fitVsDatCorrelation=0.89927455333802 cont.fitVsDatCorrelation=0.267486680207346 fstatistic=12909.6535985825,49,623 cont.fstatistic=2649.91142882476,49,623 residuals=-0.542987347809922,-0.0824352191911881,0.00401002333343743,0.0812929791581474,0.425508463556914 cont.residuals=-0.719173217535628,-0.230195300583908,-0.00543116385695516,0.223248483083675,0.824783202900631 predictedValues: Include Exclude Both Lung 80.2695674137338 81.5276502845035 121.297339221925 cerebhem 75.9853700552754 79.2424294706919 120.526156483393 cortex 79.2723999847798 75.0820715517017 107.887146047634 heart 82.8580075703783 80.704427278109 129.340197641740 kidney 66.7625804178788 80.4088682259485 91.9462301352385 liver 66.053803475307 73.4589410677985 77.9054226353879 stomach 75.6511403591682 85.7277427913774 113.6951266097 testicle 74.6309737981986 75.0295523296953 105.111534142009 diffExp=-1.25808287076968,-3.25705941541656,4.19032843307808,2.15358029226938,-13.6462878080697,-7.40513759249149,-10.0766024322093,-0.398578531496639 diffExpScore=1.38073745511769 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,-1,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 82.4849191905906 79.354919472584 89.635131911247 cerebhem 80.2771019346123 81.9083978203874 86.2813978911254 cortex 85.8445928950704 77.284646890765 69.3846380770679 heart 79.228828302386 88.246598461922 70.9265046756853 kidney 80.4240539259288 82.5555297518882 70.586357663266 liver 79.9809200277126 81.4448983424971 80.892113118087 stomach 74.5145512409181 85.662388206474 88.2905161064357 testicle 79.6644197829832 70.8688848662168 95.4982517734173 cont.diffExp=3.12999971800653,-1.63129588577516,8.5599460043054,-9.01777015953604,-2.13147582595933,-1.46397831478453,-11.1478369655560,8.79553491676641 cont.diffExpScore=7.76685236153995 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=0.310714049318372 cont.tran.correlation=-0.444889486100720 tran.covariance=0.00137420987189289 cont.tran.covariance=-0.00113869246656475 tran.mean=77.0415953796591 cont.tran.mean=80.6091031945585 weightedLogRatios: wLogRatio Lung -0.068321060936365 cerebhem -0.182638167192715 cortex 0.236009181449422 heart 0.115978116467643 kidney -0.798630360079192 liver -0.45091205251445 stomach -0.548774820732297 testicle -0.0229847869777384 cont.weightedLogRatios: wLogRatio Lung 0.169953925961158 cerebhem -0.0884255744063974 cortex 0.462192229075739 heart -0.477125957107659 kidney -0.115104778816525 liver -0.0796439668213828 stomach -0.610755260316393 testicle 0.505324928277055 varWeightedLogRatios=0.125724593150446 cont.varWeightedLogRatios=0.159874512181370 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.85508823081547 0.0766266258044502 50.3100350608362 1.36606442135117e-221 *** df.mm.trans1 0.303391314605172 0.0685697540215031 4.42456472149557 1.14067717570416e-05 *** df.mm.trans2 0.5911878127218 0.0633988260206717 9.32490157671119 1.90767699357677e-19 *** df.mm.exp2 -0.0769020049321466 0.0869418920494898 -0.884521869944719 0.376755794182658 df.mm.exp3 0.0222981754812213 0.0869418920494898 0.256472167278445 0.797670938042769 df.mm.exp4 -0.0426122115558148 0.0869418920494898 -0.4901228918685 0.624219494375953 df.mm.exp5 0.0789753576862748 0.0869418920494898 0.908369438766295 0.364034312560956 df.mm.exp6 0.143612765838102 0.0869418920494898 1.65182471249134 0.0990741843163221 . df.mm.exp7 0.0557005565330158 0.0869418920494898 0.640664186389105 0.521976461634268 df.mm.exp8 -0.0126722427179890 0.0869418920494898 -0.145755313339346 0.884161699801962 df.mm.trans1:exp2 0.0220522644264378 0.0826982349295597 0.266659432879328 0.789819627715425 df.mm.trans2:exp2 0.0484716559396228 0.0726582863339774 0.667118072628639 0.504943723433125 df.mm.trans1:exp3 -0.0347987160004853 0.0826982349295597 -0.420791520280039 0.674052438229843 df.mm.trans2:exp3 -0.104658602888069 0.0726582863339774 -1.44042212070624 0.150250354294657 df.mm.trans1:exp4 0.0743500391917469 0.0826982349295597 