fitVsDatCorrelation=0.82732499674426 cont.fitVsDatCorrelation=0.258194491228957 fstatistic=9522.90858909078,54,738 cont.fstatistic=3210.36927555795,54,738 residuals=-0.524875301148999,-0.101464526154484,-0.00593048664557265,0.0811012296998458,1.18733866768236 cont.residuals=-0.635202517362909,-0.209687509883284,-0.0267855719124914,0.188673570827125,1.3289732682036 predictedValues: Include Exclude Both Lung 68.227251996079 86.281023087045 78.5617474706657 cerebhem 67.9387768359156 98.3095749466154 60.5468807121789 cortex 66.5923965512559 75.8098505381182 73.6860832379459 heart 72.3894749567308 72.1434568947086 95.2706804360844 kidney 67.2896551573205 94.2401223097339 67.3532272747151 liver 66.9731288301221 81.2422120116117 63.5399427125119 stomach 64.904776682685 75.7438128112418 67.1579284575403 testicle 67.4012499967443 77.5181143067665 68.9000037209474 diffExp=-18.0537710909660,-30.3707981106998,-9.21745398686231,0.246018062022216,-26.9504671524134,-14.2690831814896,-10.8390361285568,-10.1168643100223 diffExpScore=0.995787030419695 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,-1,0,0,-1,0,0,0 diffExp1.4Score=0.666666666666667 diffExp1.3=0,-1,0,0,-1,0,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=-1,-1,0,0,-1,-1,0,0 diffExp1.2Score=0.8 cont.predictedValues: Include Exclude Both Lung 67.2708239993369 72.679143663524 79.6504752106379 cerebhem 69.6112774897725 67.9501325847761 67.5700053917872 cortex 64.951508583673 83.4197506516344 80.4842939577959 heart 70.5011782848098 83.902908107758 65.5896051190315 kidney 71.4308810281349 75.930727871619 70.8534614485789 liver 70.9798327736699 66.0624140522586 72.693062069073 stomach 71.5878653776002 74.8091274711571 75.8902535832123 testicle 73.0753674481032 84.4309618508214 72.2420969324902 cont.diffExp=-5.40831966418708,1.66114490499638,-18.4682420679613,-13.4017298229482,-4.49984684348414,4.91741872141131,-3.22126209355697,-11.3555944027182 cont.diffExpScore=1.23942460998376 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,-1,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=-0.133491212300611 cont.tran.correlation=-0.0528988871714611 tran.covariance=-0.000485024631428091 cont.tran.covariance=-0.000241642551448466 tran.mean=75.1878048695434 cont.tran.mean=73.0371188274156 weightedLogRatios: wLogRatio Lung -1.01893598763831 cerebhem -1.62710678079858 cortex -0.552699147387727 heart 0.0145717182797234 kidney -1.47449009759643 liver -0.830683722732269 stomach -0.656372511700311 testicle -0.598631950454516 cont.weightedLogRatios: wLogRatio Lung -0.328441536503793 cerebhem 0.102185592970154 cortex -1.07574021568996 heart -0.755754180905307 kidney -0.262647775748020 liver 0.30344491727081 stomach -0.188950519374243 testicle -0.630307070779625 varWeightedLogRatios=0.279851104908176 cont.varWeightedLogRatios=0.205033950186402 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.22233415878700 0.0831877082691707 50.7567072905143 6.50507904758953e-243 *** df.mm.trans1 -0.00739592022341872 0.073280963952367 -0.100925531332067 0.919636987468962 df.mm.trans2 0.136631101162559 0.0661236515606733 2.06629697449769 0.0391488180021487 * df.mm.exp2 0.386741587918486 0.0880570217729485 4.39194490265278 1.28778009645006e-05 *** df.mm.exp3 -0.0895641538756119 