fitVsDatCorrelation=0.86169217937052 cont.fitVsDatCorrelation=0.228850280694749 fstatistic=12453.1541613089,59,853 cont.fstatistic=3373.20540922537,59,853 residuals=-0.505644915746195,-0.0818581075647773,-0.000553755534439648,0.0812345151933244,0.492478395623969 cont.residuals=-0.492091798945714,-0.171748253837179,-0.0567642584130255,0.093855142230785,1.39010720725853 predictedValues: Include Exclude Both Lung 48.1379539297822 49.7832323239116 59.5529753456616 cerebhem 53.8456152243105 59.8800122081446 67.3201717750916 cortex 48.1560785494861 58.231823861109 94.1660247685259 heart 55.3397854474047 53.7056256697579 96.0444636228073 kidney 52.3917401402636 51.6053842921532 68.0505164715926 liver 54.3153143459614 52.9294907494681 64.3082883578778 stomach 50.8609501024141 50.8494541658955 63.523229453654 testicle 53.2219169865233 54.0390544414111 68.4437449076097 diffExp=-1.64527839412941,-6.03439698383411,-10.0757453116228,1.63415977764684,0.786355848110368,1.38582359649328,0.0114959365186209,-0.817137454887835 diffExpScore=1.42118609915002 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,-1,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 57.6812307863003 54.5613568645321 55.5506274792863 cerebhem 55.4809070982881 56.4425395900389 54.6104167217024 cortex 57.6216508395671 56.5569097147621 58.5288220172822 heart 58.3548719107459 55.4949737093991 59.3238807838926 kidney 54.9674441780226 50.8738912565386 56.7340077535149 liver 60.8953314130109 55.0366018167155 57.3368248360824 stomach 57.5340791531269 58.962172581745 55.897125624346 testicle 56.7223207569429 58.6344275816853 53.8876514589926 cont.diffExp=3.11987392176815,-0.961632491750862,1.06474112480496,2.8598982013468,4.09355292148400,5.85872959629535,-1.42809342861815,-1.91210682474239 cont.diffExpScore=1.55521621188696 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=0.149939194452962 cont.tran.correlation=0.187584867572804 tran.covariance=0.0005516262290886 cont.tran.covariance=0.000312733679001781 tran.mean=52.9558395273748 cont.tran.mean=56.6137943282138 weightedLogRatios: wLogRatio Lung -0.130761781644094 cerebhem -0.429054434799088 cortex -0.754132549854503 heart 0.119852547214173 kidney 0.0597535482854136 liver 0.102913986366389 stomach 0.00088815560091339 testicle -0.0606740120487604 cont.weightedLogRatios: wLogRatio Lung 0.223932319753247 cerebhem -0.0691599717789224 cortex 0.075435316284012 heart 0.203082841442914 kidney 0.307091942868552 liver 0.410557826119303 stomach -0.0996593757288735 testicle -0.134431937493067 varWeightedLogRatios=0.093323877183699 cont.varWeightedLogRatios=0.0410932196630570 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.78008520787162 0.0664635571511168 56.8745545664509 1.73904507984649e-292 *** df.mm.trans1 0.0287692174389159 0.0573963022023801 0.501238169272217 0.616332839033058 df.mm.trans2 0.148823811233826 0.0507093735691441 2.93483829041782 0.00342675362021033 ** df.mm.exp2 0.174120919453649 0.0652284682325523 2.66940071063563 0.0077431101460157 ** df.mm.exp3 -0.301062963616306 0.0652284682325523 -4.61551484764225 4.52382077059608e-06 *** df.mm.exp4 -0.262684290102997 0.0652284682325523 -4.0271417867192 6.14891480521131e-05 *** df.mm.exp5 -0.0127582544661964 0.0652284682325523 -0.195593347688477 0.844975049370274 df.mm.exp6 0.105195462053354 0.0652284682325523 1.61272316986369 0.107174546640035 df.mm.exp7 0.0116762740345671 0.0652284682325523 