0.899052310549027 0.368972147102594 df.mm.trans2:exp4 0.0324634162668173 0.0726582863339775 0.446795787580202 0.655177785942381 df.mm.trans1:exp5 -0.263223170473435 0.0826982349295597 -3.1829357748402 0.00153049498561589 ** df.mm.trans2:exp5 -0.0927931162695337 0.0726582863339774 -1.27711677430714 0.202036785645691 df.mm.trans1:exp6 -0.338533714727789 0.0826982349295597 -4.09360266293645 4.80651960522268e-05 *** df.mm.trans2:exp6 -0.247828370639464 0.0726582863339774 -3.41087552630003 0.000689424301974619 *** df.mm.trans1:exp7 -0.114958605254942 0.0826982349295597 -1.39009744709619 0.164995740255413 df.mm.trans2:exp7 -0.00546629346574003 0.0726582863339774 -0.075232898290691 0.940053560418628 df.mm.trans1:exp8 -0.060162700864997 0.0826982349295597 -0.72749679501917 0.467194978426985 df.mm.trans2:exp8 -0.0703879202961535 0.0726582863339775 -0.968752827070705 0.333044458495344 df.mm.trans1:probe2 -0.127709847959129 0.0413491174647799 -3.08857493918484 0.00210068087062229 ** df.mm.trans1:probe3 -0.104096356525017 0.0413491174647798 -2.51749887077235 0.0120688555651835 * df.mm.trans1:probe4 -0.0728784352865047 0.0413491174647799 -1.76251489160756 0.078472640198678 . df.mm.trans1:probe5 -0.0747430308250846 0.0413491174647799 -1.80760885377418 0.0711495893300246 . df.mm.trans1:probe6 -0.0477035769788139 0.0413491174647799 -1.15367823798045 0.249074503957512 df.mm.trans1:probe7 -0.114796847578109 0.0413491174647799 -2.77628289590194 0.0056636627981486 ** df.mm.trans1:probe8 0.458854185215781 0.0413491174647799 11.0970732472494 3.09642728348375e-26 *** df.mm.trans1:probe9 0.711020183902662 0.0413491174647799 17.1955346932928 1.60565538242116e-54 *** df.mm.trans1:probe10 0.619573944191058 0.0413491174647799 14.9839702073157 1.43047220969757e-43 *** df.mm.trans1:probe11 0.723857780432785 0.0413491174647798 17.5060031462425 4.19942353789729e-56 *** df.mm.trans1:probe12 0.644671806020453 0.0413491174647799 15.59094475401 1.63212160868407e-46 *** df.mm.trans1:probe13 0.523561651702784 0.0413491174647798 12.6619788717073 7.3503298573757e-33 *** df.mm.trans1:probe14 0.27687887897479 0.0413491174647798 6.69612547863032 4.78425072071498e-11 *** df.mm.trans1:probe15 0.39404269467049 0.0413491174647799 9.52965187240394 3.4579913308605e-20 *** df.mm.trans1:probe16 0.437050511263733 0.0413491174647799 10.5697663713380 3.94274423208222e-24 *** df.mm.trans1:probe17 0.269429093882323 0.0413491174647799 6.51595754399875 1.49090170036074e-10 *** df.mm.trans1:probe18 0.366923624200791 0.0413491174647799 8.87379578326739 7.45552218282808e-18 *** df.mm.trans1:probe19 0.108106127198628 0.0413491174647799 2.61447241989409 0.00915267388254637 ** df.mm.trans2:probe2 0.0936468140317396 0.0413491174647798 2.26478386416604 0.0238686581687669 * df.mm.trans2:probe3 -0.106898650392512 0.0413491174647799 -2.58527042284677 0.00995630657140305 ** df.mm.trans2:probe4 -0.104586738192676 0.0413491174647799 -2.52935841452385 0.0116726405735412 * df.mm.trans2:probe5 -0.163353949480897 0.0413491174647798 -3.95060304781687 8.68671583036688e-05 *** df.mm.trans2:probe6 -0.126811797772209 0.0413491174647799 -3.06685621235384 0.00225698954948629 ** df.mm.trans3:probe2 0.179969872143272 0.0413491174647798 4.3524477226525 1.5733086409774e-05 *** df.mm.trans3:probe3 -0.0342373059899407 0.0413491174647799 -0.828005725130728 0.407984325712671 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.34965747015891 0.168809036678352 25.7667335573198 3.19763416115613e-100 *** df.mm.trans1 0.0240028029592544 0.151059687153408 0.158896151657447 0.873802187455008 df.mm.trans2 0.0800316802769231 0.139668093625838 0.573013336111846 0.566842417643781 df.mm.exp2 0.0426733429401927 0.191533646298377 0.222798154605770 0.823765629746146 df.mm.exp3 0.269569850437535 0.191533646298377 1.40742817592263 0.159799179872485 df.mm.exp4 0.300032600770673 0.191533646298377 1.56647464593909 0.117745304175224 df.mm.exp5 0.253148992076409 0.191533646298377 