0.0880570217729485 -1.01711541081357 0.309431851906142 df.mm.exp4 -0.312573445058984 0.0880570217729485 -3.54967086968876 0.000410153984517031 *** df.mm.exp5 0.228332864494118 0.0880570217729485 2.59301143619035 0.00970240593804678 ** df.mm.exp6 0.133488867859099 0.0880570217729485 1.51593666435023 0.129963555281093 df.mm.exp7 -0.0233378492506135 0.0880570217729485 -0.265031098948466 0.791059511532372 df.mm.exp8 0.0119501276507942 0.0880570217729485 0.135708969145097 0.892088363985375 df.mm.trans1:exp2 -0.390978703360924 0.0830591005525807 -4.70723497798311 2.99902555655971e-06 *** df.mm.trans2:exp2 -0.256229839356732 0.0679574459066569 -3.7704454006214 0.000175986182667009 *** df.mm.trans1:exp3 0.0653104845873126 0.0830591005525807 0.786313410003371 0.431936300540357 df.mm.trans2:exp3 -0.0398172867956572 0.0679574459066569 -0.585914998193846 0.558111774900257 df.mm.trans1:exp4 0.371790285936921 0.0830591005525807 4.47621372568992 8.79867756604895e-06 *** df.mm.trans2:exp4 0.133620359431476 0.0679574459066569 1.96623574722056 0.0496458075737414 * df.mm.trans1:exp5 -0.242170426451208 0.0830591005525807 -2.91563988581722 0.00365716738391539 ** df.mm.trans2:exp5 -0.140096525942971 0.0679574459066569 -2.06153312670698 0.0396018926211638 * df.mm.trans1:exp6 -0.152041465572248 0.0830591005525807 -1.83052145473208 0.0675751182134712 . df.mm.trans2:exp6 -0.193663582613144 0.067957445906657 -2.84977723970307 0.00449692195305595 ** df.mm.trans1:exp7 -0.0265850035101943 0.0830591005525807 -0.320073337338449 0.749003332193214 df.mm.trans2:exp7 -0.106915067960337 0.0679574459066569 -1.57326495329431 0.116086064539051 df.mm.trans1:exp8 -0.0241306383789086 0.0830591005525807 -0.290523714058674 0.771497230238284 df.mm.trans2:exp8 -0.119048164817257 0.0679574459066569 -1.75180457754071 0.0802227592321441 . df.mm.trans1:probe2 0.31984353807354 0.0484960665623924 6.59524701167356 8.09307847228537e-11 *** df.mm.trans1:probe3 -0.266514163503208 0.0484960665623924 -5.49558309353451 5.36601562822949e-08 *** df.mm.trans1:probe4 -0.199506118823808 0.0484960665623924 -4.11386186479958 4.32895775121804e-05 *** df.mm.trans1:probe5 0.205085433319200 0.0484960665623924 4.22890860757435 2.64334346684819e-05 *** df.mm.trans1:probe6 -0.252393570989373 0.0484960665623924 -5.20441324173491 2.52489250822790e-07 *** df.mm.trans1:probe7 0.524803278723222 0.0484960665623924 10.8215638076139 1.96449368276441e-25 *** df.mm.trans1:probe8 -0.00939222130292898 0.0484960665623924 -0.193669754449992 0.84648776719314 df.mm.trans1:probe9 -0.178433533829917 0.0484960665623924 -3.67934033578485 0.000250846390381261 *** df.mm.trans1:probe10 -0.263635229385627 0.0484960665623924 -5.43621881264222 7.40200620505265e-08 *** df.mm.trans1:probe11 0.0621539557427924 0.0484960665623924 1.28162880308712 0.200375401356122 df.mm.trans1:probe12 0.0542975345859976 0.0484960665623924 1.11962759940832 0.263236742147867 df.mm.trans1:probe13 0.287719496433767 0.0484960665623924 5.93284191540778 4.57618937721688e-09 *** df.mm.trans1:probe14 0.355468518187083 0.0484960665623924 7.3298422611194 6.06424545921372e-13 *** df.mm.trans1:probe15 0.0990875746752586 0.0484960665623924 2.0432084847083 0.041386485435053 * df.mm.trans1:probe16 0.122065114158851 0.0484960665623924 