fitVsDatCorrelation=0.759212620180102 cont.fitVsDatCorrelation=0.249701219313881 fstatistic=9998.41950232433,59,853 cont.fstatistic=4508.99978046774,59,853 residuals=-0.406929665290088,-0.0899162200765363,-0.00384463044816904,0.0726414811106295,1.74380308176532 cont.residuals=-0.465430713093813,-0.157828126379956,-0.0307237002126979,0.135664681720597,1.76459959620528 predictedValues: Include Exclude Both Lung 54.8601331389367 50.5196353138617 55.9482151272723 cerebhem 63.6847232582428 52.2968725796361 58.7755735667296 cortex 59.3380937474034 52.1960745101606 56.8638001664492 heart 55.0037856484474 50.9781400771136 55.7108636601715 kidney 52.2306431509728 52.1756029294334 57.0148898798419 liver 50.734298262583 52.9070689893246 58.8073195276036 stomach 52.6040111095616 60.5564712270723 54.7190033328543 testicle 54.8345927166621 52.4020124821528 57.5796592506091 diffExp=4.34049782507497,11.3878506786067,7.1420192372428,4.02564557133377,0.0550402215393788,-2.17277072674157,-7.95246011751066,2.43258023450925 diffExpScore=1.95024577014641 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 54.1742940284842 55.3881661067925 51.5990798733301 cerebhem 59.5790467435983 50.7496801917286 57.2214508981786 cortex 55.7220975732601 55.7740417388772 57.9541924494937 heart 55.6176699114285 52.9801422105484 54.8873531807974 kidney 53.3535926536207 57.3495020564709 59.0505036757605 liver 55.3885243412687 56.1204349575114 55.3703181832353 stomach 57.0608541105253 59.2552720604749 53.2289211357303 testicle 56.1839323877641 54.610421939351 54.4471627497718 cont.diffExp=-1.21387207830833,8.82936655186965,-0.051944165617158,2.63752770088006,-3.99590940285023,-0.731910616242672,-2.19441794994965,1.57351044841310 cont.diffExpScore=3.62733895670679 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=-0.264304284352803 cont.tran.correlation=-0.512912770252155 tran.covariance=-0.00112784815555774 cont.tran.covariance=-0.000831048116587456 tran.mean=54.2076349463478 cont.tran.mean=55.5817295632316 weightedLogRatios: wLogRatio Lung 0.326696968199815 cerebhem 0.79895481345581 cortex 0.515430029364665 heart 0.301694595000506 kidney 0.00417009384559692 liver -0.165540473260540 stomach -0.56780768409576 testicle 0.180671588001877 cont.weightedLogRatios: wLogRatio Lung -0.0887106081790055 cerebhem 0.642734098401721 cortex -0.00374648547103252 heart 0.194053801713189 kidney -0.289834083159154 liver -0.0527851427878067 stomach -0.153322654382821 testicle 0.114034128152546 varWeightedLogRatios=0.177316813386660 cont.varWeightedLogRatios=0.0807900691957047 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.70721308687898 0.0735809334516218 50.3827950119226 6.93052860609967e-258 *** df.mm.trans1 0.233306195662176 0.0635426942786125 3.67164468411129 0.000255991249576041 *** df.mm.trans2 0.244860888222799 0.0561396831872988 4.36163644539763 1.44875791643752e-05 *** df.mm.exp2 0.134432470900192 0.0722135826895027 1.86159536604371 0.0630039065146504 . df.mm.exp3 0.0948773853192084 0.0722135826895026 1.31384404132327 0.189251926048648 df.mm.exp4 0.0159013028758703 0.0722135826895026 0.220198227032182 0.825769465537867 df.mm.exp5 -0.0357505528453814 0.0722135826895026 -0.495066876810397 0.620680410039429 df.mm.exp6 -0.0818496516466814 0.0722135826895026 -1.13343845573500 0.257348631766694 df.mm.exp7 0.161435194702879 0.0722135826895026 2.23552396502752 0.0256410072670429 * df.mm.exp8 