fitVsDatCorrelation=0.702857510307575 cont.fitVsDatCorrelation=0.253953320863567 fstatistic=10677.1606000117,52,692 cont.fstatistic=5768.88319960612,52,692 residuals=-0.569367531971513,-0.0858858338770832,-0.0102279502044936,0.0735560444287858,1.07909065812354 cont.residuals=-0.498136717453926,-0.124239985592236,-0.0159357895514998,0.0917433899752194,1.35511120715352 predictedValues: Include Exclude Both Lung 51.0009629030186 54.3789909006814 55.7562597351858 cerebhem 57.5303926452527 71.1209516605342 52.7246358053271 cortex 51.1349536349684 49.9485010204892 51.5056109642342 heart 49.7132379447784 51.7070957366028 55.4745343009715 kidney 49.8178404596661 50.2858195366076 57.6052587185666 liver 49.0255454952321 51.7851655580625 52.6917022746026 stomach 53.5638790496244 55.2978696627958 50.5162468911057 testicle 51.7246416754401 53.2399369829405 54.2194972794644 diffExp=-3.37802799766278,-13.5905590152815,1.1864526144792,-1.99385779182442,-0.467979076941482,-2.75962006283043,-1.73399061317144,-1.51529530750037 diffExpScore=1.05436628924803 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,-1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 54.2800083258153 51.6168189025364 58.5111423234254 cerebhem 53.1971534994454 53.3842803296276 54.9301765297475 cortex 56.3524839596752 53.1844889703285 55.0007822359773 heart 51.9070422096711 53.5722679129105 53.9926776398621 kidney 53.0983776833461 49.957992207657 55.337066267259 liver 54.6913816184103 50.761185659215 58.1202079565417 stomach 52.6290981414546 52.9986558590956 51.1236081818758 testicle 54.1956236381612 51.8697290834892 55.0549408675105 cont.diffExp=2.66318942327892,-0.187126830182187,3.16799498934670,-1.66522570323936,3.14038547568909,3.93019595919539,-0.369557717640959,2.32589455467199 cont.diffExpScore=1.24588618709936 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=0.919113831408792 cont.tran.correlation=-0.143199390836544 tran.covariance=0.00538175655237189 cont.tran.covariance=-9.75035243967622e-05 tran.mean=53.2047365541684 cont.tran.mean=52.9810367500524 weightedLogRatios: wLogRatio Lung -0.254218981880612 cerebhem -0.881854954017537 cortex 0.092089204679741 heart -0.154382474906120 kidney -0.0365868340428040 liver -0.214652751965169 stomach -0.127335966092474 testicle -0.114353802941652 cont.weightedLogRatios: wLogRatio Lung 0.199674213381516 cerebhem -0.0139606349409450 cortex 0.231594130716157 heart -0.125210777742161 kidney 0.240299154629315 liver 0.295643138997544 stomach -0.0277570165394257 testicle 0.174172528001503 varWeightedLogRatios=0.0848221363547192 cont.varWeightedLogRatios=0.0238482982435965 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.61717068343291 0.0777405553829931 46.5287476480291 3.00263365043226e-215 *** df.mm.trans1 0.0739268179730846 0.0698223874366 1.05878387559021 0.290067652909688 df.mm.trans2 0.372580169951355 0.0642111679977463 5.80242007067108 9.94983883294701e-09 *** df.mm.exp2 0.444779819865951 0.0879533982152364 5.0569941456668 5.46053091658311e-07 *** df.mm.exp3 -0.00306267921794155 0.0879533982152364 -0.0348216132644093 0.972232027887224 