df.mm.trans3:probe8 0.0859881847078007 0.0650522622675264 1.32183235002921 0.186560231312373 df.mm.trans3:probe9 -0.0128673821427356 0.0650522622675264 -0.197800686620531 0.843245724422306 df.mm.trans3:probe10 -0.0241474368560956 0.0650522622675264 -0.371200570347418 0.710575606998854 df.mm.trans3:probe11 -0.00362472931573216 0.0650522622675264 -0.0557202653587284 0.955577035855863 df.mm.trans3:probe12 -0.0423259949668613 0.0650522622675264 -0.650646011245486 0.51544127096366