1.32169463156383 0.186754980093631 df.mm.exp6 0.0977998933960506 0.191533646298377 0.510614689826847 0.609801763729136 df.mm.exp7 -0.0100230004016299 0.191533646298377 -0.0523302333315149 0.958282338123057 df.mm.exp8 -0.211251984969874 0.191533646298377 -1.10294973782716 0.270474857846405 df.mm.trans1:exp2 -0.0698043980741512 0.182184837540481 -0.383151523565405 0.701738118864632 df.mm.trans2:exp2 -0.0110022620329989 0.160066754786249 -0.0687354600753365 0.94522224684631 df.mm.trans1:exp3 -0.229646727186361 0.182184837540481 -1.26051503674302 0.207955520523235 df.mm.trans2:exp3 -0.296004973951652 0.160066754786249 -1.84925954391299 0.0648938382493898 . df.mm.trans1:exp4 -0.34030785340423 0.182184837540481 -1.86792632141308 0.0622415828661882 . df.mm.trans2:exp4 -0.193827892225635 0.160066754786249 -1.21091910987057 0.226385565076415 df.mm.trans1:exp5 -0.278451161394453 0.182184837540481 -1.52839920793398 0.126920992056227 df.mm.trans2:exp5 -0.213608279440855 0.160066754786249 -1.33449497196407 0.182529220836487 df.mm.trans1:exp6 -0.128627265762617 0.182184837540481 -0.70602618472043 0.480435782564225 df.mm.trans2:exp6 -0.0718036379456 0.160066754786249 -0.448585579444562 0.653886616431924 df.mm.trans1:exp7 -0.091598053556313 0.182184837540481 -0.502775394444996 0.615299876596037 df.mm.trans2:exp7 0.086506410081763 0.160066754786249 0.540439582206078 0.589087065250064 df.mm.trans1:exp8 0.176459565333376 0.182184837540481 0.968574375977732 0.333133452393458 df.mm.trans2:exp8 0.0981530205635136 0.160066754786249 0.613200540578122 0.539967540340502 df.mm.trans1:probe2 0.0382491206159269 0.0910924187702406 0.419893566690784 0.674707973779553 df.mm.trans1:probe3 0.131000910665796 0.0910924187702406 1.43810991555967 0.150904990453062 df.mm.trans1:probe4 0.0666862176620194 0.0910924187702406 0.732072092961105 0.464399865203044 df.mm.trans1:probe5 0.146177186936236 0.0910924187702406 1.60471298171293 0.109063753910881 df.mm.trans1:probe6 0.0815442289997935 0.0910924187702406 0.895181290612887 0.3710359083875 df.mm.trans1:probe7 -0.0861081976761854 0.0910924187702406 -0.945283908789086 0.344880415586025 df.mm.trans1:probe8 -0.0372858491095537 0.0910924187702406 -0.409318905051787 0.682446327601814 df.mm.trans1:probe9 0.0982548233273408 0.0910924187702406 1.07862788861898 0.281171078420327 df.mm.trans1:probe10 0.0998617852545905 0.0910924187702406 1.09626889485138 0.273384697734591 df.mm.trans1:probe11 0.000771520416431883 0.0910924187702406 0.00846964464054757 0.993244993450325 df.mm.trans1:probe12 0.102455466910065 0.0910924187702406 1.12474197406575 0.261131536491437 df.mm.trans1:probe13 0.0226789438309838 0.0910924187702406 0.248966315058404 0.8034688917715 df.mm.trans1:probe14 0.0266982390111265 0.0910924187702406 0.293089582772707 0.769551276034282 df.mm.trans1:probe15 -0.0121983677377124 0.0910924187702406 -0.133911997314287 0.893515409464246 df.mm.trans1:probe16 0.110288212860767 0.0910924187702406 1.21072877797814 0.226458474312564 df.mm.trans1:probe17 0.0758944073170828 0.0910924187702406 0.833158328010905 0.405074732592509 df.mm.trans1:probe18 0.0235547973348373 0.0910924187702406 0.258581313931829 0.796043708085533 df.mm.trans1:probe19 -0.0315089185108735 0.0910924187702406 -0.345900558314819 0.729534223389936 df.mm.trans2:probe2 -0.150785623468225 0.0910924187702406 -1.65530376187009 0.0983665078822236 . df.mm.trans2:probe3 -0.095779067480827 0.0910924187702406 -1.05144938265837 0.293459906302295 df.mm.trans2:probe4 -0.150210208679984 0.0910924187702406 -1.64898693774785 0.0996544296631919 . df.mm.trans2:probe5 -0.088381278755025 0.0910924187702406 -0.970237479124867 0.332304656167707 df.mm.trans2:probe6 -0.0166721958762446 0.0910924187702406 -0.183025065107738 0.854837897173476 df.mm.trans3:probe2 -0.00520126622170981 0.0910924187702406 -0.0570987826641072 0.954484813972632 df.mm.trans3:probe3 0.131367427100234 0.0910924187702406 1.44213348238757 0.149767229673037