fitVsDatCorrelation=0.89260491434281 cont.fitVsDatCorrelation=0.250732003714919 fstatistic=12365.1354176918,63,945 cont.fstatistic=2670.14857896755,63,945 residuals=-0.534249085278313,-0.0870389225336034,0.000782339463464762,0.0836947945260195,0.869501501991924 cont.residuals=-0.661121057752975,-0.243021568497210,-0.0319884734199052,0.200764789175907,1.07043071147012 predictedValues: Include Exclude Both Lung 70.8009199636652 47.1400120258062 76.9339024505984 cerebhem 60.6050647605855 46.1890417783213 63.5413760520266 cortex 65.9340967679058 45.8214277252422 67.9481249358254 heart 68.0498887130423 46.717077451915 72.0696905227705 kidney 65.3490332709044 48.1958844548378 72.8535070376856 liver 75.9119238559326 47.2023825226425 79.421898528495 stomach 71.4672833179424 47.8745235422142 70.9370522668012 testicle 63.4497825771042 47.2567920330126 65.9396949883023 diffExp=23.6609079378591,14.4160229822643,20.1126690426636,21.3328112611274,17.1531488160666,28.7095413332901,23.5927597757282,16.1929905440916 diffExpScore=0.993982097402696 diffExp1.5=1,0,0,0,0,1,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=1,0,1,1,0,1,1,0 diffExp1.4Score=0.833333333333333 diffExp1.3=1,1,1,1,1,1,1,1 diffExp1.3Score=0.888888888888889 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 63.6281836268142 59.6590601396817 72.6682523686043 cerebhem 64.154321144872 66.6301803717543 57.7924928895533 cortex 64.2060977578538 62.234341390015 67.4306341294426 heart 61.89330847421 66.7453981677685 65.0495425731821 kidney 64.398080281708 67.1860754543558 73.7315825066434 liver 66.9422544696947 63.5039412309524 58.9644308020044 stomach 57.7346867035955 54.9677794790612 68.5268851476767 testicle 58.4396021503926 64.455369512684 71.188284244878 cont.diffExp=3.96912348713252,-2.47585922688224,1.97175636783871,-4.85208969355845,-2.78799517264781,3.43831323874227,2.76690722453426,-6.01576736229145 cont.diffExpScore=5.6718847490969 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=0.337805440921187 cont.tran.correlation=0.44261849694564 tran.covariance=0.000423798201264757 cont.tran.covariance=0.00162010672047330 tran.mean=57.3728209225671 cont.tran.mean=62.9236675222133 weightedLogRatios: wLogRatio Lung 1.64997954306853 cerebhem 1.07800345962387 cortex 1.45805502230157 heart 1.51662743645981 kidney 1.22625143301084 liver 1.94423382702284 stomach 1.63023497246521 testicle 1.17947065335875 cont.weightedLogRatios: wLogRatio Lung 0.265426031358694 cerebhem -0.158289155105972 cortex 0.129334503312832 heart -0.314206496560972 kidney -0.177423508161973 liver 0.220271122262730 stomach 0.19798115621702 testicle -0.403378986716601 varWeightedLogRatios=0.0831457807545693 cont.varWeightedLogRatios=0.0693788649268088 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.31733814974190 0.0755405258932919 43.9146816958616 1.96231943698351e-230 *** df.mm.trans1 1.21368260172316 0.0676228321697162 17.9478226921511 3.30836840283637e-62 *** df.mm.trans2 0.499485007673978 0.06075539438767 8.221245417104 6.60787688421774e-16 *** df.mm.exp2 0.0153822568306287 0.0816425527794332 0.188409797427398 0.850595871418054 df.mm.exp3 0.0246154962681804 0.0816425527794332 0.301503265517456 0.763097092655519 df.mm.exp4 0.0166697973504345 0.0816425527794332 0.204180256287061 0.838256602651473 df.mm.exp5 -0.00348189608823405 0.0816425527794332 -0.0426480550852053 0.965991097052438 df.mm.exp6 0.0391965061798569 0.0816425527794332 0.480098978356921 0.631268120686839 df.mm.exp7 0.105982864747332 0.0816425527794332 1.29813266659676 0.194558480987379 df.mm.exp8 0.0470570421828802 0.0816425527794332 0.576378868382647 0.564496375103558 df.mm.trans1:exp2 -0.170875784792697 0.0787430701231014 -2.17004219578386 0.0302521675512039 * df.mm.trans2:exp2 -0.0357618304388553 0.0646940336304506 -0.552784058003499 0.580542016171807 df.mm.trans1:exp3 -0.0958317813807096 0.0787430701231014 -1.21701860533115 0.223900979149314 df.mm.trans2:exp3 -0.0529858129110638 0.0646940336304506 -0.819021630553024 0.412980566384839 df.mm.trans1:exp4 -0.0563006980770596 0.0787430701231014 -0.714992417606312 0.474790323571951 df.mm.trans2:exp4 -0.0256821679428419 0.0646940336304506 -0.396978925283052 0.691472638179778 df.mm.trans1:exp5 -0.0766474515034504 0.0787430701231014 -0.973386627974058 0.330610269395284 df.mm.trans2:exp5 0.0256333761286293 0.0646940336304506 0.396224731867145 0.69202869459456 df.mm.trans1:exp6 0.0305052709817054 0.0787430701231014 0.387402611226811 0.698545404712038 df.mm.trans2:exp6 -0.0378742903039199 0.0646940336304506 -0.585437144331853 0.558393590310955 df.mm.trans1:exp7 -0.09661509019237 0.0787430701231014 -1.22696625926991 0.220140862629415 df.mm.trans2:exp7 -0.0905215220331679 0.0646940336304506 -1.39922519826559 0.16207360376855 df.mm.trans1:exp8 -0.156680269161473 0.0787430701231014 -1.98976581579218 0.0469045733150004 * df.mm.trans2:exp8 -0.044582804459546 0.0646940336304506 -0.689133169748153 0.490908630914396 df.mm.trans1:probe2 -0.677109897456453 0.0431293557536337 -15.6995133737745 1.57025751782318e-49 *** df.mm.trans1:probe3 -0.648762019382913 0.0431293557536337 -15.0422376603261 5.25422194587169e-46 *** df.mm.trans1:probe4 0.238316556623823 0.0431293557536337 5.52562291876442 4.24584350575698e-08 *** df.mm.trans1:probe5 -0.340800189198509 0.0431293557536337 -7.90181497598179 7.62147284413982e-15 *** df.mm.trans1:probe6 -0.295772504408507 0.0431293557536337 -6.85780019757395 1.26209751367159e-11 *** df.mm.trans1:probe7 -0.128720482383022 0.0431293557536337 -2.98452133433913 0.00291333652032665 ** df.mm.trans1:probe8 -0.145133134970235 0.0431293557536337 -3.36506614657715 0.000796052208312704 *** df.mm.trans1:probe9 -0.612656761772494 0.0431293557536337 -14.2050988489638 1.19013632115798e-41 *** df.mm.trans1:probe10 -0.730078766067299 0.0431293557536337 -16.9276529479759 2.41510241885346e-56 *** df.mm.trans1:probe11 -0.316817161903486 0.0431293557536337 -7.34574297175291 4.41796117668508e-13 *** df.mm.trans1:probe12 -0.117920240992719 0.0431293557536337 -2.73410624694490 0.00637194143422831 ** df.mm.trans1:probe13 -0.264558516391362 0.0431293557536337 -6.13407067572698 1.25783484525559e-09 *** df.mm.trans1:probe14 -0.344763449091686 0.0431293557536337 -7.99370737325793 3.80339225534487e-15 *** df.mm.trans1:probe15 -0.240326410168654 0.0431293557536337 -5.57222351155585 3.28004262361723e-08 *** df.mm.trans1:probe16 -0.400751371195135 0.0431293557536337 -9.29184691476342 1.01692672740922e-19 *** df.mm.trans1:probe17 -0.478171851098688 0.0431293557536337 -11.0869231117231 6.12520976801011e-27 *** df.mm.trans1:probe18 -0.0541567412306272 0.0431293557536337 -1.25568166471080 0.209541787536067 df.mm.trans1:probe19 -0.344469162539323 0.0431293557536337 -7.98688402643948 4.00578143515073e-15 *** df.mm.trans1:probe20 -0.132900380126391 0.0431293557536337 -3.08143671066067 0.00211971255955119 ** df.mm.trans1:probe21 -0.485003957582358 0.0431293557536337 -11.2453327694675 1.26802152956319e-27 *** df.mm.trans1:probe22 -0.23801901108586 0.0431293557536337 -5.5187240088975 4.41049050125359e-08 *** df.mm.trans1:probe23 -0.438547758638859 0.0431293557536337 -10.1681963705639 4.04642566030696e-23 *** df.mm.trans1:probe24 -0.70078489643225 0.0431293557536337 -16.2484434136999 1.53449963772404e-52 *** df.mm.trans1:probe25 -0.118516245509419 0.0431293557536337 -2.74792524577494 0.00611157074557015 ** df.mm.trans1:probe26 -0.648759441892559 0.0431293557536337 -15.04217789847 5.25805083242078e-46 *** df.mm.trans1:probe27 0.248654584976103 0.0431293557536337 5.76532110510725 1.10311669052545e-08 *** df.mm.trans1:probe28 -0.375383917806194 0.0431293557536337 -8.703675518607 1.41137964561421e-17 *** df.mm.trans1:probe29 0.0152321572732068 0.0431293557536337 0.353173772411925 0.724036934810162 df.mm.trans1:probe30 -0.180870648588972 0.0431293557536337 -4.19367842223642 3.00273833110579e-05 *** df.mm.trans1:probe31 -0.145278916358942 0.0431293557536337 -3.36844624317630 0.000786470613419085 *** df.mm.trans1:probe32 -0.658524717058469 0.0431293557536337 -15.2685961928144 3.28698328108264e-47 *** df.mm.trans2:probe2 0.142446680007891 0.0431293557536337 3.30277783006043 0.000993236783265132 *** df.mm.trans2:probe3 0.0118638370707293 0.0431293557536337 0.275075684842099 0.783318221268457 df.mm.trans2:probe4 0.0646428206762502 0.0431293557536337 1.49881257316958 0.134256228456981 df.mm.trans2:probe5 0.0409993331874714 0.0431293557536337 0.950613160596936 0.342043839681772 df.mm.trans2:probe6 0.103037281061547 0.0431293557536337 2.38902898643151 0.0170882761171971 * df.mm.trans3:probe2 -0.978556446425213 0.0431293557536337 -22.6888723312953 2.53886106364266e-91 *** df.mm.trans3:probe3 -0.878622072187389 0.0431293557536337 -20.3717875408645 9.19900025407708e-77 *** df.mm.trans3:probe4 -0.893057944037236 0.0431293557536337 -20.7064985885395 7.84756154137191e-79 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.93158748019106 0.162202872364145 24.2387044254349 2.56677720536244e-101 *** df.mm.trans1 0.197331067901252 0.145201764028234 1.35901288267325 0.174466754962076 df.mm.trans2 0.184661854981346 0.130455796604027 1.41551283874224 0.157247655399155 df.mm.exp2 0.347792255143421 0.175305326662299 1.98392291760417 0.0475534106219823 * df.mm.exp3 0.126107778779497 0.175305326662299 0.719360792855003 0.472096499937569 df.mm.exp4 0.195350311383340 0.175305326662299 1.11434327240754 0.265415277798952 df.mm.exp5 0.116320662764397 0.175305326662299 0.6635318217596 0.507151839022975 df.mm.exp6 0.322200009574553 0.175305326662299 1.83793622081561 0.0663855649683865 . df.mm.exp7 -0.120418758185278 0.175305326662299 -0.686908723642194 0.492308773926209 df.mm.exp8 0.0128406191703150 0.175305326662299 0.0732471706068045 0.941624930756731 df.mm.trans1:exp2 -0.339557317312234 0.169079470942266 -2.00827052166598 0.0448987037596083 * df.mm.trans2:exp2 -0.237280646860863 0.138912960369178 -1.70812461436471 0.087941632499228 . df.mm.trans1:exp3 -0.117066102917499 0.169079470942266 -0.69237325066786 0.488873052540996 df.mm.trans2:exp3 -0.0838468443660869 0.138912960369178 -0.603592667978955 0.546259338864287 df.mm.trans1:exp4 -0.222994750705420 0.169079470942266 -1.31887537536455 0.187530286626565 df.mm.trans2:exp4 -0.0831109831398043 0.138912960369178 -0.598295385246466 0.549786219966848 df.mm.trans1:exp5 -0.104293350364111 0.169079470942266 -0.616830356653548 0.537495201176143 df.mm.trans2:exp5 0.00249932743956483 0.138912960369178 0.0179920392807307 0.985649001999434 df.mm.trans1:exp6 -0.271426146372287 0.169079470942266 -1.60531698413563 0.108758163158336 df.mm.trans2:exp6 -0.259744064326213 0.138912960369178 -1.86983319364811 0.0618160086147217 . df.mm.trans1:exp7 0.0232203961434704 0.169079470942266 0.137334213397197 0.890795882438079 df.mm.trans2:exp7 0.0385199187468264 0.138912960369178 0.277295355627402 0.781613971903903 df.mm.trans1:exp8 -0.097903351116838 0.169079470942266 -0.579037482026828 0.562701871858684 df.mm.trans2:exp8 0.0644863942334017 0.138912960369178 0.464221582075721 0.642595921521911 df.mm.trans1:probe2 0.0348534591202118 