0.179005798402913 0.85797565696547 df.mm.exp8 0.0432822372675768 0.0652284682325523 0.663548270262413 0.507158764168692 df.mm.trans1:exp2 -0.0620708738533181 0.0602920296122654 -1.02950380427550 0.30353481716111 df.mm.trans2:exp2 0.0105436169633202 0.044528614914863 0.236782953691222 0.812882026676312 df.mm.trans1:exp3 0.301439406879741 0.0602920296122654 4.99965598800175 6.9718092944036e-07 *** df.mm.trans2:exp3 0.457816743638102 0.0445286149148630 10.2814054403765 1.86978116306084e-23 *** df.mm.trans1:exp4 0.402105458510409 0.0602920296122654 6.66929710438223 4.61651015273102e-11 *** df.mm.trans2:exp4 0.338523820195055 0.0445286149148630 7.60238828093576 7.6646835555302e-14 *** df.mm.trans1:exp5 0.0974362735027193 0.0602920296122654 1.61607220936708 0.106448461975000 df.mm.trans2:exp5 0.0487060412458587 0.044528614914863 1.09381442335412 0.274345162289307 df.mm.trans1:exp6 0.0155398283225121 0.0602920296122654 0.257742663871955 0.796667683904888 df.mm.trans2:exp6 -0.0439130244488679 0.0445286149148630 -0.986175396042027 0.324326691411417 df.mm.trans1:exp7 0.0433482375700697 0.0602920296122654 0.718971277776511 0.472355522941203 df.mm.trans2:exp7 0.00951488719044466 0.044528614914863 0.213680286454828 0.83084744826739 df.mm.trans1:exp8 0.0571171187187407 0.0602920296122654 0.9473411176578 0.343733276280254 df.mm.trans2:exp8 0.0387465512768599 0.0445286149148630 0.870149483673405 0.384463515478280 df.mm.trans1:probe2 -0.063978327327323 0.041279130820509 -1.54989521473975 0.121537586484191 df.mm.trans1:probe3 0.0263424243111183 0.041279130820509 0.63815356058879 0.523545002339424 df.mm.trans1:probe4 0.0693651184461697 0.041279130820509 1.68039193334242 0.0932472140804736 . df.mm.trans1:probe5 0.0142323898260211 0.041279130820509 0.344784144993429 0.730341645232214 df.mm.trans1:probe6 0.0836545985345404 0.041279130820509 2.02655910799793 0.0430182025771589 * df.mm.trans1:probe7 -0.00233381701844648 0.041279130820509 -0.0565374554177132 0.954926898344634 df.mm.trans1:probe8 0.190320118084397 0.041279130820509 4.61056505554712 4.63019424204593e-06 *** df.mm.trans1:probe9 0.0362853011231920 0.041279130820509 0.87902289612077 0.379636410270885 df.mm.trans1:probe10 0.0655541308994193 0.041279130820509 1.58806955467312 0.112641186207162 df.mm.trans1:probe11 0.337615901753074 0.041279130820509 8.17885200202264 1.03498705534424e-15 *** df.mm.trans1:probe12 0.210947012925054 0.041279130820509 5.11025810699113 3.97192650194947e-07 *** df.mm.trans1:probe13 0.270382045876463 0.041279130820509 6.55009057850916 9.93019489373437e-11 *** df.mm.trans1:probe14 0.196244102340091 0.041279130820509 4.75407544779479 2.33942121695813e-06 *** df.mm.trans1:probe15 0.235144997435944 0.041279130820509 5.69646193517028 1.68353922289727e-08 *** df.mm.trans1:probe16 0.281102389006507 0.041279130820509 6.80979428149307 1.84349624526103e-11 *** df.mm.trans1:probe17 0.00767149869917524 0.041279130820509 0.185844482349511 0.852610903953133 df.mm.trans1:probe18 0.0404635952899928 0.041279130820509 0.9802433938335 0.327243920764073 df.mm.trans1:probe19 0.051424154518518 0.041279130820509 1.24576640777932 0.213192225667688 df.mm.trans1:probe20 0.0431471685163455 0.041279130820509 1.04525380400957 0.296201792575394 df.mm.trans1:probe21 -0.0193787069252796 0.041279130820509 -0.469455304413811 0.638864200234264 df.mm.trans1:probe22 0.0127220202892009 0.041279130820509 0.30819496526027 0.758009227001971 