fitVsDatCorrelation=0.76836214592152 cont.fitVsDatCorrelation=0.271379114897796 fstatistic=14264.459952758,54,738 cont.fstatistic=6299.90744844491,54,738 residuals=-0.415543326651576,-0.0793688406368487,-0.00663115581363808,0.0714881390704322,0.521143603477031 cont.residuals=-0.430594339606981,-0.129352304731192,-0.0253376247920152,0.0930913221937816,0.941336006071202 predictedValues: Include Exclude Both Lung 49.0919754702222 52.1257225094237 63.1556515557838 cerebhem 55.9114240374657 54.9972093749174 61.2003649145018 cortex 55.5118668182662 49.2871558526485 82.6812264837917 heart 51.0528359627272 52.07098084783 71.9559868576086 kidney 49.3834387442877 52.9110040604919 62.9858002153955 liver 51.337741865026 56.0894830776724 61.7240688742989 stomach 50.3047839999371 55.0078861496111 61.6727293784823 testicle 49.1220898419607 48.9727167123536 61.6346075155556 diffExp=-3.03374703920155,0.914214662548318,6.22471096561767,-1.01814488510279,-3.52756531620422,-4.75174121264639,-4.70310214967394,0.149373129607049 diffExpScore=2.263409192675 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 54.3178211508574 50.543956657611 54.48892683971 cerebhem 54.3017924419386 51.9157493661862 57.3420913777954 cortex 52.6661461519405 50.5914719530449 53.0103678084835 heart 53.2380943000484 50.9805627772636 60.2441428734338 kidney 52.0462776135241 53.2329220769486 49.6347644738599 liver 51.7918226587788 49.5136319060925 51.8505999707678 stomach 51.5880615163488 51.4404503444984 50.388112115044 testicle 53.7833500546551 55.7750329405753 53.9118857794612 cont.diffExp=3.77386449324639,2.38604307575247,2.07467419889559,2.25753152278478,-1.18664446342447,2.2781907526863,0.147611171850436,-1.99168288592018 cont.diffExpScore=1.49877656066411 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=0.0632894037171409 cont.tran.correlation=0.244164052192933 tran.covariance=0.00016980022568139 cont.tran.covariance=0.000190980765930216 tran.mean=52.0736447078026 cont.tran.mean=52.3579464943945 weightedLogRatios: wLogRatio Lung -0.235275259711268 cerebhem 0.0662010774626172 cortex 0.470634540060055 heart -0.0778560274077186 kidney -0.271438916531174 liver -0.352555987124039 stomach -0.354179669636627 testicle 0.0118554306849198 cont.weightedLogRatios: wLogRatio Lung 0.285072615714884 cerebhem 0.178485995443326 cortex 0.158503813651793 heart 0.171287279227125 kidney -0.0893500708056532 liver 0.176551489547633 stomach 0.0112951764692079 testicle -0.145563925394214 varWeightedLogRatios=0.0774170665661385 cont.varWeightedLogRatios=0.0226342237255080 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.85821724282857 0.0632341994498554 61.0147242535763 1.53650884443448e-290 *** df.mm.trans1 0.0361409442587155 0.0557036993427903 0.648806895863608 0.516665038219683 df.mm.trans2 0.101477412656942 0.0502631489451661 2.01892270553219 0.0438563904804411 * df.mm.exp2 0.215146226805397 0.0669355532638763 3.21422945377381 0.00136485838755696 ** df.mm.exp3 -0.202484028868792 0.0669355532638763 -3.02505946384801 0.00257213121843339 ** df.mm.exp4 -0.092337529876028 0.0669355532638763 -1.37949901619564 0.168158793646858 df.mm.exp5 0.0235653545773270 0.0669355532638763 