2.51701061160926 0.0120463480405766 * df.mm.trans1:probe17 -0.229316483162986 0.0484960665623924 -4.72855840520509 2.70900007320398e-06 *** df.mm.trans1:probe18 0.0722101876278777 0.0484960665623924 1.4889906078254 0.136917033952960 df.mm.trans1:probe19 -0.0468721697294888 0.0484960665623924 -0.96651487537006 0.3341032214358 df.mm.trans1:probe20 0.0338464676659554 0.0484960665623924 0.69792191542814 0.48544585783305 df.mm.trans1:probe21 -0.245957135034523 0.0484960665623924 -5.07169245815201 4.99206374165793e-07 *** df.mm.trans1:probe22 -0.231102905303907 0.0484960665623924 -4.76539483891096 2.27036268117610e-06 *** df.mm.trans2:probe2 0.282299485902491 0.0484960665623924 5.82108005685987 8.71913113943363e-09 *** df.mm.trans2:probe3 0.056276826782846 0.0484960665623924 1.16044105784215 0.246244525149030 df.mm.trans2:probe4 0.32093726424741 0.0484960665623924 6.61779989588454 7.00938809207607e-11 *** df.mm.trans2:probe5 0.178042363438744 0.0484960665623924 3.67127431272564 0.000258750426934758 *** df.mm.trans2:probe6 0.247532671438406 0.0484960665623924 5.10418037965912 4.23085100167799e-07 *** df.mm.trans3:probe2 -0.131066060678241 0.0484960665623924 -2.70261219040556 0.0070378409986563 ** df.mm.trans3:probe3 -0.198479697967954 0.0484960665623924 -4.09269683166161 4.73412263033332e-05 *** df.mm.trans3:probe4 0.0749624813451489 0.0484960665623924 1.54574353465773 0.122595203265439 df.mm.trans3:probe5 0.31616111652049 0.0484960665623924 6.51931463583205 1.30915163973173e-10 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.04993210633071 0.143072261797075 28.306899293134 6.33182314645321e-120 *** df.mm.trans1 0.101482691053606 0.126033923490360 0.805201395332014 0.420962809128496 df.mm.trans2 0.254946938907225 0.113724257873002 2.24179909964265 0.025270725758291 * df.mm.exp2 0.131403421862028 0.151446860771846 0.867653652194133 0.385866000241978 df.mm.exp3 0.092330930413445 0.151446860771846 0.609658925532574 0.542275420758098 df.mm.exp4 0.384739392864289 0.151446860771846 2.54042501048534 0.0112756095299953 * df.mm.exp5 0.220804831849678 0.151446860771846 1.45796902441127 0.145274485964777 df.mm.exp6 0.0496167265285848 0.151446860771846 0.327618058741622 0.7432933803229 df.mm.exp7 0.139444139622901 0.151446860771846 0.920746319284712 0.357483759363776 df.mm.exp8 0.330269504568051 0.151446860771846 2.18076164064966 0.0295161329801757 * df.mm.trans1:exp2 -0.0972034558464091 0.142851072906554 -0.680453103141865 0.496431018726601 df.mm.trans2:exp2 -0.198683791229474 0.116878150559907 -1.69992244296881 0.089566873697787 . df.mm.trans1:exp3 -0.127416581818057 0.142851072906554 -0.891953971542147 0.37270832598349 df.mm.trans2:exp3 0.0454997081056064 0.116878150559907 0.389291821333919 0.697172588240037 df.mm.trans1:exp4 -0.337836591023889 0.142851072906554 -2.36495662335617 0.0182899575547077 * df.mm.trans2:exp4 -0.241133579571101 0.116878150559907 -2.06311939756015 0.0394505346384970 * df.mm.trans1:exp5 -0.160801169709527 0.142851072906554 -1.12565601670150 0.260677076094175 df.mm.trans2:exp5 -0.177037843756573 0.116878150559907 -1.51472146768638 0.130271108833674 df.mm.trans1:exp6 0.00405244362504255 0.142851072906554 0.0283683107350091 0.977376067735481 df.mm.trans2:exp6 -0.145071225078995 