fitVsDatCorrelation=0.852702253413513 cont.fitVsDatCorrelation=0.303971945785232 fstatistic=8822.78177515927,49,623 cont.fstatistic=2643.95611831045,49,623 residuals=-1.04810082041234,-0.085290069271511,-0.0138172124470325,0.0728270601392281,1.02558124289994 cont.residuals=-0.61236442859028,-0.183619390549137,-0.0281787747908393,0.114259441029859,1.94670911350286 predictedValues: Include Exclude Both Lung 68.327752438062 59.2642955771235 51.4562762464977 cerebhem 70.2577050674593 58.5171503646137 45.8898403390489 cortex 62.0665231334958 65.0179350400677 48.130297334288 heart 65.4634512619098 63.5949593689322 49.5086807752575 kidney 67.9570016938708 58.4825092791695 51.2993252109443 liver 63.6963566260726 62.2400038627936 47.1674643022822 stomach 75.8274401353448 72.7567695573893 60.4000046645512 testicle 67.9994866637501 58.4882090408256 49.0441632130771 diffExp=9.06345686093857,11.7405547028456,-2.95141190657188,1.86849189297761,9.47449241470126,1.45635276327891,3.07067057795548,9.51127762292452 diffExpScore=1.11083864374580 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 65.6871931835977 69.629859840852 67.287869914486 cerebhem 61.075181167347 71.8803152316443 67.1345413849213 cortex 59.1827107759868 70.1711877467024 60.3813499183308 heart 71.959808312868 58.8742725299706 64.2501067323502 kidney 61.4787911686093 73.1435372770138 61.5241656957742 liver 65.4381401310897 69.9171161289786 70.6257850194906 stomach 63.1229009938722 63.2647419742278 61.6675553965306 testicle 67.5446484329936 63.3068989019939 68.1293407658754 cont.diffExp=-3.94266665725422,-10.8051340642974,-10.9884769707156,13.0855357828974,-11.6647461084045,-4.47897599788884,-0.141840980355610,4.23774953099974 cont.diffExpScore=2.30927867418829 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=0,0,0,1,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.363301568872428 cont.tran.correlation=-0.776133343853167 tran.covariance=0.00141573826304302 cont.tran.covariance=-0.00373107826283666 tran.mean=64.99734681943 cont.tran.mean=65.9798314873592 weightedLogRatios: wLogRatio Lung 0.591032217290996 cerebhem 0.760792639164166 cortex -0.192860689517810 heart 0.120667444075119 kidney 0.622181238798457 liver 0.0958150846892649 stomach 0.178076675052802 testicle 0.624421614703598 cont.weightedLogRatios: wLogRatio Lung -0.245634870807265 cerebhem -0.683116798060358 cortex -0.709467711620969 heart 0.838089043892673 kidney -0.730637979587845 liver -0.279002535632751 stomach -0.00930632367936897 testicle 0.270866494184944 varWeightedLogRatios=0.116813674257959 cont.varWeightedLogRatios=0.302604675299555 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.20562274837488 0.088206652500679 47.6792013883817 4.7358245741472e-210 *** df.mm.trans1 0.00148688912264229 0.0789322040679038 0.0188375472369066 0.984976732000201 df.mm.trans2 -0.204969516310091 0.0729798311885426 -2.80857756138891 0.00513205842865219 ** df.mm.exp2 0.129655513747918 0.100080790185538 1.29550849376341 0.195624681013610 df.mm.exp3 0.0633674582732107 0.100080790185538 0.633163049129959 0.526859519723841 df.mm.exp4 0.0662875796708925 0.100080790185538 0.66234069043623 0.507997841320236 df.mm.exp5 -0.0156652981494154 0.100080790185538 -0.156526523425463 0.87566872300794 df.mm.exp6 0.0658303679431153 0.100080790185538 0.657772263998652 0.510927438743224 df.mm.exp7 0.149002406256897 0.100080790185538 1.48882124112593 0.137040459658252 df.mm.exp8 0.0300135530603202 0.100080790185538 0.299893246293106 0.764358617554019 df.mm.trans1:exp2 -0.101801546369844 0.0951958199160004 -1.06939092976637 0.285307706104938 df.mm.trans2:exp2 -0.142342660325993 0.0836386066419197 -1.70187747071639 0.0892772412563663 . df.mm.trans1:exp3 -0.159476710162415 0.0951958199160003 -1.67524908449904 0.094387172770411 . df.mm.trans2:exp3 0.0292886707795960 0.0836386066419197 0.350181237535304 0.72632104425539 df.mm.trans1:exp4 -0.109111604265642 0.0951958199160004 -1.14618062391731 