fitVsDatCorrelation=0.884366088669784 cont.fitVsDatCorrelation=0.236945822634234 fstatistic=13182.5813372547,66,1014 cont.fstatistic=3031.48413866564,66,1014 residuals=-0.651619391520819,-0.0828112284718437,-0.00576445290697969,0.0827137975856658,0.597684069030274 cont.residuals=-0.604689368009442,-0.201251934531706,-0.0613559141684329,0.132202869632664,1.29467454998444 predictedValues: Include Exclude Both Lung 63.4423184549711 66.1146451939498 69.9170513036677 cerebhem 61.1506909021565 62.983692572006 63.7657049616581 cortex 59.3967697333901 67.5975091655806 62.7377953986455 heart 62.3870317464586 71.9261457373205 71.0715331425592 kidney 63.2874119092276 67.8473396132738 72.892047839252 liver 62.3116313643672 68.5069002915073 75.9641536088479 stomach 66.6812007288993 106.182989978408 74.1833090885118 testicle 60.6884234661712 81.310787550247 79.4150427351916 diffExp=-2.67232673897868,-1.83300166984947,-8.20073943219047,-9.53911399086192,-4.55992770404622,-6.19526892714009,-39.5017892495086,-20.6223640840758 diffExpScore=0.989375777165506 diffExp1.5=0,0,0,0,0,0,-1,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,0,0,-1,0 diffExp1.4Score=0.5 diffExp1.3=0,0,0,0,0,0,-1,-1 diffExp1.3Score=0.666666666666667 diffExp1.2=0,0,0,0,0,0,-1,-1 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 69.2988192065965 61.2220588176852 68.2785709139341 cerebhem 65.7630793777043 71.4117092689905 62.8286002719801 cortex 64.0182608099567 72.8979490083668 67.2029980154472 heart 65.8139836457149 62.3370524378348 61.2950131688401 kidney 71.4816859195737 68.2754078928609 64.1020117033064 liver 64.9900784706826 67.0643811291407 69.2140478673725 stomach 68.4830706828425 69.577518310836 62.8792540997311 testicle 65.4527752072941 63.6674696519463 67.9526554141359 cont.diffExp=8.07676038891136,-5.64862989128615,-8.87968819841015,3.47693120788006,3.20627802671274,-2.07430265845814,-1.09444762799352,1.78530555534780 cont.diffExpScore=15.9133989260817 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=0.671131138879218 cont.tran.correlation=-0.210921928570927 tran.covariance=0.00372332041061911 cont.tran.covariance=-0.000512526037027694 tran.mean=68.2384680254959 cont.tran.mean=66.9847062398767 weightedLogRatios: wLogRatio Lung -0.172082088183286 cerebhem -0.121922381259675 cortex -0.536583309044026 heart -0.598226197325233 kidney -0.290990270114316 liver -0.396163533625234 stomach -2.06220035337299 testicle -1.24382404832973 cont.weightedLogRatios: wLogRatio Lung 0.517549150604334 cerebhem -0.348340082092053 cortex -0.548679248857494 heart 0.225772982878496 kidney 0.194878616647910 liver -0.131641545604051 stomach -0.0671379277807684 testicle 0.115252839895290 varWeightedLogRatios=0.436701869209854 cont.varWeightedLogRatios=0.116299149223892 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.92627017527738 0.0668038832882678 58.7730829708656 0 *** df.mm.trans1 0.236376355884578 0.0571084214498397 4.13908053984989 3.77655517861438e-05 *** df.mm.trans2 0.290916938067132 0.049880509336647 5.83227681384955 7.34621948209515e-09 *** df.mm.exp2 0.00678965157346795 0.0628570745031321 0.108017301586787 0.914003336902196 df.mm.exp3 0.0646350677878411 0.0628570745031321 1.02828628756212 0.304060398618667 df.mm.exp4 0.0510985414681852 0.0628570745031321 0.812932225562598 0.416447741571566 df.mm.exp5 -0.018244776620555 0.0628570745031321 -0.290258125513714 0.771678119179812 df.mm.exp6 -0.065390831724907 0.0628570745031321 -1.04030981781770 0.29844396645044 df.mm.exp7 0.464335963606002 0.0628570745031321 7.38717109054867 3.1275236637115e-13 *** df.mm.exp8 0.0351320054542997 0.0628570745031321 0.558918876387559 0.57634049577713 df.mm.trans1:exp2 -0.0435796133144356 0.0573426333203577 -0.759986257885439 0.447439456974056 df.mm.trans2:exp2 -0.0553040902481319 0.0389951237827868 -1.41823091923468 