fitVsDatCorrelation=0.936326063251743 cont.fitVsDatCorrelation=0.248092981185834 fstatistic=10500.9011777277,59,853 cont.fstatistic=1367.04975327785,59,853 residuals=-0.632287086794732,-0.0838045981445512,0.00139295821414048,0.0926887980608477,0.801856398010143 cont.residuals=-0.910567719304322,-0.332872114696014,-0.0755597992334008,0.281207308348013,1.57051856592959 predictedValues: Include Exclude Both Lung 64.7572924206302 220.07667143437 57.7775868976593 cerebhem 77.2866619712878 160.262163403377 65.2944495928519 cortex 98.5568302015275 192.342274079382 69.3093061613157 heart 94.2291295576754 350.662701511307 66.0248068243827 kidney 70.1238122406143 198.015269115012 58.1375015106007 liver 65.0137225312582 204.513246471544 56.6665233240798 stomach 57.6948651339593 231.528768351933 53.1570967737074 testicle 61.2640627554331 204.784110688705 54.3284809186599 diffExp=-155.319379013740,-82.9755014320889,-93.7854438778545,-256.433571953631,-127.891456874397,-139.499523940286,-173.833903217974,-143.520047933272 diffExpScore=0.99914839899352 diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.888888888888889 diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.888888888888889 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 82.9401908610016 85.854287584968 87.1535550462332 cerebhem 82.0890657603004 89.3205226524312 71.6494005733354 cortex 80.5153041908629 83.2995551603197 88.9313256216065 heart 89.6739268644397 75.1657720970658 107.846084056321 kidney 78.0355901564226 99.614886815608 83.7047396772483 liver 86.934705109334 110.018293265532 89.0139040108081 stomach 87.0974372057455 80.99416657274 76.3866930867688 testicle 78.3438103290583 79.8767899457573 87.948625718034 cont.diffExp=-2.91409672396637,-7.23145689213071,-2.78425096945679,14.5081547673739,-21.5792966591854,-23.0835881561984,6.10327063300547,-1.53297961669897 cont.diffExpScore=2.01793295578083 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,-1,-1,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.329853855325566 cont.tran.correlation=-0.106571541929554 tran.covariance=0.0102992810693384 cont.tran.covariance=-0.000901248987000963 tran.mean=146.944473866751 cont.tran.mean=85.6108940357242 weightedLogRatios: wLogRatio Lung -5.85034314500076 cerebhem -3.43653381631727 cortex -3.29303723146396 heart -6.83694559749477 kidney -4.95092655040273 liver -5.44093067299645 stomach -6.60020708744804 testicle -5.69420102129064 cont.weightedLogRatios: wLogRatio Lung -0.153161729443257 cerebhem -0.375699239008970 cortex -0.149767201193768 heart 0.777930917264196 kidney -1.09359086188497 liver -1.0792245345084 stomach 0.321891963558741 testicle -0.0846988452873735 varWeightedLogRatios=1.73458784650726 cont.varWeightedLogRatios=0.40602305465187 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.56016090996355 0.0797288659964847 69.7383669072717 0 *** df.mm.trans1 -1.66867087964105 0.0688518984408647 -24.2356553330804 3.90251644550050e-99 *** df.mm.trans2 -0.0604360122684127 0.0608303410674738 -0.993517563897534 0.320739538614826 df.mm.exp2 -0.262596026790203 0.0782472685150547 -3.35597691489614 0.000825801402152907 *** df.mm.exp3 0.103309235268883 0.0782472685150547 1.32029190576801 0.187091734835865 df.mm.exp4 0.707502249344606 0.0782472685150547 9.04187791818551 1.01700359387918e-18 *** df.mm.exp5 -0.0322257302289609 0.0782472685150547 -0.411844794591912 0.680556653591635 df.mm.exp6 -0.0499738871308764 0.0782472685150547 -0.638666219016469 0.523211532782323 df.mm.exp7 0.0185993127373872 0.0782472685150547 0.237699195005238 0.812171489280799 df.mm.exp8 -0.0659202553117671 0.0782472685150547 -0.842460785696105 0.399766227051489 df.mm.trans1:exp2 0.43947110011673 0.0723255774391188 6.07628885488902 1.85203406762515e-09 *** df.mm.trans2:exp2 -0.0545689697742349 0.0534159789775339 -1.02158512899644 0.307266938229615 df.mm.trans1:exp3 0.316677784399667 0.0723255774391188 4.37850336786091 1.34334366720870e-05 *** df.mm.trans2:exp3 -0.238008764985890 0.0534159789775339 -4.45575967232564 9.47331240203352e-06 *** df.mm.trans1:exp4 -0.332419203085981 0.0723255774391188 -4.59615000468956 4.95382930142258e-06 *** df.mm.trans2:exp4 -0.241653444414185 0.0534159789775339 -4.5239916788911 6.9280573060237e-06 *** df.mm.trans1:exp5 0.111841837688276 0.0723255774391188 1.54636632915127 0.122387025995224 df.mm.trans2:exp5 -0.0734061174652964 0.0534159789775339 -1.37423517962987 0.169729638271954 df.mm.trans1:exp6 0.0539259321153569 0.0723255774391188 0.745599745273378 0.456114639541156 df.mm.trans2:exp6 -0.0233693567200283 0.0534159789775339 -0.437497489840206 0.6618613421186 df.mm.trans1:exp7 -0.134077454157290 0.0723255774391187 -1.85380412994492 0.0641122421240493 . df.mm.trans2:exp7 0.0321288302489779 0.0534159789775339 0.601483504823357 0.547677809339237 df.mm.trans1:exp8 0.0104673558956955 0.0723255774391187 0.144725507439005 0.884961786532405 df.mm.trans2:exp8 -0.00609943121845478 0.0534159789775339 -0.114187389901065 0.909116118664095 df.mm.trans1:probe2 0.2111799248295 0.0495179378099901 4.26471566000665 2.22525825630230e-05 *** df.mm.trans1:probe3 -0.0964436364308428 0.0495179378099901 -1.94765050194367 0.0517845501654406 . df.mm.trans1:probe4 0.289175381503731 0.0495179378099901 5.83981066847641 7.42653938033603e-09 *** df.mm.trans1:probe5 0.471962416012956 0.0495179378099901 9.53114036824326 1.56705219145114e-20 *** df.mm.trans1:probe6 0.273512494590172 0.0495179378099901 5.52350333407849 4.4136113640419e-08 *** df.mm.trans1:probe7 0.418522970613349 0.0495179378099901 8.45194669090023 1.22986512902053e-16 *** df.mm.trans1:probe8 0.434922510231149 0.0495179378099901 8.78313050717158 8.60492811971016e-18 *** df.mm.trans1:probe9 -0.0453906850243582 0.0495179378099901 -0.916651359726067 0.359584442295635 df.mm.trans1:probe10 0.570381822612539 0.0495179378099901 11.5186909600558 1.23007816932824e-28 *** df.mm.trans1:probe11 0.476544808645825 0.0495179378099901 9.62368042212136 6.97978135433262e-21 *** df.mm.trans1:probe12 0.748256491408755 0.0495179378099901 15.110816897908 6.87952042389668e-46 *** df.mm.trans1:probe13 0.77151405490269 0.0495179378099901 15.5804964629815 2.36780479081199e-48 *** df.mm.trans1:probe14 0.430228610035709 0.0495179378099901 8.68833859129149 1.85803382839847e-17 *** df.mm.trans1:probe15 0.421292890097259 0.0495179378099901 8.50788438956891 7.89405491214097e-17 *** df.mm.trans1:probe16 0.587656160498158 0.0495179378099901 11.8675410666960 3.56592106688651e-30 *** df.mm.trans1:probe17 0.367697180544402 0.0495179378099901 7.42553500421054 2.72293403575593e-13 *** df.mm.trans1:probe18 0.385866374725682 0.0495179378099901 7.79245646711553 1.90847319825625e-14 *** df.mm.trans1:probe19 0.59438457335893 0.0495179378099901 12.0034193596611 8.79975815917418e-31 *** df.mm.trans1:probe20 0.328964904422901 0.0495179378099901 6.64334822837742 5.45963412046388e-11 *** df.mm.trans1:probe21 0.652358816514996 0.0495179378099901 13.1741919265342 3.26818557064355e-36 *** df.mm.trans1:probe22 0.640413157421752 0.0495179378099901 12.9329529004043 4.57923051408565e-35 *** df.mm.trans2:probe2 -0.45833847005948 0.0495179378099901 -9.25600883902341 1.67308216153227e-19 *** df.mm.trans2:probe3 -0.257970869371427 0.0495179378099901 -5.20964484347694 2.37380799259152e-07 *** df.mm.trans2:probe4 -0.470496606409954 0.0495179378099901 -9.50153878005463 2.02709800446276e-20 *** df.mm.trans2:probe5 -0.331579806991546 0.0495179378099901 -6.69615540663025 3.87840930123446e-11 *** df.mm.trans2:probe6 -0.173596735762822 