df.mm.trans2:probe2 -0.0935272716787765 0.041279130820509 -2.26572773747234 0.0237179675024032 * df.mm.trans2:probe3 -0.114743381637472 0.041279130820509 -2.77969471150936 0.00556072709952924 ** df.mm.trans2:probe4 -0.0662014054282775 0.041279130820509 -1.60374998485642 0.109139349657162 df.mm.trans2:probe5 -0.0432268563639345 0.041279130820509 -1.04718426732129 0.295311229969626 df.mm.trans2:probe6 -0.0219937583984044 0.041279130820509 -0.532805753445688 0.594306821986135 df.mm.trans3:probe2 0.267736384581517 0.041279130820509 6.48599859686231 1.49164626073245e-10 *** df.mm.trans3:probe3 0.484953586423945 0.041279130820509 11.7481540135289 1.20808927159914e-29 *** df.mm.trans3:probe4 0.751514065350895 0.041279130820509 18.2056659239907 8.07328563281155e-63 *** df.mm.trans3:probe5 0.119211131417691 0.041279130820509 2.88792736300694 0.00397597149923631 ** df.mm.trans3:probe6 -0.145131873535495 0.041279130820509 -3.51586553909193 0.000461430331267811 *** df.mm.trans3:probe7 -0.096407649909947 0.041279130820509 -2.33550581113612 0.0197479769930558 * df.mm.trans3:probe8 0.124863521044064 0.041279130820509 3.02485828945862 0.00256209096570181 ** df.mm.trans3:probe9 -0.168842564287225 0.041279130820509 -4.09026452183285 4.71691872855027e-05 *** df.mm.trans3:probe10 -0.046016382767144 0.041279130820509 -1.11476142671786 0.265266649075371 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.0789362671943 0.127504382957679 31.9905572857694 3.50302843581967e-148 *** df.mm.trans1 -0.0614546544453761 0.110109666260076 -0.558122247870792 0.576907364442073 df.mm.trans2 -0.0838625113892661 0.0972813924539623 -0.86206117402106 0.388896135096079 df.mm.exp2 0.0120745189716797 0.125134975462660 0.096491959398535 0.92315252488051 df.mm.exp3 -0.0173364560500874 0.125134975462660 -0.138542050182129 0.889844737965469 df.mm.exp4 -0.0371395351790615 0.125134975462660 -0.296795800228880 0.766694665120332 df.mm.exp5 -0.139245780561194 0.125134975462660 -1.11276467707259 0.266123001029095 df.mm.exp6 0.0312489771722739 0.125134975462660 0.249722166458557 0.802862339411335 df.mm.exp7 0.0687976882881244 0.125134975462660 0.549787843356818 0.582608813039027 df.mm.exp8 0.0856255705607127 0.125134975462660 0.684265691858971 0.493993264180227 df.mm.trans1:exp2 -0.0509674045839041 0.115664859999881 -0.440647268184621 0.65958002505214 df.mm.trans2:exp2 0.0218227208366179 0.0854241604278066 0.255463099986340 0.798427025533844 df.mm.trans1:exp3 0.0163030048267388 0.115664859999881 0.140950370118942 0.887942428087988 df.mm.trans2:exp3 0.0532579564097539 0.0854241604278065 0.623453085673147 0.533153489010008 df.mm.trans1:exp4 0.0487505541100825 0.115664859999881 0.421481114576483 0.673509986670583 df.mm.trans2:exp4 0.0541061055917545 0.0854241604278065 0.633381766010805 0.526654148650199 df.mm.trans1:exp5 0.0910550355711312 0.115664859999881 0.787231623945467 0.431364962413914 df.mm.trans2:exp5 0.069269748159264 0.0854241604278065 0.810891764254506 0.417654016837862 df.mm.trans1:exp6 0.0229757037897919 0.115664859999881 0.198640311239002 0.842591451933035 df.mm.trans2:exp6 -0.0225764081457582 0.0854241604278065 -0.264285982240796 0.791623400909706 df.mm.trans1:exp7 -0.071352066057234 0.115664859999881 -0.616886287307205 0.537474345260739 df.mm.trans2:exp7 0.0087725248715083 0.0854241604278065 0.102693720694184 0.91823019409443 df.mm.trans1:exp8 -0.10238960407918 0.115664859999881 -0.885226542264312 0.376283925308221 df.mm.trans2:exp8 -0.0136294275391162 0.0854241604278065 -0.159550032108711 0.8732733136703 df.mm.trans1:probe2 0.034046226096659 0.0791903161657645 0.429929159840605 0.66735575950703 df.mm.trans1:probe3 0.0397770763236213 0.0791903161657645 0.502297228367648 0.61558809745643 df.mm.trans1:probe4 0.097451532896136 0.0791903161657645 1.23059911381268 0.218812070250702 df.mm.trans1:probe5 0.0529575299142303 0.0791903161657645 0.668737447687131 0.503843906693439 df.mm.trans1:probe6 0.0399797255709289 0.0791903161657645 0.504856243877618 0.613790208033227 df.mm.trans1:probe7 0.0686538597446489 0.0791903161657645 0.866947665683513 0.386214486691523 df.mm.trans1:probe8 0.0570029067001847 0.0791903161657645 0.719821683510694 0.471831969729507 df.mm.trans1:probe9 0.0243907595298715 0.0791903161657645 0.308001795053018 0.758156159305813 df.mm.trans1:probe10 0.137123287477061 0.0791903161657645 1.73156635957897 0.0837123514426953 . df.mm.trans1:probe11 0.0741300110527333 0.0791903161657645 0.93609944551252 0.349486928469453 df.mm.trans1:probe12 -0.0061970632209639 0.0791903161657645 -0.078255316066575 0.93764331383307 df.mm.trans1:probe13 0.064411218647364 0.0791903161657645 0.813372414280247 0.416231542546431 df.mm.trans1:probe14 0.09044043650844 0.0791903161657645 1.14206434432117 0.253747753589394 df.mm.trans1:probe15 0.0293411857237709 0.0791903161657645 0.370514819796309 0.711090922733652 df.mm.trans1:probe16 0.048797630613495 0.0791903161657645 0.616207043691425 0.537922281130398 df.mm.trans1:probe17 0.00997923482668139 0.0791903161657645 0.126015847768462 0.899749075048515 df.mm.trans1:probe18 0.0737082228282405 0.0791903161657645 0.930773185372203 0.352234245876133 df.mm.trans1:probe19 -0.0102721910753669 0.0791903161657645 -0.129715242629727 0.896822303502904 df.mm.trans1:probe20 0.133949946716241 0.0791903161657645 1.69149402606061 0.0911077485130878 . df.mm.trans1:probe21 0.0517933531619012 0.0791903161657645 0.654036448768371 0.513264606008966 df.mm.trans1:probe22 0.0869420917710766 0.0791903161657645 1.09788792343101 0.272563240835148 df.mm.trans2:probe2 -0.0141724566254149 0.0791903161657645 -0.178967041825525 0.858006079375107 df.mm.trans2:probe3 0.0201603670069023 0.0791903161657645 0.254581216277780 0.799107929106758 df.mm.trans2:probe4 0.0267228129546423 0.0791903161657645 0.337450514766288 0.73586032953834 df.mm.trans2:probe5 -0.107716271257954 0.0791903161657645 -1.3602202450168 0.174119591213963 df.mm.trans2:probe6 0.143039574144331 0.0791903161657645 1.80627608361753 0.0712275741491222 . df.mm.trans3:probe2 0.114753328260007 0.0791903161657645 1.44908284012657 0.147681964822722 df.mm.trans3:probe3 0.186755953070746 0.0791903161657645 2.35831806353470 0.0185827130140181 * df.mm.trans3:probe4 0.0725127835055858 0.0791903161657645 0.915677408760423 0.360094902415195 df.mm.trans3:probe5 0.159109541040421 0.0791903161657645 2.00920451823132 0.0448300558109865 * df.mm.trans3:probe6 0.0648010263979155 0.0791903161657645 0.8182948311795 0.413417386513848 df.mm.trans3:probe7 0.0660813248831725 0.0791903161657645 0.834462192887932 0.40425421474643 df.mm.trans3:probe8 0.0258290008363264 0.0791903161657645 0.326163628167111 0.744380560655885 df.mm.trans3:probe9 0.098628867435264 0.0791903161657645 1.2454662667189 0.213302413243900 df.mm.trans3:probe10 0.0482852787597279 0.0791903161657645 0.609737163552361 0.542198314636995