fitVsDatCorrelation=0.91304349141592 cont.fitVsDatCorrelation=0.263782814678137 fstatistic=3637.84750269372,52,692 cont.fstatistic=639.490336727831,52,692 residuals=-0.97755189141395,-0.173397487457710,-0.00286004056333907,0.158155731790144,1.19152259245486 cont.residuals=-1.17313667940985,-0.438320224867268,-0.151173027894009,0.248619505201929,2.27065779577169 predictedValues: Include Exclude Both Lung 57.5735524950163 131.500682793687 67.3854280128021 cerebhem 63.5671934885035 88.848951663706 68.5052105835041 cortex 58.5574334903087 75.42806385121 67.3867512665184 heart 74.195594663305 89.0825937840205 95.1188987738902 kidney 70.4704231457426 131.749267974846 79.3332027283408 liver 205.044333415237 101.492340435353 468.386946763255 stomach 66.379067995022 87.6979489379794 71.2303019993489 testicle 215.45951789135 100.561685598526 589.820047554595 diffExp=-73.927130298671,-25.2817581752025,-16.8706303609012,-14.8869991207155,-61.2788448291034,103.551992979884,-21.3188809429574,114.897832292824 diffExpScore=73.4021040547349 diffExp1.5=-1,0,0,0,-1,1,0,1 diffExp1.5Score=4 diffExp1.4=-1,0,0,0,-1,1,0,1 diffExp1.4Score=4 diffExp1.3=-1,-1,0,0,-1,1,-1,1 diffExp1.3Score=2 diffExp1.2=-1,-1,-1,-1,-1,1,-1,1 diffExp1.2Score=1.6 cont.predictedValues: Include Exclude Both Lung 82.7255155696297 107.055867310821 85.8594160206895 cerebhem 90.8586236909255 96.6755047448274 79.881084053724 cortex 104.194227961080 102.125279173991 74.5677838857898 heart 105.232758786995 100.549123118641 71.4167711332312 kidney 95.7554860748526 96.5988014657966 96.3512498031231 liver 97.8265425060024 105.777101898687 105.554152661871 stomach 99.4172241037161 79.9616076263466 72.8219543971961 testicle 104.881090768995 69.5587319954013 70.6011573623999 cont.diffExp=-24.3303517411917,-5.81688105390188,2.06894878708893,4.68363566835428,-0.84331539094407,-7.950559392685,19.4556164773695,35.3223587735939 cont.diffExpScore=4.25917765030323 cont.diffExp1.5=0,0,0,0,0,0,0,1 cont.diffExp1.5Score=0.5 cont.diffExp1.4=0,0,0,0,0,0,0,1 cont.diffExp1.4Score=0.5 cont.diffExp1.3=0,0,0,0,0,0,0,1 cont.diffExp1.3Score=0.5 cont.diffExp1.2=-1,0,0,0,0,0,1,1 cont.diffExp1.2Score=1.5 tran.correlation=0.00599496947320453 cont.tran.correlation=-0.434635507722652 tran.covariance=0.00704226074772384 cont.tran.covariance=-0.00542056947358261 tran.mean=101.100540726488 cont.tran.mean=96.1995929247944 weightedLogRatios: wLogRatio Lung -3.6887181886523 cerebhem -1.44634833829211 cortex -1.06245735588217 heart -0.804239253526131 kidney -2.85826136114806 liver 3.49624568133957 stomach -1.20726986700626 testicle 3.80373895753246 cont.weightedLogRatios: wLogRatio Lung -1.17165981590362 cerebhem -0.281751896646683 cortex 0.0929862719133912 heart 0.210951080959661 kidney -0.0400381648639466 liver -0.36117515209501 stomach 0.977921603784937 testicle 1.82639131095209 varWeightedLogRatios=7.43134663451372 cont.varWeightedLogRatios=0.818993865728056 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.15468567346884 0.154351402582235 26.9170581152013 9.53937026751604e-110 *** df.mm.trans1 -0.524815276786092 0.138630131716773 -3.78572299028259 0.000166561972082097 *** df.mm.trans2 