df.mm.exp4 -0.070890508272994 0.0879533982152364 -0.806000788048158 0.420519278823433 df.mm.exp5 -0.134350288399551 0.0879533982152364 -1.52751674325048 0.127089496089504 df.mm.exp6 -0.0318454723982269 0.0879533982152364 -0.362072109144615 0.717408683962887 df.mm.exp7 0.164481612625511 0.0879533982152364 1.87009957503856 0.0618919726363736 . df.mm.exp8 0.0208698245326238 0.0879533982152364 0.237282753777766 0.8125076702778 df.mm.trans1:exp2 -0.324310956959284 0.0842089676705553 -3.85126389659665 0.000128413307740243 *** df.mm.trans2:exp2 -0.176375730286301 0.073294498512697 -2.40639794070966 0.0163718152257199 * df.mm.trans1:exp3 0.00568645375993753 0.0842089676705553 0.0675278882670102 0.946180966875002 df.mm.trans2:exp3 -0.0819227083277132 0.073294498512697 -1.11771974691281 0.264074593721541 df.mm.trans1:exp4 0.0453172499491958 0.0842089676705553 0.538152303760417 0.59064494627574 df.mm.trans2:exp4 0.0205076460697790 0.073294498512697 0.279797890509154 0.779716182401453 df.mm.trans1:exp5 0.110878937441287 0.0842089676705553 1.31671175301745 0.188371157855272 df.mm.trans2:exp5 0.0560955254075776 0.073294498512697 0.765344282939053 0.444327503456853 df.mm.trans1:exp6 -0.00765754169992679 0.0842089676705553 -0.0909349907943871 0.927570561937739 df.mm.trans2:exp6 -0.0170286811287719 0.073294498512697 -0.232332323357420 0.816348601752986 df.mm.trans1:exp7 -0.115451183045640 0.0842089676705553 -1.37100817453684 0.170816870851594 df.mm.trans2:exp7 -0.147725110706114 0.073294498512697 -2.01550066790515 0.0442383296770948 * df.mm.trans1:exp8 -0.00678004141514947 0.0842089676705553 -0.0805144820403753 0.935851357802754 df.mm.trans2:exp8 -0.0420388972377545 0.073294498512697 -0.573561428085519 0.566450969928848 df.mm.trans1:probe2 0.204624179945086 0.0421044838352777 4.85991422542128 1.45397791931411e-06 *** df.mm.trans1:probe3 0.384966725250723 0.0421044838352777 9.1431289540753 6.65375963189756e-19 *** df.mm.trans1:probe4 0.201462006223549 0.0421044838352777 4.78481120945964 2.09311544928833e-06 *** df.mm.trans1:probe5 0.179707764940759 0.0421044838352777 4.26813841594203 2.24603876582283e-05 *** df.mm.trans1:probe6 0.198540270625592 0.0421044838352777 4.71541870462839 2.91812536439277e-06 *** df.mm.trans1:probe7 0.263506880783038 0.0421044838352777 6.25840425485173 6.81849564529487e-10 *** df.mm.trans1:probe8 0.180357006063309 0.0421044838352777 4.28355817800562 2.09957438429587e-05 *** df.mm.trans1:probe9 0.357574940041662 0.0421044838352776 8.49256201407377 1.23185531990763e-16 *** df.mm.trans1:probe10 0.0811696698160925 0.0421044838352776 1.92781533989698 0.0542870128740557 . df.mm.trans1:probe11 0.288132544504509 0.0421044838352777 6.8432745935504 1.70670607561381e-11 *** df.mm.trans1:probe12 0.311847026575528 0.0421044838352777 7.4065039675001 3.78320626242951e-13 *** df.mm.trans1:probe13 0.220651915843171 0.0421044838352777 5.24057999871015 2.12788300305530e-07 *** df.mm.trans1:probe14 0.112542477700041 0.0421044838352776 2.67293331846397 0.00769630828979354 ** df.mm.trans1:probe15 0.10464095224398 0.0421044838352776 2.48526861541301 0.0131805038221006 * df.mm.trans1:probe16 0.409267932377405 0.0421044838352776 9.7202933060183 5.07473991187968e-21 *** df.mm.trans1:probe17 0.303212074906542 0.0421044838352777 7.20142006948183 1.55859974475877e-12 *** df.mm.trans1:probe18 0.424818546021984 0.0421044838352777 10.0896272160459 1.99579840189302e-22 *** df.mm.trans1:probe19 0.500573805260368 0.0421044838352777 11.8888479245756 8.61708396809546e-30 *** df.mm.trans1:probe20 0.35913142347091 0.0421044838352776 8.52952917974044 9.23011784606798e-17 *** df.mm.trans1:probe21 0.342537826377651 0.0421044838352777 8.13542395431653 1.90279963104454e-15 *** df.mm.trans1:probe22 0.589409320814059 0.0421044838352777 13.9987304706065 2.18521581500879e-39 *** df.mm.trans2:probe2 0.144088141552997 0.0421044838352777 3.42215670228146 0.000657822924241686 *** df.mm.trans2:probe3 0.0541066314277683 0.0421044838352777 1.28505628140333 0.199202559435686 df.mm.trans2:probe4 -0.100516985460311 0.0421044838352777 -2.38732259142651 0.0172390634120253 * df.mm.trans2:probe5 -0.0575607384903691 0.0421044838352777 -1.36709284254760 0.172040122796309 df.mm.trans2:probe6 0.0159262138224174 0.0421044838352777 0.378254579363194 0.705357488512165 df.mm.trans3:probe2 -0.0246255441080925 0.0421044838352777 -0.584867497828337 0.558827457805464 df.mm.trans3:probe3 -0.103438411116374 0.0421044838352777 -2.45670773500153 0.0142661363234984 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.91761879320649 0.105706037016981 37.0614479906873 1.75507539341749e-166 *** df.mm.trans1 0.0774937045991764 0.0949394795885628 0.816243199720593 0.414641936448203 df.mm.trans2 0.0202833005022292 0.0873097454454024 0.232314278306000 0.816362610667234 df.mm.exp2 0.0766720026882786 0.119592728939301 0.641109232712577 0.521663979668418 df.mm.exp3 0.129259353668648 0.119592728939301 1.08082953550005 0.280149473724693 df.mm.exp4 0.0728512231202448 0.119592728939301 0.60916097296534 0.54261767746693 df.mm.exp5 0.00109955977038824 0.119592728939301 0.00919420252502404 0.992666841001192 df.mm.exp6 -0.00246162855864555 0.119592728939301 -0.0205834299499507 0.983583892071505 df.mm.exp7 0.130502973021418 0.119592728939301 1.09122832281597 0.275552222826191 df.mm.exp8 0.0642175620722337 0.119592728939301 0.536968782649212 0.591461777375242 df.mm.trans1:exp2 -0.0968231017747647 0.114501320577043 -0.845606856644215 0.398064386548691 df.mm.trans2:exp2 -0.0430032430105469 0.0996606074494176 -0.43149689843475 0.666241502440875 df.mm.trans1:exp3 -0.0917890215904457 0.114501320577043 -0.80164159791227 0.423035487376455 df.mm.trans2:exp3 -0.0993401276429188 0.0996606074494177 -0.996784288048199 0.319217643097071 df.mm.trans1:exp4 -0.117552742458663 0.114501320577043 -1.02664966540335 0.304944185463669 df.mm.trans2:exp4 -0.0356672457218927 0.0996606074494177 -0.357887099373697 0.720536939662492 df.mm.trans1:exp5 -0.0231091724450145 0.114501320577043 -0.201824505853322 0.840113237405606 df.mm.trans2:exp5 -0.0337646304194994 0.0996606074494176 -0.338796153100276 0.734866075508513 df.mm.trans1:exp6 0.0100117799697802 0.114501320577043 0.0874381179127425 0.930348567179827 df.mm.trans2:exp6 -0.0142539378290735 0.0996606074494177 -0.143024793786332 0.886312234823309 df.mm.trans1:exp7 -0.161389797982072 0.114501320577043 -1.40950162992644 0.159136129151017 df.mm.trans2:exp7 -0.104083988041708 0.0996606074494176 -1.04438444341748 0.296672246524999 df.mm.trans1:exp8 -0.0657733899231176 0.114501320577043 -0.574433461480138 0.565861195733639 df.mm.trans2:exp8 -0.059329763814612 0.0996606074494176 -0.595318103441468 0.551825485671812 df.mm.trans1:probe2 -0.0119733545872761 0.0572506602885214 -0.209139152752736 0.834401160169508 df.mm.trans1:probe3 -0.0416136151753376 0.0572506602885214 -0.72686699097654 0.467553249404113 df.mm.trans1:probe4 -0.0497877831726894 0.0572506602885214 -0.86964557127862 0.384795665233056 df.mm.trans1:probe5 0.0423282697149666 0.0572506602885214 0.73934989573305 0.45994527776352 df.mm.trans1:probe6 -0.0397482672972816 0.0572506602885214 -0.694284871073374 0.487736589165287 df.mm.trans1:probe7 -0.0105900205929166 0.0572506602885214 -0.184976392229311 0.853301751280312 df.mm.trans1:probe8 0.0889838155971795 0.0572506602885214 1.55428452962350 0.120573763885904 df.mm.trans1:probe9 -0.0254383354884794 0.0572506602885214 -0.44433261311363 0.656941046773077 df.mm.trans1:probe10 -0.0124894468986431 0.0572506602885214 -0.218153761645735 0.827373625330289 df.mm.trans1:probe11 0.0240616328028988 0.0572506602885214 0.420285681975323 0.674407239412309 df.mm.trans1:probe12 0.031074040786075 0.0572506602885214 0.542771744980298 0.587461734426676 df.mm.trans1:probe13 -0.0283422049947655 0.0572506602885214 -0.495054639578507 0.620718699868974 df.mm.trans1:probe14 0.0231739770022453 0.0572506602885214 0.404780955982994 0.68576361807891 df.mm.trans1:probe15 -0.0600194768838334 0.0572506602885214 -1.04836305086017 0.294837378696165 df.mm.trans1:probe16 -0.00101440566439594 0.0572506602885214 -0.0177186718770356 0.985868392321439 df.mm.trans1:probe17 -0.0270107429530351 0.0572506602885214 -0.471797928913157 0.637219753568744 df.mm.trans1:probe18 0.0411285275560434 0.0572506602885214 0.718393942511255 0.472756898929252 df.mm.trans1:probe19 0.0494676069228147 0.0572506602885214 0.864053037528596 0.387858150740395 df.mm.trans1:probe20 0.00519956795563536 0.0572506602885214 0.0908211002184346 0.927661025772132 df.mm.trans1:probe21 -0.0687589831755114 0.0572506602885214 -1.20101642197649 0.230155750006160 df.mm.trans1:probe22 0.0474564622317733 0.0572506602885214 0.828924277774455 0.40743307649292 df.mm.trans2:probe2 0.0101949759860171 0.0572506602885214 0.178076129334374 0.858715304466826 df.mm.trans2:probe3 0.0433714567550765 0.0572506602885214 0.757571293265457 0.44896555718082 df.mm.trans2:probe4 -0.0402054494339742 0.0572506602885214 -0.702270493149845 0.482746636070162 df.mm.trans2:probe5 0.028612923322375 0.0572506602885214 0.499783289453376 0.617386552497322 df.mm.trans2:probe6 0.0115353539070689 0.0572506602885214 0.201488574086922 0.840375774916453 df.mm.trans3:probe2 0.14232035159476 0.0572506602885214 2.485916334895 0.0131567561587908 * df.mm.trans3:probe3 0.0191438629866388 0.0572506602885214 0.334386763229648 0.738189045185858