fitVsDatCorrelation=0.901731421172185 cont.fitVsDatCorrelation=0.25544076613806 fstatistic=12056.8417926205,67,1037 cont.fstatistic=2398.08791370596,67,1037 residuals=-0.550489765693809,-0.0945214936137621,-0.00161492718734733,0.0914690451093933,0.744696532992167 cont.residuals=-0.660930924049101,-0.251684992250368,-0.0854504724503246,0.209798896722779,1.18520508097077 predictedValues: Include Exclude Both Lung 61.3528850588747 48.4652714853318 55.6635577513177 cerebhem 59.92030107064 50.1847830863957 58.4385866885882 cortex 53.9787398762906 46.3440972722085 59.4400501964612 heart 53.5831953870347 48.9556904642812 60.9341925555538 kidney 68.0263216760272 47.9448211279475 65.8609100167256 liver 61.5886749480528 45.632707147793 71.9348590235926 stomach 53.4651196863746 46.7611239079478 61.5826789771652 testicle 56.057608251717 47.8626205278981 70.9084445961908 diffExp=12.8876135735429,9.73551798424435,7.63464260408217,4.62750492275349,20.0815005480798,15.9559678002598,6.70399577842681,8.19498772381889 diffExpScore=0.988482146241172 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,1,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,0,0,1,1,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=1,0,0,0,1,1,0,0 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 61.6839217220123 59.5871417347912 61.8913637091386 cerebhem 64.9633669397905 65.8302669836755 60.4283691296849 cortex 63.0924352864034 63.0987280821526 65.9139517382966 heart 59.639663957 71.3429624717772 65.67001427147 kidney 56.9484942525295 68.3264333635599 64.8175925200088 liver 65.3386657326001 66.0864230615853 61.3470555061729 stomach 58.6102930858116 69.3361955579953 63.943522068488 testicle 63.3688075768096 59.9524516932835 60.8167229682076 cont.diffExp=2.09677998722108,-0.86690004388501,-0.00629279574921071,-11.7032985147771,-11.3779391110304,-0.747757328985173,-10.7259024721836,3.41635588352605 cont.diffExpScore=1.32431785643636 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=0.111093254719191 cont.tran.correlation=-0.50955983487825 tran.covariance=0.000311229478782766 cont.tran.covariance=-0.00166199631976403 tran.mean=53.132747560926 cont.tran.mean=63.5753907188611 weightedLogRatios: wLogRatio Lung 0.942882867284384 cerebhem 0.709987718301936 cortex 0.5966178129533 heart 0.355505900475795 kidney 1.41510913966216 liver 1.19058286547956 stomach 0.524124670057115 testicle 0.623860274160443 cont.weightedLogRatios: wLogRatio Lung 0.141955987499065 cerebhem -0.0554168678555605 cortex -0.00041336397756401 heart -0.748588891122995 kidney -0.752864153678463 liver -0.0476256527423491 stomach -0.698265458009635 testicle 0.228400203362766 varWeightedLogRatios=0.129327166302548 cont.varWeightedLogRatios=0.175064099606975 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.48566128029689 0.0701937650850554 49.6577050123075 2.32429895618618e-276 *** df.mm.trans1 0.532461382529442 0.0580249588437961 9.1764198224222 2.3462469615866e-19 *** df.mm.trans2 0.390340476614735 0.0528399467467152 7.38722312658274 3.07748886540703e-13 *** df.mm.exp2 -0.0374131963725169 0.0671180769812447 -0.557423544523952 0.577358380114899 df.mm.exp3 -0.238448061614999 0.0671180769812447 -3.55266527796424 0.000398503182865314 *** df.mm.exp4 -0.215807370958221 0.0671180769812447 -3.21533900648741 0.00134315649511515 ** df.mm.exp5 -0.0757636031947965 0.0671180769812447 -1.12881069605090 0.259238765505644 df.mm.exp6 -0.312822259369342 0.0671180769812447 -4.66077506148986 3.5603400055862e-06 *** df.mm.exp7 -0.274463013251746 0.0671180769812447 -4.08925621227856 4.6624594274837e-05 *** df.mm.exp8 -0.344838828888092 0.0671180769812447 -5.13779363768799 3.31998362766585e-07 *** df.mm.trans1:exp2 0.0137863638365012 0.0564882791604539 0.244057068853900 0.807234897321418 df.mm.trans2:exp2 0.07227756174225 0.0433245323963047 1.66828255827672 0.0955615321988365 . df.mm.trans1:exp3 0.110396128386356 0.0564882791604539 1.95431919731132 0.0509319383697829 . df.mm.trans2:exp3 0.193694504717061 0.0433245323963047 4.47078119494215 8.65166841150604e-06 *** df.mm.trans1:exp4 0.0804006747033001 0.0564882791604539 1.42331605597196 0.154945332734302 df.mm.trans2:exp4 0.225875494118246 0.0433245323963047 5.21357027127456 2.23512724190525e-07 *** df.mm.trans1:exp5 0.179016120668664 0.0564882791604539 3.16908433624208 0.00157395960759101 ** df.mm.trans2:exp5 0.0649669034105019 0.0433245323963047 1.49954078710479 0.134037759737394 df.mm.trans1:exp6 0.316658068485011 0.0564882791604539 5.605730484116 2.65772178239928e-08 *** df.mm.trans2:exp6 0.252599491186186 0.0433245323963047 5.83040317378547 7.37906054242943e-09 *** df.mm.trans1:exp7 0.136850289775111 0.0564882791604539 2.42263159382835 0.0155790211193318 * df.mm.trans2:exp7 0.238667695630122 0.0433245323963047 5.5088348893635 4.55545153789482e-08 *** df.mm.trans1:exp8 0.254576513359595 0.0564882791604539 4.50671390849906 7.33258240435535e-06 *** df.mm.trans2:exp8 0.332326174292801 0.0433245323963047 7.67062345307957 3.93200628681435e-14 *** df.mm.trans1:probe2 -0.0323251539611569 0.0443944350423156 -0.72813527034065 0.466695164338304 df.mm.trans1:probe3 -0.0201032306353445 0.0443944350423156 -0.452832221339965 0.650764283034419 df.mm.trans1:probe4 0.00898806435699082 0.0443944350423156 0.202459257526842 0.839597419416154 df.mm.trans1:probe5 0.0399702840981971 0.0443944350423156 0.900344470204396 0.368145996020457 df.mm.trans1:probe6 0.196631369421506 0.0443944350423156 4.4291895872553 1.04622278462718e-05 *** df.mm.trans1:probe7 0.650837811188758 0.0443944350423156 14.6603467431987 2.29820935469253e-44 *** df.mm.trans1:probe8 0.624606794251779 0.0443944350423156 14.0694840165534 2.82062181836558e-41 *** df.mm.trans1:probe9 0.602540169453211 0.0443944350423156 13.5724256627860 9.60977723825525e-39 *** df.mm.trans1:probe10 0.650502851290872 0.0443944350423156 14.6528016556767 2.51975192367678e-44 *** df.mm.trans1:probe11 0.657282656640237 0.0443944350423156 14.8055191154867 3.88858075785712e-45 *** df.mm.trans1:probe12 0.56184972187167 0.0443944350423156 12.6558592611017 3.05781541046804e-34 *** df.mm.trans2:probe2 -0.0820180489173344 0.0443944350423156 -1.84748491199757 0.0649615606120896 . df.mm.trans2:probe3 0.0597565825793677 