fitVsDatCorrelation=0.862915522834501 cont.fitVsDatCorrelation=0.232410956959852 fstatistic=13414.0131539611,58,830 cont.fstatistic=3610.78070561039,58,830 residuals=-0.542334564926295,-0.0772628858277249,-0.0112338767701342,0.0761774919636823,1.09994874329221 cont.residuals=-0.489819354217467,-0.155834736567251,-0.0446512424827762,0.0988589792839369,1.44673085137081 predictedValues: Include Exclude Both Lung 48.5360046301144 48.2104949906829 69.0280762181494 cerebhem 50.4829821894216 53.7696638423335 57.7488638223389 cortex 47.349984943797 45.1862893948526 59.459320751113 heart 49.5023947148583 49.3143584241133 65.7568598710044 kidney 47.1054316833083 48.2703000161305 69.4970693054792 liver 52.5792196731932 48.1478835117333 66.222379728412 stomach 48.2707271404286 49.2804865750822 62.9434467860118 testicle 49.468695236788 48.8575131180965 63.4021679842577 diffExp=0.325509639431587,-3.28668165291192,2.16369554894441,0.188036290744947,-1.16486833282218,4.4313361614599,-1.00975943465355,0.611182118691481 diffExpScore=4.0451956632157 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 52.1963460704578 47.4311953315291 52.7996218874516 cerebhem 52.2602516652396 57.9918744202958 50.7068784210151 cortex 50.7964386133382 51.9740704029472 54.8780762462251 heart 52.657660617185 49.5868574118133 53.9441771729349 kidney 50.2628073388676 52.9161024921238 51.1549743959069 liver 50.7548018618767 55.2548125538786 53.8074022033506 stomach 50.6246295492845 47.9219789176066 48.9357073275526 testicle 52.9906999127135 52.8805383508066 53.3386856631002 cont.diffExp=4.76515073892874,-5.73162275505616,-1.17763178960906,3.07080320537172,-2.65329515325617,-4.50001069200189,2.70265063167784,0.110161561906942 cont.diffExpScore=5.59865845953246 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=0.408912525175313 cont.tran.correlation=-0.00278717300017133 tran.covariance=0.000726011171278247 cont.tran.covariance=-1.38246113482431e-05 tran.mean=49.0207768803084 cont.tran.mean=51.7813165943728 weightedLogRatios: wLogRatio Lung 0.0261019790259882 cerebhem -0.249339048917362 cortex 0.179335311635294 heart 0.0148429211682401 kidney -0.0944050802908496 liver 0.344981929952967 stomach -0.0804757510481869 testicle 0.0484237296579875 cont.weightedLogRatios: wLogRatio Lung 0.374040307390781 cerebhem -0.417127894858766 cortex -0.0902833128060136 heart 0.236364044154191 kidney -0.202836298714062 liver -0.337204262118752 stomach 0.213805188476917 testicle 0.00825984026429676 varWeightedLogRatios=0.0324988037121224 cont.varWeightedLogRatios=0.0818248362578897 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.83533048199571 0.062814201894611 61.0583334073174 0 *** df.mm.trans1 0.0337870545866892 0.0543812897612409 0.621299250809056 0.534573337943107 df.mm.trans2 0.0287664358997616 0.0481786053224402 0.597079050072937 0.550617440872789 df.mm.exp2 0.326872758797577 0.0622692973171923 5.24934073260096 1.94023185257221e-07 *** df.mm.exp3 0.0596985183905406 0.0622692973171923 0.958715144743702 0.337981445618387 df.mm.exp4 0.090903034559115 0.0622692973171923 1.45983716655844 0.144713208112534 df.mm.exp5 -0.0354490998898490 0.0622692973171923 -0.569286974755401 0.569315428817344 df.mm.exp6 0.120210393159448 0.0622692973171923 1.93049220624910 0.053886241158123 . df.mm.exp7 0.108747555021909 0.0622692973171923 1.74640729391826 0.0811101331320157 . df.mm.exp8 0.117380862040752 0.0622692973171923 1.88505197742681 0.0597719357915561 . df.mm.trans1:exp2 -0.287542351692469 0.057725183569534 -4.98122888333659 7.68875508099501e-07 *** df.mm.trans2:exp2 -0.217740055516314 0.0433375968126413 -5.02427618351003 6.18999258995731e-07 *** df.mm.trans1:exp3 -0.0844379027707963 0.057725183569534 -1.46275676488902 0.143912496262724 df.mm.trans2:exp3 -0.124481545289991 0.0433375968126413 -2.87236843861357 0.00417791699016457 ** df.mm.trans1:exp4 -0.0711878741121671 0.057725183569534 -1.23322040243348 0.217842687000028 df.mm.trans2:exp4 -0.0682644857400129 0.0433375968126413 -1.57517930759143 0.115596124898550 df.mm.trans1:exp5 0.00553153059088148 0.057725183569534 0.0958252576922231 0.923682487789233 df.mm.trans2:exp5 0.0366888291666042 0.0433375968126413 0.846581994964296 0.39747217053483 df.mm.trans1:exp6 -0.0401953007565663 0.057725183569534 -0.696321748516369 0.486422301386137 df.mm.trans2:exp6 -0.121509947774925 0.0433375968126413 -2.80379985766725 0.00516829228304501 ** df.mm.trans1:exp7 -0.114228127474634 0.057725183569534 -1.97882657812665 0.0481656963407794 * df.mm.trans2:exp7 -0.0867960979136158 0.0433375968126413 -2.00278982447633 0.0455246504440771 * df.mm.trans1:exp8 -0.098346698021935 0.057725183569534 -1.70370524510277 0.088810485027815 . df.mm.trans2:exp8 -0.104049431372505 0.0433375968126413 -2.4009045038269 0.0165737265202650 * df.mm.trans1:probe2 -0.106345052486143 0.0387232303425396 -2.74628566742577 0.00615789961790496 ** df.mm.trans1:probe3 0.232422512880846 0.0387232303425396 6.00214679469852 2.90776177712024e-09 *** df.mm.trans1:probe4 0.362061237200213 0.0387232303425396 9.34997504075657 7.92732252629857e-20 *** df.mm.trans1:probe5 -0.144669909553879 0.0387232303425396 -3.7359979597299 0.000199764621864949 *** df.mm.trans1:probe6 0.0358895441117888 0.0387232303425396 0.926822059893133 0.354288345955564 df.mm.trans1:probe7 -0.070816322949172 0.0387232303425396 -1.82878138840024 0.0677910648706702 . df.mm.trans1:probe8 0.193852366239187 0.0387232303425396 5.00610007286066 6.7847950752298e-07 *** df.mm.trans1:probe9 0.0858720376698489 0.0387232303425396 2.21758455868063 0.0268532934641865 * df.mm.trans1:probe10 -0.0550574289532427 0.0387232303425396 -1.42181911132448 0.155454415752369 df.mm.trans1:probe11 -0.0276177925250759 0.0387232303425396 -0.713209933178953 0.475916333871211 df.mm.trans1:probe12 1.41225058161888e-05 0.0387232303425396 0.000364703711215808 0.99970909615615 df.mm.trans1:probe13 0.0611528914681083 0.0387232303425396 1.57923011399513 0.114664289478113 df.mm.trans1:probe14 0.00906609453585808 0.0387232303425396 0.23412547082619 0.814945309730557 df.mm.trans1:probe15 -0.0290913926331143 0.0387232303425396 -0.751264612372893 0.452706372927138 df.mm.trans1:probe16 -0.0866766224208692 0.0387232303425396 -2.23836239007287 0.0254618484897572 * df.mm.trans1:probe17 0.00553493662082666 0.0387232303425396 0.142935818418698 0.886375561539635 df.mm.trans1:probe18 -0.070636502092459 0.0387232303425396 -1.82413764212385 0.0684906912915 . df.mm.trans1:probe19 -0.00336594850186492 0.0387232303425396 -0.0869232363129386 0.930753494781948 df.mm.trans1:probe20 -0.0799293544117552 0.0387232303425396 -2.06411897211862 0.0393166998495110 * df.mm.trans1:probe21 0.162428159053047 0.0387232303425396 4.19459217674336 3.02787272290355e-05 *** df.mm.trans1:probe22 -0.0652487427131182 0.0387232303425396 -1.68500257173635 0.0923639878894415 . df.mm.trans2:probe2 0.0225812394007098 0.0387232303425396 0.583144515603675 0.559954424524485 df.mm.trans2:probe3 0.0408090256086476 0.0387232303425396 1.05386418559757 0.292251787573905 df.mm.trans2:probe4 0.0502759791658669 0.0387232303425396 1.29834155676408 0.194530625865574 df.mm.trans2:probe5 0.0700125584910525 0.0387232303425396 1.80802474049124 0.070964507446777 . df.mm.trans2:probe6 -0.0114815348348600 0.0387232303425396 -0.296502505945298 0.766920529684675 df.mm.trans3:probe2 0.130145697770046 0.0387232303425396 3.36092047638581 0.000812322854843643 *** df.mm.trans3:probe3 0.302578013292091 0.0387232303425396 7.81386290904798 1.67598977195897e-14 *** df.mm.trans3:probe4 0.296389991194771 0.0387232303425396 7.65406162071066 5.4078481017101e-14 *** df.mm.trans3:probe5 0.148907194634392 0.0387232303425396 3.84542284611028 0.000129552688939443 *** df.mm.trans3:probe6 0.424590370487394 0.0387232303425396 10.9647456250818 3.21337207767974e-26 *** df.mm.trans3:probe7 1.13620645522431 0.0387232303425396 29.3417270504969 2.15217818587321e-130 *** df.mm.trans3:probe8 0.0558268278905426 0.0387232303425396 1.44168829399581 0.149767514053785 df.mm.trans3:probe9 0.535019609094655 0.0387232303425396 13.8165025066854 3.1207041993054e-39 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.77775285703656 0.120895262931277 31.2481462502300 2.57271545228762e-142 *** df.mm.trans1 0.132676577021272 0.10466487077648 1.26763235875591 0.205284776473102 df.mm.trans2 0.0577785191643286 0.0927268831321144 0.623104295245291 0.533387159181935 df.mm.exp2 0.242688803890278 0.119846513123550 2.02499678601487 0.0431873565122687 * df.mm.exp3 0.0256686593180856 0.119846513123550 0.214179442097107 0.830459732271045 df.mm.exp4 0.0317991941572034 0.119846513123550 0.265332660320468 0.790819091015354 df.mm.exp5 0.103324729640467 0.119846513123550 0.862142142875274 0.388858320712394 df.mm.exp6 0.105762022029291 0.119846513123550 0.882478924691456 0.377773376656471 df.mm.exp7 0.0557165099726803 0.119846513123550 0.464898882082969 0.642125806585416 df.mm.exp8 0.113701317290480 0.119846513123550 0.948724450358138 0.34303692066686 df.mm.trans1:exp2 -0.241465221959790 0.111100691163820 -2.17339081719793 0.0300330097878864 * df.mm.trans2:exp2 -0.0416660409460776 0.083409643097343 -0.499535058523765 0.617534883334818 df.mm.trans1:exp3 -0.0528549070161826 0.111100691163820 -0.475738777702533 0.634385675716556 df.mm.trans2:exp3 0.0657961472148316 0.0834096430973429 0.788831420103842 0.430435798293963 df.mm.trans1:exp4 -0.0229999591485395 0.111100691163820 -0.207019046484829 0.836045760303489 df.mm.trans2:exp4 0.0126464913404637 0.0834096430973429 0.151619055913052 0.879524246834553 df.mm.trans1:exp5 -0.141071836548802 0.111100691163820 -1.26976560695548 0.204524014342257 df.mm.trans2:exp5 0.0061028162551932 0.0834096430973429 0.0731667949720266 0.941691017457473 df.mm.trans1:exp6 -0.133768284403717 0.111100691163820 -1.20402747275868 0.228922212025851 df.mm.trans2:exp6 0.0469132780366913 0.0834096430973429 0.562444296541844 0.573965215683081 df.mm.trans1:exp7 -0.0862907959060886 0.111100691163820 -0.776690000774621 0.437562971238642 df.mm.trans2:exp7 -0.045422402326919 0.0834096430973429 -0.544570155682227 0.58619545909323 df.mm.trans1:exp8 -0.0985973862689738 