0.352060354000908 0.724893505888679 df.mm.exp6 0.140948776792303 0.0669355532638763 2.10573857866907 0.0355643089151611 * df.mm.exp7 0.101983104359641 0.0669355532638763 1.52360142535312 0.128036682339394 df.mm.exp8 -0.0374031445090506 0.0669355532638763 -0.558793386850756 0.576472244577537 df.mm.trans1:exp2 -0.0850730909821154 0.0631364397426721 -1.34744834090822 0.178249406990593 df.mm.trans2:exp2 -0.161522322613003 0.0516570870621862 -3.12681825087346 0.00183644854864123 ** df.mm.trans1:exp3 0.325385254245437 0.0631364397426721 5.15368391964488 3.28230020496416e-07 *** df.mm.trans2:exp3 0.146489004486299 0.0516570870621862 2.83579684448624 0.00469628688461978 ** df.mm.trans1:exp4 0.131503036527048 0.0631364397426721 2.08283896055939 0.0376096593061284 * df.mm.trans2:exp4 0.0912867928590906 0.0516570870621862 1.76716880588325 0.077613202911331 . df.mm.trans1:exp5 -0.0176458237485771 0.063136439742672 -0.279487152276830 0.779949304984821 df.mm.trans2:exp5 -0.00861256219403204 0.0516570870621862 -0.166725665031480 0.867631579503287 df.mm.trans1:exp6 -0.0962181753123634 0.063136439742672 -1.5239721419916 0.127944053857345 df.mm.trans2:exp6 -0.0676589903739964 0.0516570870621862 -1.30977169294402 0.190680648980799 df.mm.trans1:exp7 -0.077578511499449 0.063136439742672 -1.22874384136386 0.219559529777377 df.mm.trans2:exp7 -0.0481650859823304 0.0516570870621862 -0.932400348559103 0.351434594722325 df.mm.trans1:exp8 0.0380163840198391 0.063136439742672 0.602130626541251 0.54727213358596 df.mm.trans2:exp8 -0.0249920553556683 0.0516570870621862 -0.483806903892629 0.628666436782026 df.mm.trans1:probe2 0.0415663062922354 0.0368637387583408 1.12756621255164 0.259869618444337 df.mm.trans1:probe3 0.146151172781510 0.0368637387583408 3.96463239227034 8.06605643744652e-05 *** df.mm.trans1:probe4 -0.0857156927264267 0.0368637387583408 -2.32520345503568 0.0203313555038788 * df.mm.trans1:probe5 0.0750120844844483 0.0368637387583408 2.03484744117215 0.0422231513842447 * df.mm.trans1:probe6 0.103977721491709 0.0368637387583408 2.82059620087187 0.00492212291070294 ** df.mm.trans1:probe7 0.118851811451679 0.0368637387583408 3.22408457348315 0.00131937999601296 ** df.mm.trans1:probe8 0.136792457015345 0.0368637387583409 3.71075918023628 0.000222170541515444 *** df.mm.trans1:probe9 0.044602585905032 0.0368637387583408 1.20993115205766 0.226692717913026 df.mm.trans1:probe10 -0.0276398252089738 0.0368637387583408 -0.749783558042386 0.45362401732249 df.mm.trans1:probe11 0.0615353118687392 0.0368637387583408 1.66926399604045 0.095489174028778 . df.mm.trans1:probe12 -0.0521552793265686 0.0368637387583408 -1.41481252535102 0.157545252393909 df.mm.trans1:probe13 -0.0975956573477118 0.0368637387583408 -2.64747040411439 0.00828271344630075 ** df.mm.trans1:probe14 -0.0614432117867643 0.0368637387583408 -1.66676560371571 0.095985320244311 . df.mm.trans1:probe15 -0.108963085788163 0.0368637387583408 -2.95583382093897 0.00321762596433524 ** df.mm.trans1:probe16 -0.00965216850789728 0.0368637387583408 -0.261833683533073 0.79352272947656 df.mm.trans1:probe17 -0.0768776968240202 0.0368637387583408 -2.08545577343605 0.0373709658002776 * df.mm.trans1:probe18 -0.160753539486579 