0.0443944350423156 1.34603768518305 0.178584539728378 df.mm.trans2:probe4 -0.00671790209634121 0.0443944350423156 -0.151323067630839 0.879750305040102 df.mm.trans2:probe5 -0.00817547091312585 0.0443944350423156 -0.184155309225880 0.853927621958244 df.mm.trans2:probe6 0.201909750991550 0.0443944350423156 4.54808695727504 6.05214488220948e-06 *** df.mm.trans3:probe2 0.0650975537883165 0.0443944350423156 1.46634490846132 0.142857510960107 df.mm.trans3:probe3 -0.198028316856528 0.0443944350423156 -4.46065631126452 9.06257834361442e-06 *** df.mm.trans3:probe4 0.286547128852879 0.0443944350423156 6.45457315944556 1.66338548020281e-10 *** df.mm.trans3:probe5 -0.189251276878112 0.0443944350423156 -4.26295045083292 2.20131347490721e-05 *** df.mm.trans3:probe6 0.125786241253947 0.0443944350423156 2.83337857850992 0.00469516377202061 ** df.mm.trans3:probe7 -0.0334871321591478 0.0443944350423156 -0.754309231038277 0.450834811144087 df.mm.trans3:probe8 -0.0125249944602060 0.0443944350423156 -0.282129831098593 0.777900255328822 df.mm.trans3:probe9 -0.275150943600621 0.0443944350423156 -6.19787014607472 8.24586509608056e-10 *** df.mm.trans3:probe10 -0.411171413598645 0.0443944350423156 -9.2617782658283 1.12428661098822e-19 *** df.mm.trans3:probe11 -0.692672914140763 0.0443944350423156 -15.6026969029007 1.85121574752298e-49 *** df.mm.trans3:probe12 -0.716751070861557 0.0443944350423156 -16.1450657087621 1.76865479210221e-52 *** df.mm.trans3:probe13 -0.668813034780953 0.0443944350423156 -15.0652448700712 1.57476330308438e-46 *** df.mm.trans3:probe14 -0.702970256186248 0.0443944350423156 -15.8346480930818 9.62931196070307e-51 *** df.mm.trans3:probe15 -0.511020960674657 0.0443944350423156 -11.5109238396111 6.11686252031162e-29 *** df.mm.trans3:probe16 -0.676861293930175 0.0443944350423156 -15.2465346903280 1.64484708247742e-47 *** df.mm.trans3:probe17 -0.756906178062352 0.0443944350423156 -17.0495733832605 1.19757732796027e-57 *** df.mm.trans3:probe18 -0.725475763024499 0.0443944350423156 -16.3415924165494 1.37504543987316e-53 *** df.mm.trans3:probe19 -0.720866983286904 0.0443944350423156 -16.2377780593399 5.31245529522044e-53 *** df.mm.trans3:probe20 -0.464376062637684 0.0443944350423156 -10.4602313824932 2.05328188912106e-24 *** df.mm.trans3:probe21 -0.404937463294717 0.0443944350423156 -9.12135637966204 3.75990727287115e-19 *** df.mm.trans3:probe22 -0.572794202567196 0.0443944350423156 -12.9023874731422 1.97800047917492e-35 *** df.mm.trans3:probe23 -0.474197049610742 0.0443944350423156 -10.6814525099542 2.44120226692139e-25 *** df.mm.trans3:probe24 -0.524475341610388 0.0443944350423156 -11.8139884224334 2.62854655962769e-30 *** df.mm.trans3:probe25 -0.462382159090228 0.0443944350423156 -10.4153180156364 3.15025720106749e-24 *** df.mm.trans3:probe26 -0.342269227063669 0.0443944350423156 -7.70973268918564 2.94458194935194e-14 *** df.mm.trans3:probe27 -0.38749972584839 0.0443944350423156 -8.72856531407677 1.01464098646553e-17 *** df.mm.trans3:probe28 -0.448396208531573 0.0443944350423156 -10.1002796432520 6.08650499378777e-23 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.01921584455332 