0.116878150559907 -1.24121766458511 0.214919728425730 df.mm.trans1:exp7 -0.0772451790836659 0.142851072906553 -0.540739229408491 0.588850497317064 df.mm.trans2:exp7 -0.110558698244974 0.116878150559907 -0.945931277277573 0.344493239792658 df.mm.trans1:exp8 -0.247504786311729 0.142851072906554 -1.73260712205945 0.0835832881083193 . df.mm.trans2:exp8 -0.180389784837707 0.116878150559907 -1.54340040438308 0.123162323955364 df.mm.trans1:probe2 0.0905225762571685 0.0834070570725695 1.08531075707908 0.27813843500712 df.mm.trans1:probe3 0.080416667122269 0.0834070570725695 0.964147039168416 0.335288025558258 df.mm.trans1:probe4 0.0987630712764345 0.0834070570725695 1.18410929174140 0.236751031168141 df.mm.trans1:probe5 -0.00540222174804194 0.0834070570725695 -0.0647693605031725 0.94837517686242 df.mm.trans1:probe6 0.089566936732441 0.0834070570725695 1.07385321909286 0.283239455911391 df.mm.trans1:probe7 0.104964987741459 0.0834070570725695 1.25846650661866 0.208621064588636 df.mm.trans1:probe8 0.114259518205475 0.0834070570725695 1.36990228663820 0.171134030865072 df.mm.trans1:probe9 0.148219958334898 0.0834070570725695 1.77706735541499 0.0759689701889762 . df.mm.trans1:probe10 0.0755479121368053 0.0834070570725695 0.90577362142239 0.365351300651252 df.mm.trans1:probe11 0.0418716644230767 0.0834070570725695 0.502015847251937 0.615806112906337 df.mm.trans1:probe12 0.0484095210658635 0.0834070570725695 0.58040078100039 0.561821549106427 df.mm.trans1:probe13 0.143902374712886 0.0834070570725695 1.72530214784682 0.084891662541299 . df.mm.trans1:probe14 0.0201923594410466 0.0834070570725695 0.242094136272881 0.808774479040363 df.mm.trans1:probe15 0.00289029863920013 0.0834070570725695 0.0346529267503754 0.972365867604201 df.mm.trans1:probe16 -0.00431749991723717 0.0834070570725695 -0.0517642039987176 0.958730585834759 df.mm.trans1:probe17 0.0792239669828865 0.0834070570725695 0.949847288269104 0.342500797435065 df.mm.trans1:probe18 0.0813430737266728 0.0834070570725695 0.975254092179503 0.329753792885828 df.mm.trans1:probe19 0.108035054397853 0.0834070570725695 1.29527474280570 0.195630494552269 df.mm.trans1:probe20 0.0831586253273155 0.0834070570725695 0.99702145413142 0.319080881397493 df.mm.trans1:probe21 0.0318882926000712 0.0834070570725695 0.382321277350984 0.702333146487211 df.mm.trans1:probe22 0.113962102807552 0.0834070570725695 1.36633645649910 0.172249539213416 df.mm.trans2:probe2 0.0190728756710993 0.0834070570725695 0.22867220521286 0.819187048695533 df.mm.trans2:probe3 0.00557629699390499 0.0834070570725695 0.0668564170661633 0.946714117038229 df.mm.trans2:probe4 -0.0559234221793281 0.0834070570725695 -0.670487895654574 0.502756579514491 df.mm.trans2:probe5 -0.0910910891655948 0.0834070570725695 -1.09212688185773 0.275133728079832 df.mm.trans2:probe6 -0.0847050860285281 0.0834070570725695 -1.01556257949288 0.310170553756877 df.mm.trans3:probe2 -0.103959641974486 0.0834070570725695 -1.24641302095139 0.213008282417838 df.mm.trans3:probe3 -0.0535179690775851 0.0834070570725695 -0.641647972677193 0.52130100943963 df.mm.trans3:probe4 -0.0230430367331602 0.0834070570725695 -0.276272027115299 0.782416487846338 df.mm.trans3:probe5 0.0369519791951029 0.0834070570725695 0.443031806804457 0.657872560909154