fitVsDatCorrelation=0.891644540413298 cont.fitVsDatCorrelation=0.200009543442581 fstatistic=9767.57987740549,53,715 cont.fstatistic=2074.87830231534,53,715 residuals=-0.82385619974643,-0.0922580088617255,0.00184469491750313,0.087306876431698,0.851179584271597 cont.residuals=-0.598251002599285,-0.258426920850841,-0.0437741110505055,0.209698720629292,1.26586201026699 predictedValues: Include Exclude Both Lung 52.3962483750435 56.2247287726438 71.3127441719035 cerebhem 47.1547584763707 47.3258420931385 61.9468669452393 cortex 52.6257889649705 54.6751775852089 64.37345831532 heart 54.9060109356974 64.4161834468233 68.8685240905777 kidney 51.7224516655271 59.110734521584 74.0070960553303 liver 53.4932696686104 58.1569209662136 76.4723005434072 stomach 56.6576888236152 60.8797076364943 73.3335973604344 testicle 56.0489465769905 59.0184976612531 86.7450632434556 diffExp=-3.8284803976003,-0.171083616767824,-2.04938862023842,-9.51017251112584,-7.38828285605695,-4.66365129760322,-4.22201881287913,-2.96955108426258 diffExpScore=0.972069090386893 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 63.7757749946033 64.5092777947012 66.5763992615532 cerebhem 64.2996609724606 63.4671463568813 59.3119418137272 cortex 59.4233334594213 63.0474047656585 57.6712761032309 heart 63.1110955986018 65.0726320508856 68.4170273765345 kidney 61.498804783666 64.9778865162355 61.6067384088045 liver 64.9604437229861 59.8095788682142 66.0310730511505 stomach 61.9132638321065 69.0396008242254 66.2582260660023 testicle 63.8588878353885 60.987308069243 68.4238020240906 cont.diffExp=-0.733502800097924,0.832514615579292,-3.62407130623718,-1.96153645228378,-3.47908173256958,5.15086485477184,-7.12633699211896,2.87157976614544 cont.diffExpScore=2.84241572497352 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=0.850185412386355 cont.tran.correlation=-0.411493342069915 tran.covariance=0.00458750235996285 cont.tran.covariance=-0.000520581553978291 tran.mean=55.3008097606365 cont.tran.mean=63.3595062778299 weightedLogRatios: wLogRatio Lung -0.281670352253551 cerebhem -0.0139620217535464 cortex -0.152138226918540 heart -0.652625230378858 kidney -0.535771678281322 liver -0.336140839610750 stomach -0.292732901706946 testicle -0.209188735633670 cont.weightedLogRatios: wLogRatio Lung -0.0475847383364314 cerebhem 0.0541743862373241 cortex -0.243565505164002 heart -0.127333192742056 kidney -0.228180626088208 liver 0.341394670553359 stomach -0.455416158618272 testicle 0.190190164982084 varWeightedLogRatios=0.0418380768117819 cont.varWeightedLogRatios=0.065658077506201 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.75005140533993 0.0753393373481378 49.7754763625171 1.63064753804100e-234 *** df.mm.trans1 0.053048752816338 0.0643252647420794 0.824695444768769 0.409819555295274 df.mm.trans2 0.212726248194890 0.0579435477180269 3.671267234621 0.000259330793048171 *** df.mm.exp2 -0.136902231176940 0.0750725436571474 -1.82359920828266 0.0686300282041986 . df.mm.exp3 0.078798073322899 0.0750725436571474 1.04962572845228 0.294244866288514 df.mm.exp4 0.217671874521362 0.0750725436571474 2.89948713494322 0.00385231765234875 ** df.mm.exp5 2.69001517333473e-05 0.0750725436571474 0.000358322103167291 0.999714200299734 df.mm.exp6 -0.0153444695354801 0.0750725436571475 -0.204395226110327 0.838102826492969 df.mm.exp7 0.129792141358207 0.0750725436571474 1.72888961843309 0.0842605135772673 . df.mm.exp8 -0.0800138550540707 0.0750725436571474 -1.06582048717425 0.286864565959324 df.mm.trans1:exp2 0.031502164382678 0.0675705684483178 0.466211327003603 0.641206241112639 df.mm.trans2:exp2 -0.0353979519232514 0.0527472580887983 -0.671086103919564 0.502382410167721 df.mm.trans1:exp3 -0.0744267820920426 0.0675705684483178 -1.10146745544947 0.271063909789413 df.mm.trans2:exp3 -0.106744932620642 0.0527472580887983 -2.02370580933213 0.0433718963638764 * df.mm.trans1:exp4 -0.170884036056593 0.0675705684483178 -2.52897141434137 0.0116536625698741 * df.mm.trans2:exp4 -0.0816636510925142 0.0527472580887983 -1.54820656184699 0.122015056394286 df.mm.trans1:exp5 -0.0129699375802499 0.0675705684483178 -0.191946551258779 0.847838571773247 df.mm.trans2:exp5 0.0500289670878445 0.0527472580887983 0.948465738325626 0.343212860872977 df.mm.trans1:exp6 0.0360653220693903 0.0675705684483179 0.53374306147765 0.593685198659186 df.mm.trans2:exp6 0.0491326867432555 0.0527472580887983 0.931473758513517 0.351922956490535 df.mm.trans1:exp7 -0.0515994309200222 0.0675705684483178 -0.763637662150032 0.445335100192357 df.mm.trans2:exp7 -0.0502489039778892 0.0527472580887983 -0.952635374777145 0.341096774440679 df.mm.trans1:exp8 0.147404217183347 0.0675705684483178 2.18148552792021 0.0294725290487092 * df.mm.trans2:exp8 0.128508095634925 0.0527472580887983 2.43629906636258 0.0150814973324781 * df.mm.trans1:probe2 -0.0513832750827834 0.0462624057032382 -1.11069180907699 0.267074398339109 df.mm.trans1:probe3 0.0200290194702627 0.0462624057032381 0.432943751320325 0.665186199063144 df.mm.trans1:probe4 0.0090290913844795 0.0462624057032382 0.195171246441417 0.845314334929839 df.mm.trans1:probe5 0.446758238204307 0.0462624057032382 9.65704725928327 8.0207054902781e-21 *** df.mm.trans1:probe6 0.634735574260253 0.0462624057032382 13.7203321922324 3.36039712125684e-38 *** df.mm.trans1:probe7 0.435577470677145 0.0462624057032382 9.4153657609435 6.3346544282574e-20 *** df.mm.trans1:probe8 0.73698467265623 0.0462624057032382 15.9305306642245 4.00361066673660e-49 *** df.mm.trans1:probe9 0.528179610461512 0.0462624057032381 11.4170372775176 7.52333685218913e-28 *** df.mm.trans1:probe10 0.804723789498467 0.0462624057032381 17.3947674632523 8.8215428407841e-57 *** df.mm.trans1:probe11 0.0395874158039263 0.0462624057032382 0.855714595947943 0.392442328197175 df.mm.trans1:probe12 0.175027487586351 0.0462624057032382 3.78336329306152 0.00016767452118905 *** df.mm.trans1:probe13 0.177825835158263 0.0462624057032382 3.8438518804874 0.000131902750073340 *** df.mm.trans1:probe14 0.0690842175517647 0.0462624057032381 1.49331225866036 0.135796598859294 df.mm.trans1:probe15 -0.00931127798846433 0.0462624057032382 -0.20127094228938 0.840543965847197 df.mm.trans1:probe16 0.0322578328573311 0.0462624057032382 0.697279624070072 0.485854472634397 df.mm.trans2:probe2 