fitVsDatCorrelation=0.849617069993918 cont.fitVsDatCorrelation=0.267569559719750 fstatistic=15108.2119100610,59,853 cont.fstatistic=4516.29803928115,59,853 residuals=-0.514060701048892,-0.0791040779060797,0.000908147677579866,0.0780900307715414,0.77496967404192 cont.residuals=-0.504672651267185,-0.162674630212165,-0.0334031270801529,0.133710324837544,0.946718845716021 predictedValues: Include Exclude Both Lung 70.9549392806681 73.8451544579847 64.9201119685642 cerebhem 67.6821334115759 52.5502605389127 58.325006781384 cortex 64.0294052680562 63.9694325062219 58.1029139225311 heart 68.5687968072382 64.2817025716904 58.3604798317614 kidney 69.8293502092766 69.8062636309728 60.9962098485095 liver 69.938615847008 62.3528087393208 56.6511596884566 stomach 73.9184565948113 57.7696490812544 61.6121920820517 testicle 68.1538490242425 61.9178526311272 54.86507379201 diffExp=-2.89021517731656,15.1318728726632,0.0599727618342953,4.28709423554776,0.0230865783037899,7.58580710768717,16.1488075135569,6.23599639311524 diffExpScore=1.10046630930138 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=0,1,0,0,0,0,1,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 58.4546337272784 59.7486757937829 61.9952146048441 cerebhem 58.714992099854 58.089081494194 57.6100707659476 cortex 62.1136640020135 61.9691191986585 57.4492863300965 heart 64.7796178160764 65.8491374618566 66.2018449674426 kidney 60.884417579093 60.9984982954138 61.0120419366127 liver 60.565950545834 56.6033245982851 60.9411404643419 stomach 58.4089378947823 65.7589118229051 63.4498344779098 testicle 62.1204513795098 57.9614595097202 68.6186248031117 cont.diffExp=-1.29404206650451,0.625910605659911,0.144544803354997,-1.06951964578023,-0.114080716320842,3.96262594754891,-7.3499739281228,4.15899186978952 cont.diffExpScore=9.67154350079218 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=0.0722753444624185 cont.tran.correlation=0.277466734670672 tran.covariance=0.00028542045626322 cont.tran.covariance=0.000549684306262834 tran.mean=66.2230419125226 cont.tran.mean=60.8138045762036 weightedLogRatios: wLogRatio Lung -0.170960767615877 cerebhem 1.03455225880212 cortex 0.00389720994522193 heart 0.270875800415669 kidney 0.00140398353748672 liver 0.481075131614754 stomach 1.03029513665814 testicle 0.400512891768751 cont.weightedLogRatios: wLogRatio Lung -0.0893184127505277 cerebhem 0.0435911742491524 cortex 0.0096170065076752 heart -0.068435369572081 kidney -0.00769364569597102 liver 0.27539037720862 stomach -0.489126194434877 testicle 0.283731353639927 varWeightedLogRatios=0.208885217982093 cont.varWeightedLogRatios=0.0585825366752777 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.67892771088205 0.0613959852402566 76.2090174556582 0 *** df.mm.trans1 -0.16616913502064 0.0530200710571421 -3.13407982500725 0.00178290208623013 ** df.mm.trans2 -0.41699398980769 0.0468429931325383 -8.9019501513885 3.24757119986322e-18 *** df.mm.exp2 -0.280296585810080 0.0602550667540225 -4.65183429228163 3.81199000138954e-06 *** df.mm.exp3 -0.135325972551594 0.0602550667540225 -2.24588536436333 0.0249666081471662 * df.mm.exp4 -0.0663842877207212 0.0602550667540224 -1.10172125427593 0.270893639943393 df.mm.exp5 -0.00989151825320893 0.0602550667540225 -0.164160771634173 0.869643471302796 df.mm.exp6 -0.047343742166615 0.0602550667540224 -0.785722176026873 0.43224846255975 df.mm.exp7 -0.152291634993845 0.0602550667540224 -2.52744944446815 0.0116690435172752 * df.mm.exp8 -0.0481587269141298 0.0602550667540224 -0.799247756387472 0.424369259134731 df.mm.trans1:exp2 0.233073805107986 0.0556950112038588 4.18482374040419 3.14972617299987e-05 *** df.mm.trans2:exp2 -0.0599037524824387 0.0411334918151617 -1.45632548657973 0.145670631992003 df.mm.trans1:exp3 0.0326233901645487 0.0556950112038588 0.585750670650522 0.558198017732837 df.mm.trans2:exp3 -0.00823906880170043 0.0411334918151617 -0.200300738841324 0.841293128829542 df.mm.trans1:exp4 0.0321768443944701 0.0556950112038588 0.577732972827568 0.563596881787093 df.mm.trans2:exp4 -0.0723110784851475 0.0411334918151617 -1.75796109919591 0.0791127593503247 . df.mm.trans1:exp5 -0.00609908779730917 0.0556950112038588 -0.109508691451463 0.912824785043706 df.mm.trans2:exp5 -0.0463551326984626 0.0411334918151617 -1.12694377872817 0.260083169865869 df.mm.trans1:exp6 0.0329166654865797 0.0556950112038588 0.591016408383434 0.554665995064554 df.mm.trans2:exp6 -0.121817932305080 0.0411334918151617 -2.96152665211317 0.00314609017052777 ** df.mm.trans1:exp7 0.193209165168194 0.0556950112038588 3.46905694050399 0.000548454411181221 *** df.mm.trans2:exp7 -0.0932152231701237 0.0411334918151617 -2.26616363106243 0.0236911580663837 * df.mm.trans1:exp8 0.00788134463219288 0.0556950112038588 0.14150898728335 0.887501273459251 df.mm.trans2:exp8 -0.128003117662922 0.0411334918151617 -3.11189524677651 0.00192082600832410 ** df.mm.trans1:probe2 -0.595543514712399 0.0381317674710705 -15.6180411821776 1.49845140837234e-48 *** df.mm.trans1:probe3 -0.712726179479631 0.0381317674710705 -18.6911393504210 1.31440408729497e-65 *** df.mm.trans1:probe4 -0.296865717523472 0.0381317674710705 -7.7852598295816 2.01268510781303e-14 *** df.mm.trans1:probe5 -0.52100906363273 0.0381317674710705 -13.6633861524516 1.40455271603475e-38 *** df.mm.trans1:probe6 0.321457404374941 0.0381317674710705 8.43017320450254 1.46058947528278e-16 *** df.mm.trans1:probe7 -0.62248180002147 0.0381317674710705 -16.3244937569109 2.45011003472988e-52 *** df.mm.trans1:probe8 -0.446950596783496 0.0381317674710705 -11.7212137392421 1.58911749061247e-29 *** df.mm.trans1:probe9 -0.51977208022128 0.0381317674710705 -13.6309464442113 2.02387949515480e-38 *** df.mm.trans1:probe10 -0.239542682945146 0.0381317674710705 -6.28197166907828 5.32322607019742e-10 *** df.mm.trans1:probe11 -0.526827148126074 0.0381317674710705 -13.8159645635562 2.50136254064429e-39 *** df.mm.trans1:probe12 -0.369702050889299 0.0381317674710705 -9.69538197173213 3.71421019789938e-21 *** df.mm.trans1:probe13 -0.466280021999632 0.0381317674710705 -12.2281250758540 8.49086897832445e-32 *** df.mm.trans1:probe14 -0.456943350196194 0.0381317674710705 -11.9832722294571 1.08363272501015e-30 *** df.mm.trans1:probe15 -0.150291749434125 0.0381317674710705 -3.94137904958502 8.76490093300234e-05 *** df.mm.trans1:probe16 -0.225013928085984 0.0381317674710705 -5.90095720731266 5.20991944299929e-09 *** df.mm.trans1:probe17 -0.404623643545977 0.0381317674710705 -10.6111956088307 8.57871065406584e-25 *** df.mm.trans1:probe18 -0.438801591522497 0.0381317674710705 -11.5075072734408 1.37622722379853e-28 *** df.mm.trans1:probe19 -0.472179756070515 0.0381317674710705 -12.3828447351344 1.66797310069505e-32 *** df.mm.trans1:probe20 -0.455047175562476 0.0381317674710705 -11.9335453282544 1.80958881453044e-30 *** df.mm.trans1:probe21 -0.224551013627377 0.0381317674710705 -5.88881734364234 5.59123894447238e-09 *** df.mm.trans1:probe22 -0.199138206219289 0.0381317674710705 -5.22237020275469 2.22100560123507e-07 *** df.mm.trans2:probe2 0.169285734104185 0.0381317674710705 4.43949350715561 1.02007290990683e-05 *** df.mm.trans2:probe3 0.130616007355790 0.0381317674710705 3.42538560413924 0.000643241113878814 *** df.mm.trans2:probe4 0.290343615271956 0.0381317674710705 7.61421865619607 7.0352100671517e-14 *** df.mm.trans2:probe5 0.0257730840073331 0.0381317674710705 0.675895341774714 0.499290295796819 df.mm.trans2:probe6 0.0245683185079956 0.0381317674710705 0.644300543546398 0.519553776011691 df.mm.trans3:probe2 -0.0536753137058434 0.0381317674710705 -1.40762721650827 0.159605785489736 df.mm.trans3:probe3 0.0462791250305985 0.0381317674710705 1.21366325507227 0.225212279574548 df.mm.trans3:probe4 0.161370588900827 0.0381317674710705 4.23191998700964 2.56816015652352e-05 *** df.mm.trans3:probe5 0.0471093779987767 0.0381317674710705 1.23543651719048 0.217008237063588 df.mm.trans3:probe6 -0.0169087338879353 0.0381317674710705 -0.443429062153058 0.657567864326131 df.mm.trans3:probe7 0.225832135028688 0.0381317674710705 5.92241456418248 4.59699886260265e-09 *** df.mm.trans3:probe8 -0.137055773325418 0.0381317674710705 -3.59426752062825 0.00034396102650617 *** df.mm.trans3:probe9 0.0337787952751787 0.0381317674710705 0.88584394365684 0.375951282500475 df.mm.trans3:probe10 0.467198611757201 0.0381317674710705 12.2522149572965 6.59644733612628e-32 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.02730751011617 0.112167975870472 35.9042541230018 9.8073872981149e-173 *** df.mm.trans1 -0.0094991560663577 0.0968655202407072 -0.098065400802604 0.921903399553741 df.mm.trans2 0.0663548685119703 0.0855802493083225 0.775352596519223 0.438346230127849 df.mm.exp2 0.0496346328759696 0.110083564052121 0.450881412709976 0.652189610093385 df.mm.exp3 0.173358807339266 0.110083564052121 1.57479282971966 0.115675066748777 df.mm.exp4 0.134308220033230 0.110083564052121 1.22005697389703 0.222780476820362 df.mm.exp5 0.0774144738774929 0.110083564052121 0.703233716532283 0.482102000851441 df.mm.exp6 -0.00144869436478201 0.110083564052121 -0.0131599515082569 0.989503258164571 df.mm.exp7 0.0718737503248158 0.110083564052121 0.65290173827208 0.513995552761704 df.mm.exp8 -0.0710512248953077 0.110083564052121 -0.645429910514771 0.518822193895871 df.mm.trans1:exp2 -0.0451904981567334 0.101752527439268 -0.444121628169931 0.657067294663394 df.mm.trans2:exp2 -0.077803940886909 0.0751492218804899 -1.03532596798728 0.300810124387193 df.mm.trans1:exp3 -0.112643771911902 0.101752527439268 -1.10703659895952 0.268590188945127 df.mm.trans2:exp3 -0.136869651935819 0.0751492218804898 -1.82130497842657 0.0689106676264045 . df.mm.trans1:exp4 -0.0315681675754786 0.101752527439268 -0.310244554803028 0.756450774783799 df.mm.trans2:exp4 -0.0370889185777417 0.0751492218804898 -0.493536960858016 0.621760274346067 df.mm.trans1:exp5 -0.0366881624291747 0.101752527439268 -0.360562664657861 0.718515670706111 df.mm.trans2:exp5 -0.0567122562340633 0.0751492218804898 -0.754661922172036 0.450660153821048 df.mm.trans1:exp6 0.0369305958026964 0.101752527439268 0.362945243052943 0.71673570522245 df.mm.trans2:exp6 -0.0526306117843977 0.0751492218804898 -0.700348060397703 0.483900936028797 df.mm.trans1:exp7 -0.0726557876045383 0.101752527439268 -0.714044058000461 0.475395271254621 df.mm.trans2:exp7 0.0239744244620984 0.0751492218804898 0.319024254172918 0.749786225821514 df.mm.trans1:exp8 0.131875527853287 