0.609961200219596 0.127489233811981 4.7844134126585 2.09713138152886e-06 *** df.mm.exp2 -0.309521097274987 0.174628677522485 -1.77245285062148 0.0767593220662632 . df.mm.exp3 -0.538907537967006 0.174628677522485 -3.0860196939739 0.00210947059143633 ** df.mm.exp4 -0.480505493539124 0.174628677522485 -2.75158410609423 0.00608599638423662 ** df.mm.exp5 0.0407904292778430 0.174628677522485 0.233583795379719 0.815377190228029 df.mm.exp6 -0.927731770542283 0.174628677522485 -5.31259689819749 1.45869160326953e-07 *** df.mm.exp7 -0.31828459534444 0.174628677522485 -1.82263646418246 0.0687898627426826 . df.mm.exp8 -1.11791967527056 0.174628677522485 -6.40169582184814 2.83447739428333e-10 *** df.mm.trans1:exp2 0.408555304700917 0.167194229651667 2.44359692049243 0.0147904438547604 * df.mm.trans2:exp2 -0.0825531910910769 0.145523897935404 -0.567282709316384 0.570706097455193 df.mm.trans1:exp3 0.555852275466009 0.167194229651667 3.32459006883236 0.000932180870361865 *** df.mm.trans2:exp3 -0.0169251006017816 0.145523897935404 -0.116304612794899 0.907444873394825 df.mm.trans1:exp4 0.73414696665157 0.167194229651667 4.39098268032991 1.30491958823363e-05 *** df.mm.trans2:exp4 0.0910574090202904 0.145523897935404 0.625721344137644 0.531704039558521 df.mm.trans1:exp5 0.161339358481623 0.167194229651667 0.964981619388167 0.334891118825943 df.mm.trans2:exp5 -0.03890184199173 0.145523897935404 -0.267322704680423 0.789300356859481 df.mm.trans1:exp6 2.19789468267801 0.167194229651667 13.1457568078582 2.14249375677970e-35 *** df.mm.trans2:exp6 0.66870305853372 0.145523897935404 4.59514257122599 5.13944345358549e-06 *** df.mm.trans1:exp7 0.460603056905235 0.167194229651667 2.75489804800594 0.00602536956858245 ** df.mm.trans2:exp7 -0.0868289367549093 0.145523897935404 -0.596664451590291 0.550926574126147 df.mm.trans1:exp8 2.43762941097179 0.167194229651667 14.5796264383667 3.48859412220112e-42 *** df.mm.trans2:exp8 0.849678957577693 0.145523897935404 5.83875892298359 8.08722763673407e-09 *** df.mm.trans1:probe2 -0.113828042239400 0.0835971148258334 -1.36162644460337 0.173758939566312 df.mm.trans1:probe3 0.90189732252184 0.0835971148258334 10.7886178177423 3.45146269149013e-25 *** df.mm.trans1:probe4 1.11741147117461 0.0835971148258335 13.366627227538 2.04728646184613e-36 *** df.mm.trans1:probe5 0.505974467768781 0.0835971148258334 6.05253505247077 2.33642312879605e-09 *** df.mm.trans1:probe6 0.0416691789720039 0.0835971148258335 0.498452357582168 0.618323629162773 df.mm.trans1:probe7 0.691610784997227 0.0835971148258334 8.27314179966775 6.69503552557443e-16 *** df.mm.trans1:probe8 0.562258415841603 0.0835971148258335 6.72581125572353 3.65957584337903e-11 *** df.mm.trans1:probe9 0.657429643954217 0.0835971148258334 7.86426236508171 1.42812406467377e-14 *** df.mm.trans1:probe10 0.0378887392895764 0.0835971148258335 0.453230226527721 0.650525048546962 df.mm.trans1:probe11 0.888514696993972 0.0835971148258335 10.6285330402264 1.52099247107337e-24 *** df.mm.trans1:probe12 0.899566002009987 0.0835971148258335 10.7607302463027 4.47400216022728e-25 *** df.mm.trans1:probe13 0.776886052087122 0.0835971148258334 9.29321608413985 1.91330479051845e-19 *** df.mm.trans1:probe14 0.859966352276866 0.0835971148258335 10.2870338775270 3.41524757738189e-23 *** df.mm.trans1:probe15 0.924227825861072 0.0835971148258334 11.0557383204745 2.80785262844066e-26 *** df.mm.trans1:probe16 0.719432974758226 0.0835971148258334 8.60595459851809 5.06644862122842e-17 *** df.mm.trans1:probe17 0.428440688582401 0.0835971148258335 5.12506549388715 3.8631753524456e-07 *** df.mm.trans1:probe18 0.59116144977965 0.0835971148258335 7.07155325888074 3.75595069430155e-12 *** df.mm.trans1:probe19 0.289837868721498 0.0835971148258335 3.46707980682524 0.000558682993491962 *** df.mm.trans1:probe20 -0.0270869202868457 0.0835971148258334 -0.324017405903047 0.746022764157902 df.mm.trans1:probe21 -0.0150832211653362 0.0835971148258335 -0.180427532657803 0.856869766143248 df.mm.trans1:probe22 -0.158353064371122 0.0835971148258335 -1.89424078451792 0.0586097783800733 . df.mm.trans2:probe2 0.234891313910528 0.0835971148258335 2.80980168274828 0.0050972067947503 ** df.mm.trans2:probe3 0.00136051157261276 0.0835971148258335 0.0162746235375139 0.987019993326656 df.mm.trans2:probe4 0.096701341512237 0.0835971148258335 1.15675453290110 0.247771821681461 df.mm.trans2:probe5 0.230463410328515 0.0835971148258335 2.75683450091147 0.00599019611266554 ** df.mm.trans2:probe6 0.465869955046115 0.0835971148258335 5.57279944429554 3.59310418150105e-08 *** df.mm.trans3:probe2 -0.176224960229033 0.0835971148258335 -2.10802682121483 0.0353876522056626 * df.mm.trans3:probe3 0.0880165483805636 0.0835971148258335 1.05286586222428 0.292769968243299 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.26549978811245 0.365036515073025 11.6851317936211 6.45601670502743e-29 *** df.mm.trans1 -0.0673893026680512 0.327856173117987 -0.205545321984221 0.837206542307939 df.mm.trans2 0.404014424063194 0.301508278133469 1.33997788241273 0.180692360712160 df.mm.exp2 0.0639580442027587 0.412991672302146 0.154865215190022 0.876972734816983 df.mm.exp3 0.324580810747035 0.412991672302146 0.78592580072552 0.432179981719838 df.mm.exp4 0.362120663412607 0.412991672302146 0.876823160607653 0.380886981915802 df.mm.exp5 -0.0718037575499209 0.412991672302146 -0.173862482867182 0.86202438527691 df.mm.exp6 -0.0508617602688151 0.412991672302146 -0.123154445186015 0.902020557624413 df.mm.exp7 0.0566868655770814 0.412991672302146 0.137259100797580 0.890865922736458 df.mm.exp8 0.00178451487161583 0.412991672302146 0.00432094638051267 0.996553639636853 df.mm.trans1:exp2 0.0298185823144285 0.395409422339697 0.0754119164333199 0.939908773571585 df.mm.trans2:exp2 -0.165948808277493 0.344159726918455 -0.482185436870749 0.62982669427213 df.mm.trans1:exp3 -0.0938521629294959 0.395409422339697 -0.237354391744533 0.81245212086344 df.mm.trans2:exp3 -0.371731346523341 0.344159726918455 -1.08011285879309 0.280468225360875 df.mm.trans1:exp4 -0.121474102388644 0.395409422339697 -0.307210945227009 0.758775242191399 df.mm.trans2:exp4 -0.424825090683482 0.344159726918455 -1.23438350700499 0.217479043333702 df.mm.trans1:exp5 0.218073593670415 0.395409422339697 0.551513396873653 0.581459823322944 df.mm.trans2:exp5 -0.0309807310975367 0.344159726918455 -0.0900184672243109 