0.156987238349821 25.6021819786214 1.95974340753317e-112 *** df.mm.trans1 0.0915782168740526 0.129771896880182 0.70568604663771 0.480541891274421 df.mm.trans2 0.0704166882028054 0.118175699854067 0.595864363737735 0.551395869035291 df.mm.exp2 0.175362093196736 0.150108510860883 1.16823551303668 0.242980284044129 df.mm.exp3 0.0168688929940548 0.150108510860883 0.112377991742843 0.910545453742615 df.mm.exp4 0.0870946758401163 0.150108510860883 0.580211443978908 0.56189808409181 df.mm.exp5 0.0107844367564750 0.150108510860883 0.0718442724841214 0.942739692867763 df.mm.exp6 0.169917669217694 0.150108510860883 1.13196559104613 0.257910582566060 df.mm.exp7 0.0677947101197952 0.150108510860883 0.451638016598712 0.651624217733729 df.mm.exp8 0.050576255458484 0.150108510860883 0.336931298354941 0.736236866991754 df.mm.trans1:exp2 -0.123561876695338 0.126335137227472 -0.978048383110234 0.328278501382762 df.mm.trans2:exp2 -0.075722184162244 0.096894627114699 -0.781490020830648 0.434692722193341 df.mm.trans1:exp3 0.00570867586176569 0.126335137227472 0.045186762661974 0.963967145003687 df.mm.trans2:exp3 0.040391911082638 0.096894627114699 0.416864301823713 0.676863941173849 df.mm.trans1:exp4 -0.120797129461788 0.126335137227472 -0.956164152845996 0.339212153137829 df.mm.trans2:exp4 0.0929642212350326 0.096894627114699 0.95943628664762 0.337562640533182 df.mm.trans1:exp5 -0.0906604960064588 0.126335137227472 -0.717619009216894 0.473153853282011 df.mm.trans2:exp5 0.126072465357418 0.096894627114699 1.30112957871420 0.193503120411925 df.mm.trans1:exp6 -0.112356992608286 0.126335137227472 -0.889356635644305 0.37401770123858 df.mm.trans2:exp6 -0.0663941515241879 0.096894627114699 -0.685220156176398 0.493358141517204 df.mm.trans1:exp7 -0.118907688011472 0.126335137227472 -0.941208365471387 0.346817287469628 df.mm.trans2:exp7 0.0837325540571477 0.096894627114699 0.864160960731386 0.387699313077492 df.mm.trans1:exp8 -0.0236278181192500 0.126335137227472 -0.187024913557557 0.85167768268369 df.mm.trans2:exp8 -0.0444642872773969 0.096894627114699 -0.458893218349066 0.646407014072408 df.mm.trans1:probe2 0.0492164578604887 0.0992874473530334 0.495696678407807 0.620213461368088 df.mm.trans1:probe3 0.0722555840120441 0.0992874473530334 0.727741380590913 0.46693619008231 df.mm.trans1:probe4 0.0923067174447588 0.0992874473530334 0.929691717388468 0.352747084006322 df.mm.trans1:probe5 -0.0201783177467562 0.0992874473530335 -0.203231307528823 0.83899411353965 df.mm.trans1:probe6 0.0235660725939838 0.0992874473530334 0.237351983782911 0.812430619065983 df.mm.trans1:probe7 0.0700600142120618 0.0992874473530335 0.705628113924125 0.480577911966754 df.mm.trans1:probe8 0.0714534632172708 0.0992874473530334 0.719662607129035 0.471894910324862 df.mm.trans1:probe9 0.112929809565676 0.0992874473530334 1.13740268862119 0.255632718564718 df.mm.trans1:probe10 0.03128928653237 0.0992874473530334 0.315138392279495 0.752719981497606 df.mm.trans1:probe11 -0.0366246358073839 0.0992874473530335 -0.368874785119198 0.712296355904632 df.mm.trans1:probe12 -0.0171045498382426 