0.111100691163820 -0.887459701970618 0.375088537291237 df.mm.trans2:exp8 -0.00494608277207002 0.083409643097343 -0.0592986924341316 0.95272847645915 df.mm.trans1:probe2 0.103955881690511 0.0745286093368538 1.39484531665756 0.163435648904699 df.mm.trans1:probe3 0.141465478305372 0.0745286093368538 1.89813656210835 0.0580247776877076 . df.mm.trans1:probe4 0.0123728595467858 0.0745286093368538 0.166014898934488 0.86818565534435 df.mm.trans1:probe5 0.0277239407528542 0.0745286093368538 0.371990581865653 0.709994812378973 df.mm.trans1:probe6 0.0331437812601813 0.0745286093368538 0.44471219247334 0.656643696652037 df.mm.trans1:probe7 0.0170466588216452 0.0745286093368538 0.228726377337834 0.819137934527336 df.mm.trans1:probe8 0.0232591818365222 0.0745286093368538 0.312083937208536 0.755055115687113 df.mm.trans1:probe9 -0.0470810100615264 0.0745286093368538 -0.631717275827998 0.527745583294753 df.mm.trans1:probe10 0.106325052689550 0.0745286093368538 1.42663406221607 0.154061442576968 df.mm.trans1:probe11 0.079846315984773 0.0745286093368538 1.07135121257777 0.284322963139198 df.mm.trans1:probe12 0.106715706036106 0.0745286093368538 1.43187571840732 0.152555849108986 df.mm.trans1:probe13 0.0555414196793292 0.0745286093368538 0.745236227718855 0.456339899682451 df.mm.trans1:probe14 0.0468728517381859 0.0745286093368538 0.628924276935457 0.529571677267233 df.mm.trans1:probe15 0.0672157211755602 0.0745286093368538 0.901878107932474 0.367383255897755 df.mm.trans1:probe16 0.0905995979485013 0.0745286093368538 1.21563516017064 0.224469580323906 df.mm.trans1:probe17 0.0939707154089624 0.0745286093368538 1.26086768886609 0.207710826799693 df.mm.trans1:probe18 0.0667359428092021 0.0745286093368538 0.895440601978356 0.370811211476748 df.mm.trans1:probe19 0.0566673944907623 0.0745286093368538 0.760344181851529 0.447264807133594 df.mm.trans1:probe20 0.0437516830479296 0.0745286093368538 0.58704547739756 0.557332807818884 df.mm.trans1:probe21 0.0457734364166585 0.0745286093368538 0.614172689171913 0.539269521512007 df.mm.trans1:probe22 0.210172242067827 0.0745286093368538 2.82002098171311 0.00491650038096278 ** df.mm.trans2:probe2 0.122787939782005 0.0745286093368538 1.64752758537368 0.0998281424508675 . df.mm.trans2:probe3 0.00227762857967019 0.0745286093368538 0.0305604599352683 0.97562742262232 df.mm.trans2:probe4 0.0530135201236718 0.0745286093368538 0.711317715376409 0.477087229607877 df.mm.trans2:probe5 0.0440155555728148 0.0745286093368538 0.59058603084721 0.554958595935585 df.mm.trans2:probe6 0.134136836718963 0.0745286093368538 1.79980329584163 0.0722547517149148 . df.mm.trans3:probe2 0.0328397873051823 0.0745286093368538 0.440633303068266 0.659593225903225 df.mm.trans3:probe3 0.0175374844738644 0.0745286093368538 0.235312112085691 0.814024539264331 df.mm.trans3:probe4 0.0295531959910093 0.0745286093368538 0.396534917986126 0.691812380403013 df.mm.trans3:probe5 0.00661206967349792 0.0745286093368538 0.088718543554365 0.929326996625605 df.mm.trans3:probe6 -0.0124753701009441 0.0745286093368538 -0.167390351328816 0.867103690524055 df.mm.trans3:probe7 -0.00539293124073715 0.0745286093368538 -0.0723605510517743 0.942332411042933 df.mm.trans3:probe8 -0.0334350085684935 0.0745286093368538 -0.448619783275094 0.653823054657996 df.mm.trans3:probe9 -0.0505745413562366 0.0745286093368538 -0.678592312485669 0.497585370260807