0.101752527439268 1.29604179052946 0.195311644501075 df.mm.trans2:exp8 0.0406824952389893 0.0751492218804898 0.541356174035799 0.588403464722533 df.mm.trans1:probe2 0.121283673203507 0.0696651932020631 1.74095079090249 0.0820527601374701 . df.mm.trans1:probe3 0.0698820445019331 0.0696651932020632 1.00311276391987 0.316090925188279 df.mm.trans1:probe4 0.0290772617550742 0.0696651932020631 0.417385790788463 0.676501276034883 df.mm.trans1:probe5 0.0457956551921751 0.0696651932020631 0.657367805746914 0.511121782006748 df.mm.trans1:probe6 0.0865944561873817 0.0696651932020631 1.24300891459836 0.214206103561387 df.mm.trans1:probe7 0.0683579440502697 0.0696651932020631 0.981235261230069 0.326754957368375 df.mm.trans1:probe8 0.106198468312279 0.0696651932020631 1.52441216956439 0.127776432276662 df.mm.trans1:probe9 0.186987611697977 0.0696651932020631 2.68408947285371 0.00741376508359867 ** df.mm.trans1:probe10 0.116335049207669 0.0696651932020631 1.66991640818737 0.0953027884484504 . df.mm.trans1:probe11 0.116050426524752 0.0696651932020631 1.66583082872030 0.0961142764366034 . df.mm.trans1:probe12 0.00945811300109011 0.0696651932020631 0.135765259039144 0.892038888034955 df.mm.trans1:probe13 0.0753791424454478 0.0696651932020631 1.08202014493538 0.279549362653728 df.mm.trans1:probe14 0.122630881585515 0.0696651932020631 1.76028911927115 0.078717153620109 . df.mm.trans1:probe15 -0.0103711623654792 0.0696651932020631 -0.148871507976699 0.88169021123941 df.mm.trans1:probe16 0.0841845798861015 0.0696651932020631 1.20841665710917 0.227221924034737 df.mm.trans1:probe17 0.0575403434478541 0.0696651932020631 0.8259554133577 0.409060334472375 df.mm.trans1:probe18 0.0863596588927977 0.0696651932020631 1.23963854721988 0.215450048721224 df.mm.trans1:probe19 0.0228688639836031 0.0696651932020632 0.328268148446415 0.742789487978772 df.mm.trans1:probe20 0.072434628067461 0.0696651932020631 1.03975349436504 0.298749063026158 df.mm.trans1:probe21 0.0670717635457224 0.0696651932020631 0.962772949630405 0.335934323822932 df.mm.trans1:probe22 0.0800440367892638 0.0696651932020631 1.14898176707982 0.250885573841616 df.mm.trans2:probe2 -0.0354464319845680 0.0696651932020631 -0.508811220572604 0.611016130264134 df.mm.trans2:probe3 0.0286514672773843 0.0696651932020631 0.411273779063255 0.680975100728431 df.mm.trans2:probe4 -0.00347286953777951 0.0696651932020631 -0.0498508563337576 0.960252909733252 df.mm.trans2:probe5 -0.0978385898588325 0.0696651932020631 -1.40441137620982 0.160560393704402 df.mm.trans2:probe6 0.051860815955719 0.0696651932020631 0.74442937099589 0.45682178477956 df.mm.trans3:probe2 0.0896962494736061 0.0696651932020631 1.28753320490253 0.198257719323767 df.mm.trans3:probe3 0.045352245718173 0.0696651932020631 0.651002941836812 0.515219913215565 df.mm.trans3:probe4 0.120200363370798 0.0696651932020631 1.72540056010694 0.0848174900532804 . df.mm.trans3:probe5 0.0149538099926324 0.0696651932020631 0.214652530270878 0.830089540086129 df.mm.trans3:probe6 0.0396841011133056 0.0696651932020631 0.569640293657154 0.569071673673057 df.mm.trans3:probe7 -0.0143584906645351 0.0696651932020631 -0.206107096019794 0.836756437433017 df.mm.trans3:probe8 0.0929999088487377 0.0696651932020631 1.33495515585513 0.182247197097231 df.mm.trans3:probe9 -0.0198285213950845 0.0696651932020631 -0.284625944229741 0.775999816292695 df.mm.trans3:probe10 0.0602400906589126 0.0696651932020631 0.864708585307254 0.387441860727659