0.0368637387583408 -4.36074974761496 1.48043353065837e-05 *** df.mm.trans1:probe19 -0.0296375365655368 0.0368637387583408 -0.803975330875276 0.421670110487881 df.mm.trans1:probe20 -0.109041020202412 0.0368637387583408 -2.95794794221029 0.00319590086947455 ** df.mm.trans1:probe21 0.056985009717198 0.0368637387583408 1.54582827560605 0.122574731318912 df.mm.trans1:probe22 0.0161101061770396 0.0368637387583408 0.437017695970799 0.662226355723451 df.mm.trans2:probe2 -0.0777634786529626 0.0368637387583409 -2.10948431364325 0.0352389629964511 * df.mm.trans2:probe3 -0.0781568380483985 0.0368637387583408 -2.12015494577892 0.0343260563486654 * df.mm.trans2:probe4 0.218678429412874 0.0368637387583409 5.93207408631051 4.59666701764348e-09 *** df.mm.trans2:probe5 -0.0142941145551428 0.0368637387583408 -0.387755421359928 0.69830884801249 df.mm.trans2:probe6 -0.114861256232451 0.0368637387583408 -3.11583306797556 0.00190531679550859 ** df.mm.trans3:probe2 0.425643880458876 0.0368637387583408 11.5464110477011 1.81769427515649e-28 *** df.mm.trans3:probe3 0.123311895143611 0.0368637387583408 3.34507294422789 0.000864198745284107 *** df.mm.trans3:probe4 -0.00655282359996941 0.0368637387583409 -0.177757976284670 0.858961830004429 df.mm.trans3:probe5 0.206430744003741 0.0368637387583408 5.59983200176716 3.02804219901706e-08 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.77191846996136 0.0950933172616373 39.6654421002414 3.95864527786581e-185 *** df.mm.trans1 0.155946861522293 0.0837687453994158 1.86163539609822 0.063051832790834 . df.mm.trans2 0.136941197758068 0.075587097026537 1.81170071540108 0.0704388152462574 . df.mm.exp2 -0.0245537504121243 0.100659514281549 -0.243928759118055 0.807353789167509 df.mm.exp3 -0.00242988257156548 0.100659514281549 -0.0241396214645840 0.98074776455998 df.mm.exp4 -0.11188497134327 0.100659514281549 -1.11151908631631 0.266706950923168 df.mm.exp5 0.102420579172556 0.100659514281549 1.01749526513789 0.309251327776163 df.mm.exp6 -0.0185844629628779 0.100659514281549 -0.184626988273522 0.853572338522504 df.mm.exp7 0.0442615840247639 0.100659514281549 0.439715851409361 0.660271653954011 df.mm.exp8 0.0992410488001626 0.100659514281549 0.985908282078348 0.324501235259601 df.mm.trans1:exp2 0.0242586157029338 0.0949463035422977 0.255498263733109 0.798409475050809 df.mm.trans2:exp2 0.0513325640050123 0.0776833392618799 0.660792449098565 0.508951638426506 df.mm.trans1:exp3 -0.0284496273941363 0.0949463035422977 -0.299639125829288 0.764536806393746 df.mm.trans2:exp3 0.00336951964203017 0.0776833392618799 0.0433750618092138 0.965414289475654 df.mm.trans1:exp4 0.0918067986303314 0.0949463035422977 0.96693389005326 0.333893839361702 df.mm.trans2:exp4 0.120486022866326 0.0776833392618799 1.55098923412848 0.121332970662909 df.mm.trans1:exp5 -0.145139673343971 0.0949463035422977 -1.52865006776501 0.126779693520859 df.mm.trans2:exp5 -0.0505869247335235 0.0776833392618799 -0.651194003941938 0.515123969159382 df.mm.trans1:exp6 -0.0290356350483316 0.0949463035422976 -0.305811116020926 0.759834705971704 df.mm.trans2:exp6 -0.00201089987501029 0.0776833392618799 -0.0258858578186411 0.97935537783671 df.mm.trans1:exp7 -0.095823675353429 