fitVsDatCorrelation=0.845191894074173 cont.fitVsDatCorrelation=0.229468882821165 fstatistic=8267.9928651361,61,899 cont.fstatistic=2482.7366999724,61,899 residuals=-0.800016796987627,-0.0923893246298574,-0.00347562252300106,0.0969205175150089,1.05672415496680 cont.residuals=-0.736279216590653,-0.251144104333158,-0.048272013547972,0.193980209682402,1.35164616931450 predictedValues: Include Exclude Both Lung 60.9949465978829 54.350117235842 65.5108136688341 cerebhem 59.9866889982902 54.0017236664322 76.0591371483882 cortex 78.9630179303917 52.4936831468052 86.3549743238554 heart 71.8410618712819 56.890654304462 76.0687325736425 kidney 66.5831754074405 62.4221975463098 67.7125177860366 liver 57.3697698274323 63.1683255739613 58.3470068199398 stomach 56.5585536284146 48.4244213210141 67.3266008952511 testicle 55.7269433880872 52.7699177483066 62.3127579345544 diffExp=6.64482936204087,5.98496533185794,26.4693347835866,14.9504075668198,4.16097786113077,-5.79855574652898,8.13413230740052,2.95702563978065 diffExpScore=1.16428836261710 diffExp1.5=0,0,1,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,1,0,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,1,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,0,1,1,0,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 63.6196961440215 64.8042504732 68.4655632728739 cerebhem 64.6653540973568 75.591044471447 68.8686587712027 cortex 70.7135681501849 71.8535400900298 67.7127871883618 heart 69.5310661390625 69.905595024644 65.671842305676 kidney 69.6352344682602 68.465443296677 64.6342167547257 liver 70.1605718745879 66.1440583263046 65.480940184559 stomach 71.7639958418027 73.8135288871477 64.4253412158278 testicle 72.0286941786234 61.9675688303014 66.6624814695759 cont.diffExp=-1.18455432917852,-10.9256903740902,-1.13997193984491,-0.374528885581469,1.1697911715832,4.01651354828324,-2.04953304534507,10.0611253483220 cont.diffExpScore=21.6713326715583 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=0.0610075941683939 cont.tran.correlation=-0.119856050330238 tran.covariance=0.00114157143719516 cont.tran.covariance=-0.000354772592317583 tran.mean=59.5340748870222 cont.tran.mean=69.0414506433532 weightedLogRatios: wLogRatio Lung 0.467503948758777 cerebhem 0.424796102765747 cortex 1.70044754813905 heart 0.97011772145386 kidney 0.268848102116945 liver -0.394545230812195 stomach 0.614511483836911 testicle 0.217719242885783 cont.weightedLogRatios: wLogRatio Lung -0.0767836433188068 cerebhem -0.663052676568941 cortex -0.06823377971491 heart -0.0228014147677254 kidney 0.0717440365199837 liver 0.248852322828492 stomach -0.120731227731091 testicle 0.632180814717649 varWeightedLogRatios=0.372308901541466 cont.varWeightedLogRatios=0.133144779683525 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.94945532510893 0.084714107298387 46.6209873545362 3.85688062910167e-242 *** df.mm.trans1 -0.0766269379908099 0.0728694143744636 -1.05156516830281 0.293281666681273 df.mm.trans2 0.0760666800288867 0.0640976440125374 1.18673129411758 0.235647131580351 df.mm.exp2 -0.172395070203091 0.0818153842933265 -2.10712290472188 0.0353833962793789 * df.mm.exp3 -0.0528165182272111 0.0818153842933265 -0.645557295653005 0.518730828301262 df.mm.exp4 0.0599274118006166 0.0818153842933265 0.732471188863007 0.46407194109566 df.mm.exp5 0.193079236988886 0.0818153842933265 2.35993803190673 0.0184908047726728 * df.mm.exp6 0.204889878506195 0.0818153842933265 2.50429525297612 0.0124456675150292 * df.mm.exp7 -0.218297274399172 0.0818153842933265 -2.66816902816868 0.00776388016957537 ** df.mm.exp8 -0.0697837105759172 0.0818153842933265 -0.85294118188979 0.393919105824982 df.mm.trans1:exp2 0.155726739700327 0.0752592075520493 2.06920514798971 0.0388121153926415 * df.mm.trans2:exp2 0.165964265388583 0.0540165859666245 3.07246862086264 0.00218693190090914 ** df.mm.trans1:exp3 0.311005115540957 0.0752592075520492 4.13245270123083 3.92440905011895e-05 *** df.mm.trans2:exp3 0.0180625889542974 0.0540165859666245 0.334389681077917 0.738163518751493 df.mm.trans1:exp4 0.103737775059123 0.0752592075520493 1.37840642272745 0.168420764204088 df.mm.trans2:exp4 -0.0142431025013147 0.0540165859666245 -0.263680168719200 0.792086804575496 df.mm.trans1:exp5 -0.105418330950643 0.0752592075520493 -1.40073665906907 0.161637898959625 df.mm.trans2:exp5 -0.0546050655796804 0.0540165859666245 -1.01089442441659 0.312338958193549 df.mm.trans1:exp6 -0.266163390056423 0.0752592075520493 -3.53662227804278 0.000425992974442859 *** df.mm.trans2:exp6 -0.054533651230166 0.0540165859666245 -1.00957234253681 0.312971869253311 df.mm.trans1:exp7 0.142782705235881 0.0752592075520493 1.89721244589418 0.0581201324298624 . df.mm.trans2:exp7 0.102854762997854 0.0540165859666245 1.90413298355075 0.0572118817443363 . df.mm.trans1:exp8 -0.020543554226281 0.0752592075520493 -0.272970642324038 0.784938473667652 df.mm.trans2:exp8 0.0402782286913362 0.0540165859666245 0.745664095028572 0.456065268272562 df.mm.trans1:probe2 0.359961488992219 0.0532162960067799 6.76412144404713 2.41394826273806e-11 *** df.mm.trans1:probe3 0.37479935158974 0.0532162960067799 7.04294322817938 3.74009819094584e-12 *** df.mm.trans1:probe4 0.110258799359901 0.0532162960067799 2.07189916686148 0.0385595060805205 * df.mm.trans1:probe5 0.000766590712231675 0.0532162960067799 0.0144051873158179 0.988509917066322 df.mm.trans1:probe6 0.248761981899078 0.0532162960067799 4.67454521576221 3.39716568485496e-06 *** df.mm.trans1:probe7 -0.0930260821515977 0.0532162960067799 -1.74807510353118 0.0807923825092988 . df.mm.trans1:probe8 0.0488456822432957 0.0532162960067799 0.917870763441947 0.358932707520482 df.mm.trans1:probe9 0.0741163848009276 0.0532162960067799 1.39273850986331 0.164043207822593 df.mm.trans1:probe10 0.283870933418244 0.0532162960067799 5.33428582444142 1.21438533235635e-07 *** df.mm.trans1:probe11 0.4860726185342 0.0532162960067799 9.13390549526922 4.27703386856999e-19 *** df.mm.trans1:probe12 0.570180154527732 0.0532162960067799 10.7143900893645 2.71665997937084e-25 *** df.mm.trans1:probe13 0.300233311851233 0.0532162960067799 5.64175514607372 2.25501765827438e-08 *** df.mm.trans1:probe14 0.410616772479788 0.0532162960067799 7.71599685230769 3.1858418860949e-14 *** df.mm.trans1:probe15 0.678550450564247 0.0532162960067799 12.7508019437843 2.39023695990895e-34 *** df.mm.trans1:probe16 0.800367035870162 0.0532162960067799 15.0398861989228 9.09811337920291e-46 *** df.mm.trans1:probe17 0.778491140066025 0.0532162960067799 14.6288110688283 1.24741494084595e-43 *** df.mm.trans1:probe18 0.35392771127428 0.0532162960067799 6.65073930040507 5.05784205990535e-11 *** df.mm.trans1:probe19 0.710521550797214 0.0532162960067799 13.3515784470737 3.15667169596588e-37 *** df.mm.trans1:probe20 0.409369206341969 0.0532162960067799 7.69255354205438 3.78446200122041e-14 *** df.mm.trans1:probe21 0.659570216120317 0.0532162960067799 12.3941398709201 1.10905452892196e-32 *** df.mm.trans1:probe22 0.524474153549186 0.0532162960067799 9.855517818872 7.90232880427398e-22 *** df.mm.trans2:probe2 -0.159677160016957 0.0532162960067799 -3.00053126577268 0.00276949439458413 ** df.mm.trans2:probe3 -0.0425435079220337 0.0532162960067799 -0.799445115770808 0.424243530244343 df.mm.trans2:probe4 -0.0707824829159936 0.0532162960067799 -1.33009037132114 0.183825829867778 df.mm.trans2:probe5 -0.128733700632015 0.0532162960067799 -2.41906540461994 0.0157580581003210 * df.mm.trans2:probe6 -0.139617369867776 0.0532162960067799 -2.62358300641571 0.00884845937513527 ** df.mm.trans3:probe2 0.669987298879929 0.0532162960067799 12.5898897359292 1.36249919101016e-33 *** df.mm.trans3:probe3 0.352560869805824 0.0532162960067799 6.62505465921392 5.97149074327097e-11 *** df.mm.trans3:probe4 0.204585562299029 0.0532162960067799 3.8444156705864 0.000129350902743850 *** df.mm.trans3:probe5 0.0726796623419155 0.0532162960067799 1.36574071845692 0.172361942229402 df.mm.trans3:probe6 0.298916546332742 0.0532162960067799 5.61701149389764 2.59013767443291e-08 *** df.mm.trans3:probe7 -0.0681834341638494 0.0532162960067799 -1.28125103173589 0.200435894838459 df.mm.trans3:probe8 0.158438177369148 0.0532162960067799 2.97724924990951 0.00298649802979727 ** df.mm.trans3:probe9 -0.0809046421847793 0.0532162960067799 -1.52029825928644 0.128787643552634 df.mm.trans3:probe10 0.462337787668566 0.0532162960067799 8.68789867693278 1.72377582059462e-17 *** df.mm.trans3:probe11 0.571086463052183 0.0532162960067799 10.7314207471227 2.30737703011864e-25 *** df.mm.trans3:probe12 0.158337581179275 0.0532162960067799 2.97535892312201 0.0030047819220229 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.14788467701026 0.154273731025041 26.8865259785348 2.57483567680712e-117 *** df.mm.trans1 0.0115169017940613 0.132703239066916 0.0867868928825006 0.930860232055156 df.mm.trans2 0.0383912664310627 0.116728877952979 0.328892619412717 0.742313452692005 df.mm.exp2 0.164398782729681 0.148994836783452 1.10338577012985 0.270154935814426 df.mm.exp3 0.220028893485541 0.148994836783452 1.47675515632350 0.140091474799747 df.mm.exp4 0.206285670795383 0.148994836783452 1.38451556610111 0.166544181527153 df.mm.exp5 0.202892468255367 0.148994836783452 1.36174160551784 0.173620587495828 df.mm.exp6 0.162899041381516 0.148994836783452 1.09332004315205 0.274546177444428 df.mm.exp7 0.311454467608013 0.148994836783452 2.09037087681554 0.0368648949511914 * df.mm.exp8 0.106070024521306 0.148994836783452 0.711904028429304 0.476708902348664 df.mm.trans1:exp2 -0.148096320205802 0.137055315971719 -1.08055874488196 0.28018323016674 df.mm.trans2:exp2 -0.0104321608627318 0.0983701595881032 -0.106050055285195 0.915566296091304 df.mm.trans1:exp3 -0.114314537596560 0.137055315971719 -0.834075911511148 0.404459772569686 df.mm.trans2:exp3 -0.116770206539450 0.0983701595881032 -1.18704907086042 0.235521833707875 df.mm.trans1:exp4 -0.117435133492188 0.137055315971719 -0.856844790437899 0.39175900927392 df.mm.trans2:exp4 -0.130511176417172 0.0983701595881032 -1.3267354344412 0.184933111251840 df.mm.trans1:exp5 -0.112544896854155 0.137055315971719 -0.821164039178004 0.411770536616056 df.mm.trans2:exp5 -0.147934522628786 0.0983701595881033 -1.50385567379598 0.132969739551082 df.mm.trans1:exp6 -0.0650356525464978 0.137055315971719 -0.474521196681765 0.635243526468868 df.mm.trans2:exp6 -0.142435171003881 0.0983701595881032 -1.44795100059091 0.147979399685492 df.mm.trans1:exp7 -0.190994678195329 0.137055315971719 -1.39355906657966 0.163795202654021 df.mm.trans2:exp7 -0.181283629477567 0.0983701595881032 -1.84287217014428 0.0656767527178368 . df.mm.trans1:exp8 0.0180714351595287 0.137055315971719 0.131855047222376 0.89512844822292 df.mm.trans2:exp8 -0.150830054643566 