0.0992874473530335 -0.172273034449405 0.86325649693215 df.mm.trans2:probe2 -0.072049235009442 0.0992874473530334 -0.725663081590352 0.468209069810513 df.mm.trans2:probe3 -0.0284020895048504 0.0992874473530335 -0.286059217575228 0.774889900112377 df.mm.trans2:probe4 -0.0471164010073555 0.0992874473530335 -0.474545395852762 0.635210946657769 df.mm.trans2:probe5 0.0382563870066773 0.0992874473530335 0.385309402412676 0.70008721502205 df.mm.trans2:probe6 0.0347586998889262 0.0992874473530334 0.350081513983693 0.726348613802576 df.mm.trans3:probe2 -0.141668957532539 0.0992874473530335 -1.42685668036979 0.153922219265055 df.mm.trans3:probe3 -0.176171567853331 0.0992874473530335 -1.77435891998434 0.0762971499430406 . df.mm.trans3:probe4 0.0131467473602640 0.0992874473530334 0.132410971484830 0.894684924099165 df.mm.trans3:probe5 -0.152807624765219 0.0992874473530335 -1.53904273741560 0.124098924953105 df.mm.trans3:probe6 0.0164993710820989 0.0992874473530335 0.166177815242168 0.86804941083915 df.mm.trans3:probe7 -0.09813360831259 0.0992874473530335 -0.988378802444776 0.323197719906177 df.mm.trans3:probe8 -0.129927336207172 0.0992874473530335 -1.30859781040793 0.190960562351951 df.mm.trans3:probe9 -0.0847814219955177 0.0992874473530335 -0.853898697728253 0.39335832453568 df.mm.trans3:probe10 0.0796647698947153 0.0992874473530334 0.802364971791989 0.422525706598318 df.mm.trans3:probe11 -0.0124296077353823 0.0992874473530335 -0.125188108534875 0.900398895161648 df.mm.trans3:probe12 -0.109421232822471 0.0992874473530334 -1.10206512242585 0.270689070732661 df.mm.trans3:probe13 0.146506652298149 0.0992874473530335 1.47558081312353 0.140360013205917 df.mm.trans3:probe14 0.0978675028592899 0.0992874473530334 0.985698650417563 0.324510933834710 df.mm.trans3:probe15 -0.0405187998694293 0.0992874473530334 -0.408095896809168 0.683287516477464 df.mm.trans3:probe16 -0.207304903349608 0.0992874473530335 -2.08792660982108 0.0370480066121998 * df.mm.trans3:probe17 0.0572424139272824 0.0992874473530334 0.576532234973745 0.564380602897922 df.mm.trans3:probe18 -0.159460999019218 0.0992874473530334 -1.60605397026904 0.108566496274744 df.mm.trans3:probe19 0.03555390425132 0.0992874473530334 0.358090626752665 0.720348370092196 df.mm.trans3:probe20 -0.10810936282359 0.0992874473530335 -1.08885227393538 0.276472012544042 df.mm.trans3:probe21 -0.00663520661924215 0.0992874473530335 -0.0668282526757843 0.946731313030598 df.mm.trans3:probe22 -0.213408321926032 0.0992874473530334 -2.14939881742777 0.0318336431097444 * df.mm.trans3:probe23 -0.0408884301222039 0.0992874473530334 -0.411818726458121 0.680557399637864 df.mm.trans3:probe24 -0.00542055879926888 0.0992874473530335 -0.0545946032834861 0.956471954140378 df.mm.trans3:probe25 -0.100717798730914 0.0992874473530335 -1.01440616529091 0.310625600110144 df.mm.trans3:probe26 -0.0872937776518828 0.0992874473530334 -0.879202557615314 0.379495138214369 df.mm.trans3:probe27 -0.127043107131192 0.0992874473530334 -1.27954852821896 0.200990165149479 df.mm.trans3:probe28 -0.00758153055739374 0.0992874473530335 -0.0763594065464923 0.939147893048812