0.00737442000364745 0.0722135826895026 0.102119569878638 0.91868576801221 df.mm.trans1:exp2 0.0147253272622575 0.0667485161601641 0.220609057839187 0.825449638494513 df.mm.trans2:exp2 -0.0998579781371888 0.0492970462488761 -2.02563816162648 0.043112768983501 * df.mm.trans1:exp3 -0.0164128076077885 0.0667485161601641 -0.24589022426215 0.805826363756232 df.mm.trans2:exp3 -0.0622321730205435 0.0492970462488761 -1.26239151746262 0.207152960432984 df.mm.trans1:exp4 -0.0132862024322805 0.0667485161601641 -0.199048656009072 0.842272118718293 df.mm.trans2:exp4 -0.00686646678426537 0.0492970462488761 -0.139287590367991 0.889255774170573 df.mm.trans1:exp5 -0.0133670033512522 0.0667485161601641 -0.200259183577622 0.84132561650132 df.mm.trans2:exp5 0.0680034828411847 0.0492970462488761 1.37946363962395 0.168113373242632 df.mm.trans1:exp6 0.00366491546372195 0.0667485161601641 0.0549063211372059 0.956225953013703 df.mm.trans2:exp6 0.128024532063719 0.0492970462488761 2.59700208846971 0.0095660931510382 ** df.mm.trans1:exp7 -0.203429733422826 0.0667485161601641 -3.04770420565894 0.00237706925875307 ** df.mm.trans2:exp7 0.0197790648834128 0.0492970462488761 0.401222109405059 0.6883570915372 df.mm.trans1:exp8 -0.00784008364493745 0.0667485161601641 -0.117457047676162 0.90652552971292 df.mm.trans2:exp8 0.0292084979263895 0.0492970462488761 0.592499959915051 0.553672875052294 df.mm.trans1:probe2 -0.0117988443565467 0.04569958497615 -0.258182746357638 0.796328152091953 df.mm.trans1:probe3 -0.133745252391714 0.04569958497615 -2.92661853409639 0.00351768600665295 ** df.mm.trans1:probe4 0.0676799427611411 0.04569958497615 1.48097499783559 0.138982506219326 df.mm.trans1:probe5 -0.103906848282837 0.04569958497615 -2.27369347745860 0.0232321783125461 * df.mm.trans1:probe6 -0.131891896443046 0.04569958497615 -2.88606333103198 0.00399936385010885 ** df.mm.trans1:probe7 -0.0183098103200637 0.04569958497615 -0.400655943147391 0.688773780742142 df.mm.trans1:probe8 0.0278487518687991 0.04569958497615 0.609387413109616 0.542429952460931 df.mm.trans1:probe9 0.255588604048866 0.04569958497615 5.5927992383794 3.00917494694347e-08 *** df.mm.trans1:probe10 -0.103558126166875 0.04569958497615 -2.26606272728561 0.0236973617631647 * df.mm.trans1:probe11 0.00736535663726334 0.04569958497615 0.161169004950641 0.87199845555441 df.mm.trans1:probe12 0.138165505641665 0.04569958497615 3.0233426783585 0.00257482024036812 ** df.mm.trans1:probe13 -0.0272179682116363 0.04569958497615 -0.595584581913403 0.551610762436495 df.mm.trans1:probe14 0.0273569962697094 0.04569958497615 0.598626798995804 0.549580708085062 df.mm.trans1:probe15 0.365817760033543 0.04569958497615 8.00483768560389 3.90186800760835e-15 *** df.mm.trans1:probe16 0.0607112078247471 0.04569958497615 1.32848488353738 0.184373273190043 df.mm.trans1:probe17 0.208163043112767 0.04569958497615 4.55503136891516 6.00062645929681e-06 *** df.mm.trans1:probe18 0.361226888581178 0.04569958497615 7.90438006755854 8.30416669102251e-15 *** df.mm.trans1:probe19 0.212775862117244 0.04569958497615 4.65596924410339 3.73812933660916e-06 *** df.mm.trans1:probe20 0.261205742628227 0.04569958497615 5.71571367145996 1.50985861553366e-08 *** df.mm.trans1:probe21 0.273541327808581 0.04569958497615 5.98564140027394 3.17181163336256e-09 *** df.mm.trans1:probe22 0.31954591206544 0.04569958497615 6.99231540575712 5.45882576272676e-12 *** df.mm.trans2:probe2 -0.0901367489285202 