fitVsDatCorrelation=0.836642482907784 cont.fitVsDatCorrelation=0.232976620224328 fstatistic=10137.1943577875,53,715 cont.fstatistic=3206.80440281396,53,715 residuals=-0.600728967356589,-0.0793443103144418,-0.00742473025450797,0.0740292562533159,1.11247944845350 cont.residuals=-0.493373713792588,-0.166167108005424,-0.0577946860040607,0.0911722169873479,1.50952750959859 predictedValues: Include Exclude Both Lung 57.2274129871098 52.0743306440097 65.5412701790464 cerebhem 62.0728976173044 65.8828195079488 84.6414223202855 cortex 55.3741461367661 57.0743507396145 73.318006310782 heart 58.1045334118077 53.8792173282253 76.0309192456505 kidney 57.8994908582638 51.4189612010182 67.3172595201585 liver 59.4830443693133 51.8678962299182 58.7011288222698 stomach 60.4846304878255 56.6394769330152 73.7708412395477 testicle 60.7373777169074 53.8769255761436 62.6261254809782 diffExp=5.1530823431001,-3.80992189064442,-1.70020460284834,4.22531608358244,6.4805296572456,7.6151481393951,3.84515355481025,6.86045214076378 diffExpScore=1.33772845070694 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 59.1963529960431 53.7634364566603 60.6501579680839 cerebhem 56.0639918193638 53.8982953608247 57.9032113819656 cortex 56.3944462148076 53.9345088440821 59.976849116345 heart 55.4035818431478 55.2369636760817 56.4652873588403 kidney 56.0506499245256 59.6474341322882 57.4550927034717 liver 55.0905654745152 55.6797363781543 54.5210765867884 stomach 52.3435516579992 50.5997929402975 50.4066613756302 testicle 53.4424148421959 55.1907861413065 49.8332398713542 cont.diffExp=5.43291653938282,2.16569645853908,2.45993737072546,0.166618167066154,-3.59678420776257,-0.589170903639079,1.74375871770164,-1.74837129911067 cont.diffExpScore=2.54502765170961 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=0.486436463257934 cont.tran.correlation=0.264210924467566 tran.covariance=0.00139563870689690 cont.tran.covariance=0.000491792115172946 tran.mean=57.1310944840745 cont.tran.mean=55.1210317938933 weightedLogRatios: wLogRatio Lung 0.377429682498121 cerebhem -0.247690314920149 cortex -0.121852121969206 heart 0.303844741838938 kidney 0.474730135975444 liver 0.550320753543865 stomach 0.267300417736800 testicle 0.485017014557604 cont.weightedLogRatios: wLogRatio Lung 0.388215761586962 cerebhem 0.157847331328793 cortex 0.178849834108344 heart 0.0120871105730806 kidney -0.252348668690078 liver -0.0427033144809334 stomach 0.133522286632628 testicle -0.128594619947292 varWeightedLogRatios=0.0857633375507022 cont.varWeightedLogRatios=0.040380940164885 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.43479136411135 0.0774328047636174 57.2727718910562 2.41468381321210e-269 *** df.mm.trans1 0.144004609102273 0.068761829209307 2.09425215643887 0.036589014327465 * df.mm.trans2 -0.432152655596542 0.0625478254846467 -6.90915555014171 1.07965439301081e-11 *** df.mm.exp2 0.0607382504089986 0.0843237635413799 0.720298144415634 0.471576851742705 df.mm.exp3 -0.0533637472946305 0.08432376354138 -0.63284351947175 0.527038172338763 df.mm.exp4 -0.0991767176300703 0.08432376354138 -1.17614197309163 0.239929591179989 df.mm.exp5 -0.0277262114545824 