0.156430605231247 df.mm.trans1:exp3 -0.130526346782765 0.0573426333203577 -2.27625309869446 0.0230386485961951 * df.mm.trans2:exp3 -0.0424542155766444 0.0389951237827868 -1.08870575236857 0.276542346772367 df.mm.trans1:exp4 -0.0678722345593387 0.0573426333203578 -1.18362604975176 0.236838527023066 df.mm.trans2:exp4 0.0331510139583792 0.0389951237827868 0.850132292002433 0.395452223748164 df.mm.trans1:exp5 0.0158000997093430 0.0573426333203578 0.275538439629587 0.782958736372171 df.mm.trans2:exp5 0.044114668810376 0.0389951237827868 1.13128680027037 0.258201878727655 df.mm.trans1:exp6 0.0474078170676755 0.0573426333203578 0.826746424476547 0.408575300182307 df.mm.trans2:exp6 0.100935022606470 0.0389951237827868 2.58840113365725 0.0097802309294868 ** df.mm.trans1:exp7 -0.414544020845547 0.0573426333203578 -7.22924631887068 9.54884560346392e-13 *** df.mm.trans2:exp7 0.00943767918260314 0.0389951237827868 0.24202203422083 0.808812027508386 df.mm.trans1:exp8 -0.0795101650662363 0.0573426333203578 -1.38658028873621 0.165874546533565 df.mm.trans2:exp8 0.171756406996227 0.0389951237827868 4.40456114341259 1.17254945288474e-05 *** df.mm.trans1:probe2 -0.0310319238992412 0.0426941928392267 -0.726841798276828 0.467490634333171 df.mm.trans1:probe3 -0.189251248714712 0.0426941928392267 -4.43271639839578 1.03189766377658e-05 *** df.mm.trans1:probe4 0.0166883096935065 0.0426941928392267 0.390880084238846 0.695967985810281 df.mm.trans1:probe5 0.210463878966977 0.0426941928392267 4.92956687949388 9.6225634080831e-07 *** df.mm.trans1:probe6 -0.00978514204973566 0.0426941928392267 -0.229191405177362 0.818766370597249 df.mm.trans1:probe7 -0.1639881297192 0.0426941928392267 -3.84099379362269 0.000130142205053159 *** df.mm.trans1:probe8 -0.0879323292465152 0.0426941928392267 -2.05958523627889 0.0396930456957472 * df.mm.trans1:probe9 -0.210392736946877 0.0426941928392267 -4.92790056341272 9.70302990875822e-07 *** df.mm.trans1:probe10 -0.178975217966664 0.0426941928392267 -4.19202720708715 3.00607293049993e-05 *** df.mm.trans1:probe11 -0.0266069329519641 0.0426941928392267 -0.623197938233841 0.533294597526178 df.mm.trans1:probe12 -0.125917271870530 0.0426941928392267 -2.94928334503701 0.00325823381962957 ** df.mm.trans1:probe13 -0.219409623209758 0.0426941928392267 -5.1390975825679 3.31069814050553e-07 *** df.mm.trans1:probe14 -0.152020092471229 0.0426941928392267 -3.56067376759389 0.000387072764917500 *** df.mm.trans1:probe15 -0.179292621974253 0.0426941928392267 -4.19946156727717 2.91069660109695e-05 *** df.mm.trans1:probe16 -0.064944860086656 0.0426941928392267 -1.5211637875722 0.128530497849345 df.mm.trans1:probe17 0.140260158333689 0.0426941928392267 3.28522801360518 0.00105389527403303 ** df.mm.trans1:probe18 0.122034341175531 0.0426941928392267 2.85833583117674 0.00434587053685538 ** df.mm.trans1:probe19 0.239640320612096 0.0426941928392267 5.61294885031574 2.56648233433578e-08 *** df.mm.trans1:probe20 -0.00468062992687102 0.0426941928392267 -0.109631535710181 0.912723291654997 df.mm.trans1:probe21 0.224224452419143 0.0426941928392267 5.25187238609952 1.83429365250641e-07 *** df.mm.trans1:probe22 0.20281635390597 0.0426941928392267 4.75044357132396 2.32202822059204e-06 *** df.mm.trans2:probe2 -0.127888723071589 0.0426941928392267 -2.99545944229884 0.00280691856885652 ** df.mm.trans2:probe3 -0.251652130243024 0.0426941928392267 -5.89429413013307 5.11815794065725e-09 *** df.mm.trans2:probe4 0.0486542669005249 0.0426941928392267 1.13959917414863 0.254722454195454 df.mm.trans2:probe5 -0.182225473293447 0.0426941928392267 -4.2681559522545 2.15567784311702e-05 *** df.mm.trans2:probe6 -0.0802150268116115 0.0426941928392267 -1.87882757530226 0.0605547174212401 . df.mm.trans3:probe2 -0.0183857784154158 0.0426941928392267 -0.430638857248128 0.666822500813742 df.mm.trans3:probe3 -0.515465119040593 0.0426941928392267 -12.0734246219779 1.86813611733082e-31 *** df.mm.trans3:probe4 -0.194668521678214 0.0426941928392267 -4.55960187398967 5.75025416849754e-06 *** df.mm.trans3:probe5 -0.635611480506309 0.0426941928392267 -14.8875394576454 1.74675143854357e-45 *** df.mm.trans3:probe6 0.346557647702429 0.0426941928392267 8.1172080944933 1.37330602859267e-15 *** df.mm.trans3:probe7 -0.46958750187746 0.0426941928392267 -10.9988612185686 1.16050999598273e-26 *** df.mm.trans3:probe8 -0.509449644531915 0.0426941928392267 -11.9325278369905 8.29658548421402e-31 *** df.mm.trans3:probe9 -0.607016556656225 0.0426941928392267 -14.2177780229284 5.75419512325399e-42 *** df.mm.trans3:probe10 0.678636907345748 0.0426941928392267 15.8952977493049 5.75024021497884e-51 *** df.mm.trans3:probe11 -0.0269405362353663 0.0426941928392267 -0.631011724166238 0.528175059706688 df.mm.trans3:probe12 -0.200910655931586 0.0426941928392267 -4.70580757172655 2.87907969053687e-06 *** df.mm.trans3:probe13 -0.463639887699537 0.0426941928392267 -10.8595538846574 4.58193933985158e-26 *** df.mm.trans3:probe14 -0.437647159801424 0.0426941928392267 -10.2507420962253 1.57599691286194e-23 *** df.mm.trans3:probe15 -0.228492161519032 0.0426941928392267 -5.35183232950354 1.07657666394992e-07 *** df.mm.trans3:probe16 -0.536873579332124 0.0426941928392267 -12.5748619104670 8.35902904639832e-34 *** df.mm.trans3:probe17 0.313177537751883 0.0426941928392267 7.33536616867811 4.52079582615225e-13 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.99075119082087 0.139036711371233 28.7028594927380 4.24683015953604e-133 *** df.mm.trans1 0.265315002526660 0.118857867524335 2.23220395967762 0.0258192529949985 * df.mm.trans2 0.120790269283492 0.103814653255456 1.16351849662555 0.244892911933472 df.mm.exp2 0.184770578247629 0.130822348868855 1.4123777767731 0.158145583177517 df.mm.exp3 0.111171550220365 0.130822348868855 0.849790201610052 0.395642331790942 df.mm.exp4 0.0743503739176537 0.130822348868855 0.568330828490071 0.569936182369592 df.mm.exp5 0.203175708208688 0.130822348868855 1.55306574117825 0.120719468670302 df.mm.exp6 0.0133443817918972 0.130822348868855 0.102003838849235 0.918773794989668 df.mm.exp7 0.19847232598206 0.130822348868855 1.51711330440200 0.129549763719569 df.mm.exp8 -0.0131480357821013 0.130822348868855 -0.100502979007675 0.919964885189185 df.mm.trans1:exp2 -0.237139868261087 0.119345324939055 -1.98700592907334 0.0471904835459291 * df.mm.trans2:exp2 -0.0308162897573553 0.0811592605609053 -0.379701460367908 0.704246543690252 df.mm.trans1:exp3 -0.190431049587445 0.119345324939055 -1.59563057610083 0.110882894566387 df.mm.trans2:exp3 0.063381391309006 0.0811592605609054 0.780950822752284 0.435013698200422 df.mm.trans1:exp4 -0.125945907937773 0.119345324939055 -1.05530659036781 0.291536499955295 df.mm.trans2:exp4 -0.0563019455064593 0.0811592605609053 -0.69372176529637 0.488015595088644 df.mm.trans1:exp5 -0.172162299488258 0.119345324939055 -1.44255587368985 0.149454429505073 df.mm.trans2:exp5 -0.094133629376474 0.0811592605609054 -1.15986307324513 0.246377585746816 df.mm.trans1:exp6 -0.0775376296441836 0.119345324939055 -0.649691386602525 0.516038733426556 df.mm.trans2:exp6 0.0778011258200401 0.0811592605609054 0.95862290122339 0.337977259692606 df.mm.trans1:exp7 -0.210313621759834 0.119345324939055 -1.76222756833779 0.0783321161617393 . df.mm.trans2:exp7 -0.0705383863704023 0.0811592605609053 -0.869135399742427 0.384978748756520 df.mm.trans1:exp8 -0.0439509380008639 0.119345324939055 -0.368266943202911 0.71275106365561 df.mm.trans2:exp8 0.0523142246418135 0.0811592605609054 0.644587250798751 0.519340490393256 df.mm.trans1:probe2 0.0166356506015446 0.088858010565051 0.187216104611818 0.851528650911679 df.mm.trans1:probe3 -0.00233601507347335 0.088858010565051 -0.0262893019843518 0.979031760445526 df.mm.trans1:probe4 -0.0849213134705436 0.088858010565051 -0.955696767579267 0.339453239190968 df.mm.trans1:probe5 0.0403753519218267 0.088858010565051 0.454380552356266 0.649652194995446 df.mm.trans1:probe6 -0.0139968136997061 0.088858010565051 -0.157518873207941 0.874867294931464 df.mm.trans1:probe7 -0.0967689155752716 0.088858010565051 -1.08902860822468 0.276400021625256 df.mm.trans1:probe8 -0.0153428937254207 0.088858010565051 -0.172667535856978 0.862947220517549 df.mm.trans1:probe9 0.0635781870395443 0.088858010565051 0.715503156499325 0.474462908558909 df.mm.trans1:probe10 0.00143688967527407 0.088858010565051 0.0161706262174545 0.987101450343961 df.mm.trans1:probe11 -0.109725989931726 0.088858010565051 -1.2348463490683 0.217173755015408 df.mm.trans1:probe12 0.0147538068870923 0.088858010565051 0.166038005952106 0.86816014018963 df.mm.trans1:probe13 -0.0579145599422739 0.088858010565051 -0.651765210294416 0.514700342792253 df.mm.trans1:probe14 -0.0794073739605773 0.088858010565051 -0.893643391919572 0.371724767263166 df.mm.trans1:probe15 -0.0834389619637874 0.088858010565051 -0.93901451802934 0.347946907228479 df.mm.trans1:probe16 0.0202935507047213 0.088858010565051 0.228381780952262 0.81939549908375 df.mm.trans1:probe17 -0.00629936628880195 0.088858010565051 -0.0708924974658342 0.943497293455228 df.mm.trans1:probe18 -0.0922753736835113 0.088858010565051 -1.03845869490808 0.299304115018874 df.mm.trans1:probe19 -0.0277812577961051 0.088858010565051 -0.312647758141817 0.754612576010672 df.mm.trans1:probe20 -0.0552007584162384 0.088858010565051 -0.621224333801927 0.534591652464247 df.mm.trans1:probe21 -0.0204792979675883 0.088858010565051 -0.230472163819106 0.817771380065027 df.mm.trans1:probe22 -0.0990792656798182 0.088858010565051 -1.11502907897408 0.265102154972648 df.mm.trans2:probe2 0.00421253553191399 0.088858010565051 0.0474074932032165 0.962197792197281 df.mm.trans2:probe3 -0.0697720876536935 0.088858010565051 -0.785208752818238 0.432514603957963 df.mm.trans2:probe4 -0.0597352047914145 0.088858010565051 -0.672254582468777 0.501574784542535 df.mm.trans2:probe5 0.126989484299416 0.088858010565051 1.42912815053911 0.153275388155025 df.mm.trans2:probe6 0.0665256340040798 0.088858010565051 0.74867345758746 0.454227666344243 df.mm.trans3:probe2 -0.21243295825764 0.088858010565051 -2.39070126493686 0.0169978404577304 * df.mm.trans3:probe3 -0.111322606733172 0.088858010565051 -1.25281452989177 0.210562046473346 df.mm.trans3:probe4 -0.125048289392376 0.088858010565051 -1.40728211893548 0.159650195575675 df.mm.trans3:probe5 -0.134890725954524 0.088858010565051 -1.5180480082409 0.129313998202589 df.mm.trans3:probe6 -0.234422444796507 0.088858010565051 -2.63816895410788 0.00846318952659129 ** df.mm.trans3:probe7 -0.167786516923464 0.088858010565051 -1.88825425931218 0.0592770931583139 . df.mm.trans3:probe8 -0.222922364440591 0.088858010565051 -2.5087480917367 0.0122710928560528 * df.mm.trans3:probe9 -0.108148902719723 0.088858010565051 -1.21709795247497 0.223850134718913 df.mm.trans3:probe10 -0.141907140134793 0.088858010565051 -1.59701009770983 0.110575024353243 df.mm.trans3:probe11 -0.163267073779607 0.088858010565051 -1.83739285565124 0.0664443998257237 . df.mm.trans3:probe12 -0.226425680852046 0.088858010565051 -2.54817409721643 0.0109753706463710 * df.mm.trans3:probe13 -0.225214962416913 0.088858010565051 -2.53454878164347 0.0114086990270767 * df.mm.trans3:probe14 -0.203594099327439 0.088858010565051 -2.29122954737314 0.0221545076220106 * df.mm.trans3:probe15 -0.104809321374059 0.088858010565051 -1.17951460659059 0.238469985569868 df.mm.trans3:probe16 -0.0469660575344943 0.088858010565051 -0.52855175617635 0.597232097262818 df.mm.trans3:probe17 -0.176269265498696 0.088858010565051 -1.98371834320614 0.0475565890688847 *