0.04569958497615 -1.97237565670588 0.048889365837206 * df.mm.trans2:probe3 -0.0516133311727094 0.04569958497615 -1.12940481187402 0.259044607868247 df.mm.trans2:probe4 -0.121583026964959 0.04569958497615 -2.66048426978958 0.00794937963653694 ** df.mm.trans2:probe5 -0.102982507640230 0.04569958497615 -2.25346702150524 0.0244829344627314 * df.mm.trans2:probe6 -0.109074726003306 0.04569958497615 -2.38677716789399 0.0172138171052954 * df.mm.trans3:probe2 0.0751008838731411 0.04569958497615 1.64336030430769 0.100676923825627 df.mm.trans3:probe3 -0.417903823691377 0.04569958497615 -9.14458684711196 4.29729629564598e-19 *** df.mm.trans3:probe4 0.104770989546440 0.04569958497615 2.2926026483855 0.0221135226114660 * df.mm.trans3:probe5 -0.0178175283893541 0.04569958497615 -0.389883811825706 0.696719793724045 df.mm.trans3:probe6 0.0542068631250935 0.04569958497615 1.18615657348712 0.235890756084988 df.mm.trans3:probe7 -0.314666571191003 0.04569958497615 -6.88554548920354 1.11618386690394e-11 *** df.mm.trans3:probe8 -0.399815102446574 0.04569958497615 -8.74876878324459 1.13834003347956e-17 *** df.mm.trans3:probe9 -0.40062671748868 0.04569958497615 -8.76652857345996 9.85168157706296e-18 *** df.mm.trans3:probe10 -0.292785370591436 0.04569958497615 -6.40674025254796 2.45534566011004e-10 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.00481023893893 0.109473621125211 36.5824223020668 6.10587997704076e-177 *** df.mm.trans1 -0.013031339921872 0.0945387413888348 -0.137841267298816 0.890398399806398 df.mm.trans2 -0.00260886696109964 0.0835245507095462 -0.0312347320510814 0.975089647870967 df.mm.exp2 -0.0957882183337598 0.107439278364720 -0.891556791814921 0.372881954087978 df.mm.exp3 -0.0810361990809846 0.107439278364720 -0.754251148317418 0.450906593545494 df.mm.exp4 -0.0799334763326378 0.107439278364720 -0.743987464819811 0.457088946825107 df.mm.exp5 -0.115356232924788 0.107439278364720 -1.07368771161318 0.283266306240190 df.mm.exp6 -0.0352398795342269 0.107439278364720 -0.327998103399388 0.742993587752571 df.mm.exp7 0.0883025529265802 0.107439278364720 0.821883339785875 0.411372959727436 df.mm.exp8 -0.0314438209452074 0.107439278364720 -0.292665973038895 0.769848656093045 df.mm.trans1:exp2 0.190885649864938 0.0993083591905258 1.92215087854506 0.0549195815296543 . df.mm.trans2:exp2 0.00832757183986494 0.0733440839968376 0.113541152688252 0.909628255629204 df.mm.trans1:exp3 0.109206476151806 0.0993083591905258 1.09967053168495 0.271785953416932 df.mm.trans2:exp3 0.08797879566096 0.0733440839968376 1.19953499814318 0.230653064444269 df.mm.trans1:exp4 0.106227915240471 0.0993083591905258 1.06967747837490 0.285067098591642 df.mm.trans2:exp4 0.0354846816486496 0.0733440839968376 0.483811095795806 0.628644116863518 df.mm.trans1:exp5 0.100091033594451 0.0993083591905258 1.00788125400827 0.313797284815888 df.mm.trans2:exp5 0.150154431169793 0.0733440839968376 2.04726029677141 0.0409384169380893 * df.mm.trans1:exp6 0.0574057939542335 0.0993083591905258 0.578056010814748 0.56337887168024 df.mm.trans2:exp6 0.0483739224735562 0.0733440839968376 0.659547707701168 0.509722144832641 df.mm.trans1:exp7 -0.0363907546365768 0.0993083591905258 -0.366442008841976 0.71412614917034 df.mm.trans2:exp7 -0.0208137597013113 0.0733440839968376 -0.283782393440331 0.776646013165193 df.mm.trans1:exp8 0.0678681203718603 0.0993083591905258 0.683407931870604 0.494534694032309 df.mm.trans2:exp8 