0.0983701595881033 -1.53329073852400 0.125556055812307 df.mm.trans1:probe2 -0.00385081154626827 0.0969127433212678 -0.0397348317083827 0.968313354039022 df.mm.trans1:probe3 0.0816040529007743 0.0969127433212678 0.842036352538852 0.399991545164413 df.mm.trans1:probe4 -0.0633899381006786 0.0969127433212678 -0.654092907993943 0.513219231698056 df.mm.trans1:probe5 -0.0686546148894656 0.0969127433212678 -0.70841679367051 0.478870107018214 df.mm.trans1:probe6 0.103367367318732 0.0969127433212677 1.06660242787749 0.286437753277729 df.mm.trans1:probe7 -0.0118738955623931 0.0969127433212678 -0.122521509096393 0.902513395641068 df.mm.trans1:probe8 -0.128164033555857 0.0969127433212678 -1.32246832731780 0.186348582141600 df.mm.trans1:probe9 0.064485783936751 0.0969127433212678 0.66540045949354 0.50596506417986 df.mm.trans1:probe10 -0.0584926123313052 0.0969127433212678 -0.603559556016292 0.546288759371946 df.mm.trans1:probe11 -0.024669824101642 0.0969127433212677 -0.254557071198171 0.799123434786342 df.mm.trans1:probe12 -0.0258670863121658 0.0969127433212678 -0.266911093687812 0.789598827480105 df.mm.trans1:probe13 -0.0753405580922855 0.0969127433212678 -0.777406102750904 0.437123732999636 df.mm.trans1:probe14 -0.0545063369639312 0.0969127433212678 -0.56242693268151 0.573965384032864 df.mm.trans1:probe15 0.073881579322784 0.0969127433212677 0.762351542127593 0.446050185972468 df.mm.trans1:probe16 0.0255836769364445 0.0969127433212678 0.263986716913317 0.791850655299024 df.mm.trans1:probe17 0.132971652796949 0.0969127433212678 1.37207603706094 0.170382032842140 df.mm.trans1:probe18 -0.084288374519454 0.0969127433212678 -0.869734687419139 0.384677563503108 df.mm.trans1:probe19 0.0349719222724542 0.0969127433212677 0.360859893899831 0.718288963248 df.mm.trans1:probe20 -0.0492574695276182 0.0969127433212678 -0.508266176764067 0.611391377955056 df.mm.trans1:probe21 -0.072729065396402 0.0969127433212678 -0.750459257512747 0.453174519647152 df.mm.trans1:probe22 -0.0160493470713172 0.0969127433212678 -0.165606157882801 0.868504134107273 df.mm.trans2:probe2 -0.0527103275686929 0.0969127433212677 -0.5438947011742 0.586648790068499 df.mm.trans2:probe3 -0.0659243613303055 0.0969127433212677 -0.68024450728595 0.496524792889873 df.mm.trans2:probe4 -0.00868100528812327 0.0969127433212677 -0.0895754778021871 0.928644523503909 df.mm.trans2:probe5 -0.108133468334501 0.0969127433212678 -1.11578172930299 0.264813730123899 df.mm.trans2:probe6 -0.0328363106951138 0.0969127433212677 -0.338823456748725 0.73482185130867 df.mm.trans3:probe2 0.0175814551583098 0.0969127433212677 0.181415307789058 0.856082497347513 df.mm.trans3:probe3 -0.0558275098817323 0.0969127433212677 -0.576059535294166 0.564719120030179 df.mm.trans3:probe4 0.0763608014534408 0.0969127433212677 0.78793354554316 0.430943277827269 df.mm.trans3:probe5 -0.00604117761179748 0.0969127433212677 -0.0623362563555842 0.95030891230904 df.mm.trans3:probe6 -0.000231448503446227 0.0969127433212677 -0.00238821537306988 0.998095011472492 df.mm.trans3:probe7 0.0730473595261408 0.0969127433212677 0.753743594730234 0.451200560200891 df.mm.trans3:probe8 -0.0811568527176798 0.0969127433212677 -0.83742189041789 0.402578028801707 df.mm.trans3:probe9 0.0403477063750682 0.0969127433212677 0.416330247110173 0.677267735762875 df.mm.trans3:probe10 0.0818243955647707 0.0969127433212678 0.84430997163625 0.398720832219255 df.mm.trans3:probe11 0.158143753683867 0.0969127433212677 1.63181588162887 0.103068490985891 df.mm.trans3:probe12 0.0384080891166426 0.0969127433212678 0.396316189185967 0.69196583276772