0.0843237635413799 -0.328806617377513 0.742398141777531 df.mm.exp6 0.144907233685232 0.08432376354138 1.71846259701302 0.0861451660490324 . df.mm.exp7 0.0211068155775442 0.08432376354138 0.250306849351980 0.80242196412936 df.mm.exp8 0.139053903079210 0.0843237635413799 1.64904763781057 0.0995771330643609 . df.mm.trans1:exp2 0.0205381803737499 0.0800693695530545 0.256504834350434 0.79763483437881 df.mm.trans2:exp2 0.174467318243107 0.0674244430568997 2.58759746959829 0.00986092805916576 ** df.mm.trans1:exp3 0.0204435242794772 0.0800693695530545 0.255322658259862 0.798547327810015 df.mm.trans2:exp3 0.145046430707735 0.0674244430568997 2.15124403156478 0.0317913107977722 * df.mm.trans1:exp4 0.114387374576397 0.0800693695530545 1.42860341245229 0.153554940589646 df.mm.trans2:exp4 0.133249409491702 0.0674244430568997 1.97627749597060 0.0485073010732681 * df.mm.trans1:exp5 0.0394017709184324 0.0800693695530545 0.492095430979065 0.622802932819124 df.mm.trans2:exp5 0.0150610774793051 0.0674244430568997 0.223377113646975 0.823305818665893 df.mm.trans1:exp6 -0.106248961937370 0.0800693695530545 -1.32696139023511 0.184945054685175 df.mm.trans2:exp6 -0.148879338030505 0.0674244430568997 -2.20809147663059 0.0275545404428246 * df.mm.trans1:exp7 0.0342494440522017 0.0800693695530545 0.427747142801065 0.668964023059822 df.mm.trans2:exp7 0.0629272654527535 0.0674244430568997 0.933300485695507 0.350979902250302 df.mm.trans1:exp8 -0.0795276482633795 0.0800693695530545 -0.993234350505082 0.320931768234426 df.mm.trans2:exp8 -0.105023747114285 0.0674244430568997 -1.55765094011463 0.119758510243843 df.mm.trans1:probe2 -0.775652502384981 0.0438557998694253 -17.6864292680645 2.43882704435518e-58 *** df.mm.trans1:probe3 -0.665818134892566 0.0438557998694253 -15.1819858918307 2.48821483183251e-45 *** df.mm.trans1:probe4 -0.135512891504082 0.0438557998694253 -3.08996511083035 0.00207942207561959 ** df.mm.trans1:probe5 -0.291910831464249 0.0438557998694253 -6.6561511210233 5.59849149212583e-11 *** df.mm.trans1:probe6 -0.751030934679286 0.0438557998694253 -17.1250082523949 2.38586227255532e-55 *** df.mm.trans1:probe7 -0.797170512528474 0.0438557998694253 -18.1770829605649 5.53895575196153e-61 *** df.mm.trans1:probe8 -0.626108841600798 0.0438557998694253 -14.2765345396721 7.2302030911519e-41 *** df.mm.trans1:probe9 -0.701664879825906 0.0438557998694253 -15.9993634117954 1.77522848561140e-49 *** df.mm.trans1:probe10 -0.758302478888281 0.0438557998694253 -17.2908140119670 3.15127467121162e-56 *** df.mm.trans1:probe11 -0.73094271719195 0.0438557998694253 -16.6669566937151 6.13695254016355e-53 *** df.mm.trans1:probe12 -0.605497063372409 0.0438557998694253 -13.8065447483615 1.30881176590132e-38 *** df.mm.trans1:probe13 -0.780676128419212 0.0438557998694253 -17.8009779947822 5.92108310770934e-59 *** df.mm.trans1:probe14 -0.669989372968237 0.0438557998694253 -15.2770984673188 8.29590954987757e-46 *** df.mm.trans1:probe15 -0.715879291951978 0.0438557998694253 -16.3234804537464 3.77339101356749e-51 *** df.mm.trans1:probe16 -0.678132621032981 0.0438557998694253 -15.4627808192310 9.6235512539433e-47 *** df.mm.trans1:probe17 -0.750073954057922 0.0438557998694253 -17.1031871791454 3.11235907741624e-55 *** df.mm.trans1:probe18 -0.787535253335677 0.0438557998694253 -17.9573797691629 8.52264357486594e-60 *** df.mm.trans1:probe19 -0.633788057721985 0.0438557998694253 -14.4516360346637 1.01716747568041e-41 *** df.mm.trans1:probe20 -0.722800804973707 0.0438557998694253 -16.4813048017765 5.71277204310528e-52 *** df.mm.trans1:probe21 -0.687393385684765 0.0438557998694253 -15.67394478567 8.17920435280934e-48 *** df.mm.trans1:probe22 -0.559955823227927 0.0438557998694253 -12.7681133372352 8.87961547216721e-34 *** df.mm.trans2:probe2 -0.0564640386711142 0.0438557998694253 -1.28749307592675 0.198339065465206 df.mm.trans2:probe3 -0.0792528958303148 0.0438557998694253 -1.80712462356814 0.0711630697850376 . df.mm.trans2:probe4 -0.125788464666383 0.0438557998694253 -2.86822871868491 0.00424937984458027 ** df.mm.trans2:probe5 -0.108063870138928 0.0438557998694253 -2.46407249350538 0.0139710717222752 * df.mm.trans2:probe6 -0.130096482113098 0.0438557998694253 -2.96646013755176 0.00311293297075562 ** df.mm.trans3:probe2 0.47083264831923 0.0438557998694253 10.7359265985587 4.95523691111717e-25 *** df.mm.trans3:probe3 -0.260343402846608 0.0438557998694253 -5.9363505767024 4.54434728662489e-09 *** df.mm.trans3:probe4 -0.0663424227643759 0.0438557998694253 -1.51274000159389 0.130787577744469 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.92062289332539 0.137475351106523 28.5187334439866 4.66731528275480e-120 *** df.mm.trans1 0.195736367609789 0.122080772382377 1.60333493792708 0.109302351799686 df.mm.trans2 0.0431327931819669 0.111048337919583 0.388414576841321 0.69782488267104 df.mm.exp2 -0.00551155805038241 0.149709661620335 -0.0368149790115737 0.970642806438048 df.mm.exp3 -0.0341487576522967 0.149709661620335 -0.228099892035681 0.819633799395416 df.mm.exp4 0.0323192289711734 0.149709661620335 0.215879380270963 0.829143340264552 df.mm.exp5 0.103371919340572 0.149709661620335 0.690482619636963 0.490114812442876 df.mm.exp6 0.0696760925636345 0.149709661620335 0.465408122692401 0.641780938425893 df.mm.exp7 0.00132158835213168 0.149709661620335 0.00882767576806926 0.992959087691324 df.mm.exp8 0.120387197493047 0.149709661620335 0.804137797053806 0.421584672972776 df.mm.trans1:exp2 -0.0488546277474674 0.142156347374829 -0.343668282490761 0.731196800603672 df.mm.trans2:exp2 0.00801679314963856 0.119706357153219 0.0669704879530954 0.946623919575481 df.mm.trans1:exp3 -0.0143404952567107 0.142156347374829 -0.100878332354014 0.919675323847135 df.mm.trans2:exp3 0.0373256526399474 0.119706357153219 0.311810112074268 0.75527573902206 df.mm.trans1:exp4 -0.098534918295221 0.142156347374829 -0.693144696771157 0.488443836338733 df.mm.trans2:exp4 -0.00528048433525226 0.119706357153219 -0.0441119791866483 0.964827460253448 df.mm.trans1:exp5 -0.157976109643339 0.142156347374829 -1.11128424836914 0.266819559871757 df.mm.trans2:exp5 0.000485596536363284 0.119706357153219 0.00405656431213376 0.996764470354246 df.mm.trans1:exp6 -0.141557551769155 0.142156347374829 -0.995787767365074 0.319690149656195 df.mm.trans2:exp6 -0.0346534274932839 0.119706357153219 -0.289486943863216 0.77229269145157 df.mm.trans1:exp7 -0.124352771315921 0.142156347374829 -0.87476059713349 0.38199791795824 df.mm.trans2:exp7 -0.0619677205129253 0.119706357153219 -0.517664408028134 0.604852555902109 df.mm.trans1:exp8 -0.222642416565995 0.142156347374829 -1.5661799186423 0.117748949423393 df.mm.trans2:exp8 -0.0941847922291252 0.119706357153219 -0.786798583374925 0.43166048637126 df.mm.trans1:probe2 -0.0724635659402007 0.077862238149734 -0.9306637936717 0.352341617691033 df.mm.trans1:probe3 -0.0884200379781193 0.077862238149734 -1.13559589448331 0.256506235130474 df.mm.trans1:probe4 0.0165486961077222 0.077862238149734 0.212538150725876 0.831747795084647 df.mm.trans1:probe5 0.068231786264348 0.077862238149734 0.876314217080866 0.381153549823393 df.mm.trans1:probe6 -0.0715635990768983 0.077862238149734 -0.919105342685848 0.358350430203104 df.mm.trans1:probe7 -0.133879959287652 0.077862238149734 -1.71944658243952 0.0859658679012874 . df.mm.trans1:probe8 -0.00455657504542379 0.077862238149734 -0.0585209872423807 0.953349996973685 df.mm.trans1:probe9 -0.103826187793144 0.077862238149734 -1.33346009902104 0.182805515131369 df.mm.trans1:probe10 -0.0520959167750981 0.077862238149734 -0.66907807960663 0.503661644573805 df.mm.trans1:probe11 -0.0311680058072676 0.077862238149734 -0.400296813293879 0.689057484540949 df.mm.trans1:probe12 0.0296435345664802 0.077862238149734 0.380717730069277 0.703525795464162 df.mm.trans1:probe13 -0.092956987232341 0.077862238149734 -1.19386482383898 0.23292679058976 df.mm.trans1:probe14 -0.0147679807409831 0.077862238149734 -0.189668073920291 0.849623070344606 df.mm.trans1:probe15 -0.0183911647722837 0.077862238149734 -0.236201337250496 0.813344087605699 df.mm.trans1:probe16 -0.0201836688681126 0.077862238149734 -0.259222819016557 0.795537939637185 df.mm.trans1:probe17 -0.098630101545499 0.077862238149734 -1.26672574394570 0.205665850316800 df.mm.trans1:probe18 -0.0280521847276385 0.077862238149734 -0.360279711889252 0.71874437058154 df.mm.trans1:probe19 -0.00293579882367213 0.077862238149734 -0.0377050402536131 0.969933381914308 df.mm.trans1:probe20 -0.0577472088009155 0.077862238149734 -0.741658731795816 0.458537664736363 df.mm.trans1:probe21 -0.0854988364602177 0.077862238149734 -1.09807833029148 0.272539925762324 df.mm.trans1:probe22 -0.0602687064886146 0.077862238149734 -0.774042821280247 0.439161327149597 df.mm.trans2:probe2 0.0327760390297806 0.077862238149734 0.420949099443433 0.673918749764278 df.mm.trans2:probe3 0.112500234021537 0.077862238149734 1.44486257645448 0.148934582886650 df.mm.trans2:probe4 -0.0161967372552693 0.077862238149734 -0.208017874134596 0.83527424864773 df.mm.trans2:probe5 0.0149529576485039 0.077862238149734 0.192043768633370 0.847762448650213 df.mm.trans2:probe6 0.064346805050467 0.077862238149734 0.826418641173968 0.408842338139183 df.mm.trans3:probe2 -0.0583473260645842 0.077862238149734 -0.749366155547425 0.453883013773805 df.mm.trans3:probe3 -0.0798371089255906 0.077862238149734 -1.02536365281536 0.305538400097635 df.mm.trans3:probe4 -0.0792948215915065 0.077862238149734 -1.01839895019480 0.308832867448407