0.0173026007361489 0.0733440839968376 0.235909971101350 0.813559160885408 df.mm.trans1:probe2 -0.0522494635072839 0.0679917855968456 -0.768467294227202 0.442422311940138 df.mm.trans1:probe3 0.0749109115009744 0.0679917855968456 1.1017641446447 0.270874998918554 df.mm.trans1:probe4 0.0256153896998628 0.0679917855968455 0.376742417852476 0.706458733448198 df.mm.trans1:probe5 0.0687696693001726 0.0679917855968456 1.0114408482804 0.312092290856183 df.mm.trans1:probe6 0.000301449590123526 0.0679917855968455 0.00443361778893348 0.996463533051679 df.mm.trans1:probe7 0.106827891542713 0.0679917855968456 1.57118820464791 0.116509775119814 df.mm.trans1:probe8 0.0123554241591212 0.0679917855968456 0.181719365812543 0.855846139663429 df.mm.trans1:probe9 -0.0355406841902508 0.0679917855968456 -0.522720265077134 0.6013046701623 df.mm.trans1:probe10 -0.0204044128508938 0.0679917855968456 -0.300101147098576 0.76417312230775 df.mm.trans1:probe11 -0.0447752062290813 0.0679917855968456 -0.658538466610863 0.510369892564768 df.mm.trans1:probe12 0.0319594296894622 0.0679917855968456 0.470048394948241 0.638440589840531 df.mm.trans1:probe13 0.0665664457524054 0.0679917855968455 0.979036587553508 0.327839484566591 df.mm.trans1:probe14 -0.0426835395253882 0.0679917855968456 -0.627774945910062 0.530319401336344 df.mm.trans1:probe15 -0.00671079399783618 0.0679917855968455 -0.0987000700000373 0.921399602137891 df.mm.trans1:probe16 0.00671630485716352 0.0679917855968455 0.0987811218400348 0.92133526584034 df.mm.trans1:probe17 -0.00424510117329292 0.0679917855968456 -0.062435500642152 0.950230651221626 df.mm.trans1:probe18 0.0685628504137074 0.0679917855968456 1.00839902661548 0.31354889842382 df.mm.trans1:probe19 -0.0538161149679454 0.0679917855968456 -0.791509069743304 0.428867020425839 df.mm.trans1:probe20 -0.0282683021807065 0.0679917855968456 -0.415760550080596 0.677689799698913 df.mm.trans1:probe21 -0.0957149609971104 0.0679917855968456 -1.40774301126092 0.159571492704339 df.mm.trans1:probe22 -0.0644934427264827 0.0679917855968456 -0.948547565861762 0.343119419087818 df.mm.trans2:probe2 0.0369273137072125 0.0679917855968456 0.543114339225792 0.587192969304644 df.mm.trans2:probe3 -0.0352026533610933 0.0679917855968455 -0.517748622897271 0.60476791486503 df.mm.trans2:probe4 0.0692225780311894 0.0679917855968456 1.01810207547191 0.30891812116121 df.mm.trans2:probe5 0.118603873963167 0.0679917855968455 1.74438533893526 0.0814520990562813 . df.mm.trans2:probe6 0.00508234009618451 0.0679917855968456 0.0747493252540834 0.940431687462434 df.mm.trans3:probe2 -0.0919609146283685 0.0679917855968455 -1.35252977725349 0.176564324406910 df.mm.trans3:probe3 -0.0256843596729871 0.0679917855968455 -0.377756804701106 0.705705240972156 df.mm.trans3:probe4 -0.0480840166926669 0.0679917855968455 -0.707203322733411 0.479633288797809 df.mm.trans3:probe5 -0.0437199522764786 0.0679917855968455 -0.643018151276601 0.520385131029035 df.mm.trans3:probe6 -0.0255435458572752 0.0679917855968455 -0.375685763111659 0.707243929322735 df.mm.trans3:probe7 -0.0660922381640447 0.0679917855968455 -0.97206210403027 0.331295212760537 df.mm.trans3:probe8 -0.0483617378324809 0.0679917855968455 -0.711287950565673 0.477100273244279 df.mm.trans3:probe9 -0.0423649004749118 0.0679917855968455 -0.623088511399197 0.53339291136722 df.mm.trans3:probe10 -0.0648502507971712 0.0679917855968455 -0.953795377307753 0.340457432493332