0.252160469742181 df.mm.trans2:exp4 0.00423960585261716 0.0836386066419197 0.0506895801213917 0.959589127080903 df.mm.trans1:exp5 0.0102244597936525 0.0951958199160003 0.107404503713235 0.914502659962065 df.mm.trans2:exp5 0.00238599452469624 0.0836386066419197 0.0285274303398113 0.977250626424257 df.mm.trans1:exp6 -0.13601901832065 0.0951958199160004 -1.42883393872411 0.153553143430893 df.mm.trans2:exp6 -0.0168394524863392 0.0836386066419197 -0.201335880192667 0.840501665676104 df.mm.trans1:exp7 -0.0448581875515942 0.0951958199160004 -0.471220139615127 0.637648499344629 df.mm.trans2:exp7 0.0561125213246149 0.0836386066419197 0.670892588692305 0.502537601199023 df.mm.trans1:exp8 -0.0348294124720792 0.0951958199160004 -0.365871237863304 0.714585357654884 df.mm.trans2:exp8 -0.0431954004713403 0.0836386066419197 -0.516452894250999 0.60572139474201 df.mm.trans1:probe2 -0.176420915807323 0.0475979099580002 -3.70648450663053 0.000228868416945812 *** df.mm.trans1:probe3 -0.0245287027759617 0.0475979099580002 -0.515331509253358 0.606504188773365 df.mm.trans1:probe4 -0.0634496477041497 0.0475979099580002 -1.33303432356876 0.183007799645006 df.mm.trans1:probe5 -0.074302817135491 0.0475979099580002 -1.56105209663733 0.119019266327175 df.mm.trans1:probe6 0.866833379281038 0.0475979099580002 18.2115849213951 9.83315416885245e-60 *** df.mm.trans1:probe7 0.0456781694770399 0.0475979099580002 0.959667546691562 0.337594848586548 df.mm.trans1:probe8 0.250103314800576 0.0475979099580002 5.25450203635546 2.04099179648262e-07 *** df.mm.trans1:probe9 0.302490045949656 0.0475979099580002 6.35511194118753 4.02422485525781e-10 *** df.mm.trans1:probe10 0.218323047338160 0.0475979099580002 4.58682004169522 5.4424230395983e-06 *** df.mm.trans1:probe11 0.265739193965144 0.0475979099580002 5.58300131664666 3.53176243451518e-08 *** df.mm.trans1:probe12 0.0647613195682853 0.0475979099580002 1.36059166516828 0.174134793471004 df.mm.trans1:probe13 0.184564203883812 0.0475979099580002 3.8775694993051 0.000116711958410241 *** df.mm.trans1:probe14 -0.293500661617486 0.0475979099580002 -6.16625103657844 1.25753927016419e-09 *** df.mm.trans1:probe15 -0.186322024407227 0.0475979099580002 -3.91450012346415 0.000100580385519610 *** df.mm.trans1:probe16 -0.232321488820640 0.0475979099580002 -4.88091786016733 1.34311746280881e-06 *** df.mm.trans1:probe17 -0.233964775872001 0.0475979099580002 -4.91544221329147 1.1341259669072e-06 *** df.mm.trans1:probe18 -0.291169993518272 0.0475979099580002 -6.11728527103809 1.6818030930263e-09 *** df.mm.trans1:probe19 -0.243971330619478 0.0475979099580002 -5.12567318259888 3.96132980330112e-07 *** df.mm.trans2:probe2 0.269650275325027 0.0475979099580002 5.66517049935518 2.24543304613318e-08 *** df.mm.trans2:probe3 0.180516279779445 0.0475979099580002 3.79252534278774 0.000163643536437698 *** df.mm.trans2:probe4 0.143610166639068 0.0475979099580002 3.01715278603173 0.00265569547121250 ** df.mm.trans2:probe5 0.122113640801681 0.0475979099580002 2.56552527011022 0.0105348177079978 * df.mm.trans2:probe6 0.0162937871578051 0.0475979099580002 0.342321483699232 0.732224423485293 df.mm.trans3:probe2 -0.0414049392879188 0.0475979099580002 -0.869889861224034 0.384695625229658 df.mm.trans3:probe3 -0.142818097858829 0.0475979099580002 -3.00051195493352 0.00280297561854721 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.13881139599795 0.160859965275087 25.7292819187183 5.10531238631189e-100 *** df.mm.trans1 0.0748457624847944 0.143946417254088 0.519955716248775 0.603279140397571 df.mm.trans2 0.0840068167892721 0.133091244004304 0.63119717166785 0.528143113181765 df.mm.exp2 -0.0387081478062983 0.182514492700253 -0.212082598119315 0.83211196170854 df.mm.exp3 0.0117694738603108 0.182514492700253 0.0644851468296277 0.948604618981972 df.mm.exp4 -0.0303889360415739 0.182514492700253 -0.166501495809882 0.867816328927711 df.mm.exp5 0.0725685027401313 0.182514492700253 0.397604056897071 