0.102901447359162 0.0462624057032382 2.22429953209198 0.02643979969304 * df.mm.trans2:probe3 0.0718664255415627 0.0462624057032382 1.55345197572664 0.120757691746336 df.mm.trans2:probe4 0.234441920264903 0.0462624057032381 5.06765518786008 5.13324820106007e-07 *** df.mm.trans2:probe5 0.319438156693564 0.0462624057032382 6.90491883934183 1.11029462402860e-11 *** df.mm.trans2:probe6 0.336616375505193 0.0462624057032382 7.27624018656753 9.05163431180705e-13 *** df.mm.trans3:probe2 0.933357508327677 0.0462624057032382 20.1752912357160 5.36861401735033e-72 *** df.mm.trans3:probe3 0.0826183940942156 0.0462624057032381 1.78586463108279 0.07454487806119 . df.mm.trans3:probe4 -0.231247244501769 0.0462624057032382 -4.99859964017354 7.26993885972031e-07 *** df.mm.trans3:probe5 -0.176264408924496 0.0462624057032382 -3.81010036648738 0.000150861077666639 *** df.mm.trans3:probe6 0.496812374174847 0.0462624057032382 10.7390086317987 4.81484862798507e-25 *** df.mm.trans3:probe7 0.319884880052867 0.0462624057032382 6.91457513266493 1.04166718099078e-11 *** df.mm.trans3:probe8 0.445500794174472 0.0462624057032382 9.62986657097447 1.01392157579273e-20 *** df.mm.trans3:probe9 0.198358697050672 0.0462624057032382 4.28768660071622 2.05328629481468e-05 *** df.mm.trans3:probe10 0.443465457910244 0.0462624057032382 9.58587110136393 1.48016875667582e-20 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.10081803065508 0.163046463798122 25.1512233698767 1.72330763661354e-100 *** df.mm.trans1 0.0495955285591033 0.139210236222409 0.356263518437444 0.721748325402877 df.mm.trans2 0.0720775175239769 0.125399172436118 0.574784634728711 0.5656177655023 df.mm.exp2 0.107433763156205 0.162469079268197 0.661256675055433 0.508660740186174 df.mm.exp3 0.0499823114915479 0.162469079268197 0.307641993890045 0.758444352251807 df.mm.exp4 -0.0290533959960106 0.162469079268197 -0.178824156121736 0.858126435268153 df.mm.exp5 0.0484611685704302 0.162469079268197 0.298279332834974 0.765576635470406 df.mm.exp6 -0.0490134005262496 0.162469079268197 -0.301678330098372 0.762985009979595 df.mm.exp7 0.0430227539837514 0.162469079268197 0.264805796755525 0.791235389256478 df.mm.exp8 -0.082211519252969 0.162469079268197 -0.506013326494317 0.613003266761576 df.mm.trans1:exp2 -0.0992528204832057 0.146233596287391 -0.678727891558826 0.497529912321149 df.mm.trans2:exp2 -0.123720426587279 0.114153564514170 -1.08380694999605 0.278815732267423 df.mm.trans1:exp3 -0.120668759040664 0.146233596287391 -0.825178085639878 0.409545711552789 df.mm.trans2:exp3 -0.0729044666916006 0.114153564514170 -0.63865256421801 0.523253493423299 df.mm.trans1:exp4 0.0185765756174742 0.146233596287391 0.127033568817975 0.898949545784286 df.mm.trans2:exp4 0.037748402978555 0.114153564514170 0.330680895854718 0.740982411817785 df.mm.trans1:exp5 -0.0848168443253807 0.146233596287391 -0.580009289785167 0.562091077603019 df.mm.trans2:exp5 -0.0412232192368081 0.114153564514170 -0.361120736021266 0.718115866863563 df.mm.trans1:exp6 0.0674185111006998 0.146233596287391 0.461032982928238 0.644915145647753 df.mm.trans2:exp6 -0.0266298248379812 0.114153564514170 -0.233280712269617 0.815610203131941 df.mm.trans1:exp7 -0.0726617348279603 0.146233596287391 -0.496888106924204 0.619420700086346 df.mm.trans2:exp7 0.0248484557345201 0.114153564514170 0.217675688361318 0.827743911639848 df.mm.trans1:exp8 0.0835138747524831 0.146233596287391 0.571099096738031 0.568111925754511 df.mm.trans2:exp8 0.0260682421034288 0.114153564514170 0.228361174829481 0.819430763608863 df.mm.trans1:probe2 0.0932774516867972 0.100119299189635 0.931663050398718 0.351825159289792 df.mm.trans1:probe3 0.111549232961262 0.100119299189635 1.11416314201299 0.265583586950953 df.mm.trans1:probe4 -0.0767162833837453 0.100119299189635 -0.766248705341392 0.443781227533583 df.mm.trans1:probe5 0.0175078469893885 0.100119299189635 0.174869851578037 0.861231405565338 df.mm.trans1:probe6 0.0566581417537682 0.100119299189635 0.565906295912565 0.57163503652629 df.mm.trans1:probe7 -0.036692552197391 0.100119299189635 -0.366488304396657 0.71410916006094 df.mm.trans1:probe8 -0.0792290014250691 0.100119299189635 -0.791345944951155 0.429004573044295 df.mm.trans1:probe9 -0.0139991813632073 0.100119299189635 -0.139825003535948 0.888837634637834 df.mm.trans1:probe10 -0.0272026792391227 0.100119299189635 -0.271702653327591 0.785929092296348 df.mm.trans1:probe11 0.0253411635622122 0.100119299189635 0.253109677827586 0.800256214951586 df.mm.trans1:probe12 -0.0713251546743274 0.100119299189635 -0.712401657339122 0.476448490785369 df.mm.trans1:probe13 0.081778257178555 0.100119299189635 0.81680812630999 0.414310150861943 df.mm.trans1:probe14 0.0481995783027073 0.100119299189635 0.481421451137138 0.63036429561735 df.mm.trans1:probe15 -0.0164292073436645 0.100119299189635 -0.164096307871134 0.869701703732134 df.mm.trans1:probe16 0.0162386627555488 0.100119299189635 0.162193132462817 0.871199591774381 df.mm.trans2:probe2 0.0446695748336874 0.100119299189635 0.446163478922073 0.655614242634353 df.mm.trans2:probe3 -0.00121542508038078 0.100119299189635 -0.0121397681587708 0.990317490740162 df.mm.trans2:probe4 -0.0801370631660806 0.100119299189635 -0.80041574216669 0.423735829368475 df.mm.trans2:probe5 -0.0115478619552067 0.100119299189635 -0.115341018651499 0.908207211212191 df.mm.trans2:probe6 -0.0491531117240982 0.100119299189635 -0.4909454233294 0.623615694677629 df.mm.trans3:probe2 -0.107302914063946 0.100119299189635 -1.07175055091730 0.284193705976753 df.mm.trans3:probe3 -0.095049958783113 0.100119299189635 -0.949367000692642 0.342754760534428 df.mm.trans3:probe4 0.00174579161832776 0.100119299189635 0.0174371138477614 0.98609276558575 df.mm.trans3:probe5 -0.00498851325113277 0.100119299189635 -0.0498256908658947 0.960275208938087 df.mm.trans3:probe6 -0.0885929536312076 0.100119299189635 -0.884873888933287 0.376522121319708 df.mm.trans3:probe7 0.0333406951910722 0.100119299189635 0.333009674068153 0.73922460106354 df.mm.trans3:probe8 0.0103022647395790 0.100119299189635 0.102899888662481 0.91807127882861 df.mm.trans3:probe9 -0.00867347880017142 0.100119299189635 -0.0866314373989282 0.930988730806243 df.mm.trans3:probe10 0.0178097926182614 0.100119299189635 0.177885709972140 0.858863117993583