0.928298587540315 df.mm.trans1:exp6 0.218529610135481 0.395409422339697 0.552666673551703 0.580670148331259 df.mm.trans2:exp6 0.0388450054877083 0.344159726918455 0.112869119915684 0.910167052749808 df.mm.trans1:exp7 0.127110427675735 0.395409422339697 0.321465348305571 0.747954839500913 df.mm.trans2:exp7 -0.348491073367559 0.344159726918455 -1.01258527977078 0.311612206103262 df.mm.trans1:exp8 0.235514638571959 0.395409422339697 0.595622221590936 0.551622373060322 df.mm.trans2:exp8 -0.432963877087548 0.344159726918455 -1.25803178937940 0.208804581922623 df.mm.trans1:probe2 0.378945245437071 0.197704711169848 1.91672339619423 0.0556846358360587 . df.mm.trans1:probe3 0.287842609120174 0.197704711169848 1.45592185141652 0.145867746889789 df.mm.trans1:probe4 0.214576487094244 0.197704711169848 1.08533825938979 0.278149815998773 df.mm.trans1:probe5 0.125124796816721 0.197704711169848 0.632887279601681 0.527016333657286 df.mm.trans1:probe6 0.444712912044604 0.197704711169848 2.24937943771381 0.0248019573543851 * df.mm.trans1:probe7 0.355377080925321 0.197704711169848 1.79751447915683 0.0726898291464391 . df.mm.trans1:probe8 0.160542538515597 0.197704711169848 0.812031931690666 0.417052548953297 df.mm.trans1:probe9 0.110151852936058 0.197704711169848 0.557153404611719 0.577602778651009 df.mm.trans1:probe10 0.188295188360718 0.197704711169848 0.95240617811557 0.34122356710599 df.mm.trans1:probe11 0.221459728937004 0.197704711169848 1.12015402984883 0.263036743375456 df.mm.trans1:probe12 0.301067401015347 0.197704711169848 1.52281348903567 0.128262199221372 df.mm.trans1:probe13 0.365730442701452 0.197704711169848 1.84988228422767 0.0647566629892662 . df.mm.trans1:probe14 0.286677183582128 0.197704711169848 1.45002707262673 0.147504038416278 df.mm.trans1:probe15 0.143363946815862 0.197704711169848 0.725141783256232 0.468610184408412 df.mm.trans1:probe16 -0.0350788227427981 0.197704711169848 -0.177430383602047 0.859222264719645 df.mm.trans1:probe17 0.330532082516178 0.197704711169848 1.67184727445477 0.0950065117597432 . df.mm.trans1:probe18 0.37316156785186 0.197704711169848 1.88746927497988 0.0595154627119645 . df.mm.trans1:probe19 0.202155238225607 0.197704711169848 1.02251098129845 0.306896435706175 df.mm.trans1:probe20 0.392295099765331 0.197704711169848 1.98424760565422 0.0476228527209701 * df.mm.trans1:probe21 0.315340141682995 0.197704711169848 1.5950057022773 0.111167540937255 df.mm.trans1:probe22 0.273167295284352 0.197704711169848 1.38169340360167 0.167511758663577 df.mm.trans2:probe2 0.200163777553448 0.197704711169848 1.01243807681187 0.31168250167913 df.mm.trans2:probe3 -0.256966183981472 0.197704711169848 -1.29974739833444 0.194120350106669 df.mm.trans2:probe4 0.0477276394534157 0.197704711169848 0.241408710854709 0.809309875873594 df.mm.trans2:probe5 0.168972768496581 0.197704711169848 0.854672443042678 0.393028297016589 df.mm.trans2:probe6 -0.125368507679457 0.197704711169848 -0.634119980943464 0.526212079416289 df.mm.trans3:probe2 -0.0609764695082804 0.197704711169848 -0.308421934649273 0.7578541136119 df.mm.trans3:probe3 -0.387264475282714 0.197704711169848 -1.95880246348816 0.0505371575961345 .