0.0949463035422976 -1.00924071584041 0.313189980667589 df.mm.trans2:exp7 -0.0266801359220200 0.0776833392618799 -0.343447336012141 0.731359770569474 df.mm.trans1:exp8 -0.109129479153516 0.0949463035422976 -1.14938101939798 0.250771253293993 df.mm.trans2:exp8 -0.000758104425624438 0.0776833392618799 -0.0097589062574766 0.992216280333278 df.mm.trans1:probe2 0.102079455500447 0.0554366977947886 1.84136969843185 0.065968558260608 . df.mm.trans1:probe3 0.168577722989709 0.0554366977947886 3.04090484634812 0.00244215742355100 ** df.mm.trans1:probe4 0.129599901376149 0.0554366977947886 2.33779980647283 0.0196639464948236 * df.mm.trans1:probe5 0.0699350138340384 0.0554366977947886 1.26152921468949 0.207516832141943 df.mm.trans1:probe6 0.100584676239214 0.0554366977947886 1.81440598448975 0.0700211528489092 . df.mm.trans1:probe7 0.106422884263231 0.0554366977947886 1.91971903985297 0.0552786265048452 . df.mm.trans1:probe8 0.0671995700657379 0.0554366977947886 1.21218565929905 0.225829246740445 df.mm.trans1:probe9 0.0744522601223984 0.0554366977947886 1.34301397962051 0.179680321433464 df.mm.trans1:probe10 0.123640461619513 0.0554366977947886 2.23029990128913 0.0260274660380238 * df.mm.trans1:probe11 0.0395584210497448 0.0554366977947886 0.713578236499207 0.475713612864781 df.mm.trans1:probe12 0.0649108091913095 0.0554366977947886 1.17089963459929 0.242017048907262 df.mm.trans1:probe13 0.090632558943671 0.0554366977947886 1.63488379627459 0.102499843382416 df.mm.trans1:probe14 0.109268896383592 0.0554366977947886 1.97105709268751 0.0490905966406158 * df.mm.trans1:probe15 0.0575727987205787 0.0554366977947886 1.03853225409813 0.299362460504885 df.mm.trans1:probe16 0.144668781675131 0.0554366977947886 2.60962119732771 0.0092479451950449 ** df.mm.trans1:probe17 0.0660674046608819 0.0554366977947886 1.19176298894002 0.233737290151506 df.mm.trans1:probe18 0.0325659322807816 0.0554366977947886 0.587443581169493 0.557085514349005 df.mm.trans1:probe19 0.0262109493799638 0.0554366977947886 0.472808634399356 0.636489510529238 df.mm.trans1:probe20 0.0818867551221612 0.0554366977947886 1.47712180522158 0.1400695787069 df.mm.trans1:probe21 0.0846647906289172 0.0554366977947886 1.52723365562507 0.127131370249021 df.mm.trans1:probe22 0.0681500251817004 0.0554366977947886 1.22933053180716 0.219339700648297 df.mm.trans2:probe2 0.0670221178177809 0.0554366977947886 1.20898467051335 0.227055920578805 df.mm.trans2:probe3 -0.0249073911916798 0.0554366977947886 -0.449294279465925 0.653351297233395 df.mm.trans2:probe4 0.0301325231106734 0.0554366977947886 0.543548304810934 0.586916487864567 df.mm.trans2:probe5 0.00552617351214799 0.0554366977947886 0.0996843919636837 0.920621963776773 df.mm.trans2:probe6 0.076047483792562 0.0554366977947886 1.37178956932227 0.170545825122642 df.mm.trans3:probe2 -0.107885875767454 0.0554366977947886 -1.94610934740048 0.0520208175047569 . df.mm.trans3:probe3 -0.101536659726710 0.0554366977947886 -1.83157842666910 0.0674171819540542 . df.mm.trans3:probe4 -0.0189136043023193 0.0554366977947886 -0.341174800352147 0.73306910278681 df.mm.trans3:probe5 -0.105708987547721 0.0554366977947886 -1.90684134792852 0.0569291864761673 .