0.691058263241138 df.mm.exp6 -0.0480970410351312 0.182514492700253 -0.263524503306825 0.792233476309245 df.mm.exp7 -0.0484636299630003 0.182514492700253 -0.265533050258059 0.790686694023184 df.mm.exp8 -0.0797423129578361 0.182514492700253 -0.436909484710338 0.66232838464901 df.mm.trans1:exp2 -0.034090246522021 0.173605911253728 -0.196365701351019 0.844387976262625 df.mm.trans2:exp2 0.0705170991993319 0.152529349869301 0.46231823094871 0.64401454163326 df.mm.trans1:exp3 -0.116044000104643 0.173605911253728 -0.668433460972665 0.504104519028368 df.mm.trans2:exp3 -0.00402517407624143 0.152529349869301 -0.0263895052308983 0.978955115438472 df.mm.trans1:exp4 0.121592703542060 0.173605911253728 0.700394950056458 0.483942162770693 df.mm.trans2:exp4 -0.137400363916227 0.152529349869301 -0.900812624153732 0.368036041037002 df.mm.trans1:exp5 -0.138780224229349 0.173605911253728 -0.799398034474297 0.42436432145532 df.mm.trans2:exp5 -0.0233382241291985 0.152529349869301 -0.153008087618525 0.878441440613676 df.mm.trans1:exp6 0.0442983341689284 0.173605911253728 0.255166047336866 0.79867906655432 df.mm.trans2:exp6 0.0522140301976704 0.152529349869301 0.342321200755209 0.732224636290284 df.mm.trans1:exp7 0.00864328764632393 0.173605911253728 0.0497868280170001 0.960308220558315 df.mm.trans2:exp7 -0.0474016909349103 0.152529349869301 -0.310770949823938 0.756078701239046 df.mm.trans1:exp8 0.107627172658663 0.173605911253728 0.619951082779458 0.535516672458134 df.mm.trans2:exp8 -0.0154568724766612 0.152529349869301 -0.101337037690818 0.919315520331414 df.mm.trans1:probe2 -0.165445448883283 0.0868029556268639 -1.90598865774198 0.0571116082620886 . df.mm.trans1:probe3 -0.0228982702734626 0.0868029556268638 -0.263795974550618 0.792024368204054 df.mm.trans1:probe4 0.0630338775427623 0.0868029556268639 0.726172019000405 0.468006044892625 df.mm.trans1:probe5 -0.00221716654436031 0.0868029556268639 -0.0255425236197158 0.97963040978123 df.mm.trans1:probe6 -0.0772692728876379 0.0868029556268638 -0.890168685266801 0.373718931299451 df.mm.trans1:probe7 -0.106468339523071 0.0868029556268639 -1.22655200798395 0.220454374591579 df.mm.trans1:probe8 -0.0057935423196319 0.0868029556268639 -0.0667436065718355 0.946807231644414 df.mm.trans1:probe9 -0.084555307397568 0.0868029556268638 -0.974106316852185 0.330381817625225 df.mm.trans1:probe10 -0.0684245620011703 0.0868029556268639 -0.788274564005678 0.43083595472044 df.mm.trans1:probe11 -0.0797974679560755 0.0868029556268638 -0.91929436480363 0.358297412621985 df.mm.trans1:probe12 0.0572306105429961 0.0868029556268638 0.659316380757942 0.509936253101086 df.mm.trans1:probe13 -0.159667040119040 0.0868029556268639 -1.83941939495003 0.0663291722609393 . df.mm.trans1:probe14 0.0538036550692525 0.0868029556268639 0.619836671236587 0.535591953328028 df.mm.trans1:probe15 -0.0420574397559136 0.0868029556268639 -0.484516217819864 0.628189851080484 df.mm.trans1:probe16 0.028651338040247 0.0868029556268638 0.330073300307990 0.74145553553886 df.mm.trans1:probe17 -0.00137118859410094 0.0868029556268639 -0.0157965657298031 0.987401745579536 df.mm.trans1:probe18 0.0285771506388323 0.0868029556268639 0.329218635845485 0.742101084196894 df.mm.trans1:probe19 -0.0479015611022809 0.0868029556268639 -0.551842512231879 0.581254146160717 df.mm.trans2:probe2 0.0592686249105046 0.0868029556268638 0.682795009484241 0.494990195730635 df.mm.trans2:probe3 0.0513554728711334 0.0868029556268638 0.591632767574102 0.554311142308044 df.mm.trans2:probe4 0.118045965632115 0.0868029556268639 1.35993025559583 0.174343914684776 df.mm.trans2:probe5 0.00314811158439812 0.0868029556268638 0.0362673317015929 0.971080815627158 df.mm.trans2:probe6 -0.0484406257303428 0.0868029556268639 -0.558052722750277 0.577008862831295 df.mm.trans3:probe2 -0.0851888260133219 0.0868029556268638 -0.981404669899945 0.326774181340891 df.mm.trans3:probe3 -0.18086315562198 0.0868029556268638 -2.08360595921928 0.0376030467694785 *