0.0926086402461186 0.376352131157358 0.706739607530152 df.mm.trans1:probe3 -0.0983721739234578 0.0926086402461185 -1.06223537741211 0.288400221255198 df.mm.trans1:probe4 0.00837196381320123 0.0926086402461186 0.090401541270358 0.927987288906976 df.mm.trans1:probe5 0.0548224211969644 0.0926086402461185 0.591979550193883 0.554005879794929 df.mm.trans1:probe6 0.089140030888642 0.0926086402461186 0.96254551035132 0.336021883091382 df.mm.trans1:probe7 0.0478561953861579 0.0926086402461186 0.516757348547331 0.605446468137341 df.mm.trans1:probe8 -0.0343150410175521 0.0926086402461185 -0.370538223284089 0.711064540111126 df.mm.trans1:probe9 0.0871027764969454 0.0926086402461186 0.940546975589527 0.347177418900004 df.mm.trans1:probe10 0.0249250244549758 0.0926086402461185 0.269143617579683 0.78787791722812 df.mm.trans1:probe11 0.000163665154645495 0.0926086402461186 0.00176727737509735 0.998590290387932 df.mm.trans1:probe12 0.0412181533054167 0.0926086402461185 0.445078916998182 0.656364652116187 df.mm.trans1:probe13 0.0154755243456431 0.0926086402461186 0.167106700892217 0.867321844010711 df.mm.trans1:probe14 -0.0989585088634813 0.0926086402461185 -1.06856669745379 0.285537840365244 df.mm.trans1:probe15 0.169309677740010 0.0926086402461186 1.82822766094016 0.0678305107679956 . df.mm.trans1:probe16 -0.00917007360622532 0.0926086402461185 -0.0990196333933287 0.92114368155382 df.mm.trans1:probe17 -0.0119385048439835 0.0926086402461186 -0.128913509714164 0.897453517428466 df.mm.trans1:probe18 0.0132610251428834 0.0926086402461186 0.143194253879991 0.886167294362514 df.mm.trans1:probe19 0.0501959612200941 0.0926086402461185 0.542022440743027 0.587930825746334 df.mm.trans1:probe20 -0.0109261376217004 0.0926086402461186 -0.117981838332394 0.90610712012202 df.mm.trans1:probe21 0.0713341148490419 0.0926086402461185 0.770274940431724 0.441329333618708 df.mm.trans1:probe22 -0.0230323340619626 0.0926086402461185 -0.248706103455913 0.803642166376136 df.mm.trans1:probe23 0.00327381804924795 0.0926086402461185 0.0353511080666705 0.971807236031156 df.mm.trans1:probe24 0.0711860829702814 0.0926086402461185 0.768676473178916 0.442277450352362 df.mm.trans1:probe25 0.0423372205603936 0.0926086402461186 0.457162749046713 0.647659144349115 df.mm.trans1:probe26 0.111466781656765 0.0926086402461185 1.20363263471453 0.22903301975523 df.mm.trans1:probe27 0.0903235967961238 0.0926086402461186 0.975325807139355 0.329648268537357 df.mm.trans1:probe28 0.0888268587044538 0.0926086402461185 0.959163836855673 0.337721547821065 df.mm.trans1:probe29 0.0305672870776159 0.0926086402461185 0.33006949455666 0.741420607546139 df.mm.trans1:probe30 0.0682922583040017 0.0926086402461186 0.737428582500583 0.461044815067394 df.mm.trans1:probe31 -0.0198500851077604 0.0926086402461186 -0.214343770246560 0.830325228818639 df.mm.trans1:probe32 -0.038774374393824 0.0926086402461186 -0.418690678221562 0.675537348125464 df.mm.trans2:probe2 -0.123315275674967 0.0926086402461185 -1.33157419596316 0.183321246862235 df.mm.trans2:probe3 -0.0159198780519125 0.0926086402461185 -0.17190488932354 0.863549107850439 df.mm.trans2:probe4 -0.0783049324602964 0.0926086402461185 -0.845546724929679 0.398019692197679 df.mm.trans2:probe5 0.0246185777429938 0.0926086402461185 0.265834566597317 0.790424604486832 df.mm.trans2:probe6 -0.0831115897456167 0.0926086402461185 -0.897449628077226 0.369707619409312 df.mm.trans3:probe2 -0.120218169288494 0.0926086402461185 -1.29813124314319 0.194558969845182 df.mm.trans3:probe3 -0.000998941647813743 0.0926086402461185 -0.0107867003031136 0.991395901955532 df.mm.trans3:probe4 0.0105138805184465 0.0926086402461185 0.113530233145683 0.909634309194062