0.0495179378099901 -3.50573435487048 0.000479092178271295 *** df.mm.trans3:probe2 0.0295341718652772 0.0495179378099901 0.596433800991583 0.551043712111548 df.mm.trans3:probe3 0.430620485312594 0.0495179378099901 8.69625239574734 1.74283533226877e-17 *** df.mm.trans3:probe4 -0.0392255684218419 0.0495179378099901 -0.792148666860037 0.428494234128586 df.mm.trans3:probe5 0.255501663275852 0.0495179378099901 5.1597799620869 3.07666695544430e-07 *** df.mm.trans3:probe6 0.26122698323016 0.0495179378099901 5.27540109268158 1.68060743854460e-07 *** df.mm.trans3:probe7 0.326956060263820 0.0495179378099901 6.60278021912813 7.08868131104944e-11 *** df.mm.trans3:probe8 0.421037274030425 0.0495179378099901 8.50272229926106 8.22457395492724e-17 *** df.mm.trans3:probe9 0.50184082097686 0.0495179378099901 10.1345258540960 7.20298415299349e-23 *** df.mm.trans3:probe10 0.0679772343203814 0.0495179378099901 1.37277999300422 0.170181547111676 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.36195671946438 0.219963707181593 19.8303473575453 2.89040732495574e-72 *** df.mm.trans1 -0.0193460765005088 0.189955277028660 -0.101845428056152 0.918903301945824 df.mm.trans2 0.121298559926013 0.167824628672283 0.722769720306527 0.470019490712912 df.mm.exp2 0.225151594382534 0.215876132744242 1.04296659163003 0.297259258826219 df.mm.exp3 -0.0800736816270344 0.215876132744242 -0.370924199026213 0.710786089149292 df.mm.exp4 -0.267928758999649 0.215876132744242 -1.24112265489338 0.214901648981926 df.mm.exp5 0.128081301810349 0.215876132744242 0.593309228686678 0.553131501839199 df.mm.exp6 0.273911671449278 0.215876132744242 1.26883721682097 0.204845256912993 df.mm.exp7 0.122496366102386 0.215876132744242 0.567438208870975 0.570565801597688 df.mm.exp8 -0.138260279596789 0.215876132744242 -0.640461165573093 0.522044832856484 df.mm.trans1:exp2 -0.235466524772442 0.199538798636110 -1.18005383605547 0.238307783511491 df.mm.trans2:exp2 -0.185571844812423 0.147369169394062 -1.25923112395516 0.208291331702067 df.mm.trans1:exp3 0.0504012061451117 0.199538798636110 0.252588501532607 0.800647069679808 df.mm.trans2:exp3 0.0498653617283289 0.147369169394062 0.338370379186912 0.735167359347999 df.mm.trans1:exp4 0.345989059394744 0.19953879863611 1.73394378316223 0.0832893618238394 . df.mm.trans2:exp4 0.134973199210775 0.147369169394062 0.915884915181002 0.35998610746387 df.mm.trans1:exp5 -0.189036051139214 0.199538798636110 -0.947364885582732 0.343721176058266 df.mm.trans2:exp5 0.0205787887864364 0.147369169394062 0.139641071949107 0.888976551239635 df.mm.trans1:exp6 -0.22687410656672 0.19953879863611 -1.13699244516581 0.255860739040588 df.mm.trans2:exp6 -0.0259165459146395 0.147369169394062 -0.175861382819763 0.860444584131873 df.mm.trans1:exp7 -0.0735886622710642 0.19953879863611 -0.368793752263010 0.712372976697452 df.mm.trans2:exp7 -0.180770760488829 0.147369169394062 -1.22665250290888 0.220291705226409 df.mm.trans1:exp8 0.0812474890016086 0.19953879863611 0.407176396555218 0.68398059133491 df.mm.trans2:exp8 0.0660940725349618 0.147369169394062 0.448493214739018 0.653911183410895 df.mm.trans1:probe2 0.166467789226895 0.136614876388098 1.21851875599543 0.223363799386397 df.mm.trans1:probe3 0.188072121845704 0.136614876388098 1.37665916639579 0.168978870090865 df.mm.trans1:probe4 0.148689206180534 0.136614876388098 1.08838224731935 0.276733805384809 df.mm.trans1:probe5 0.168412870782266 0.136614876388098 1.23275645548173 0.218006284514457 df.mm.trans1:probe6 0.0126666115794544 0.136614876388098 0.0927176594111968 0.926149638352022 df.mm.trans1:probe7 -0.00376507057327 0.136614876388098 -0.0275597407311200 0.978019737951626 df.mm.trans1:probe8 -0.123496916647606 0.136614876388098 -0.903978541083429 0.366262003539991 df.mm.trans1:probe9 0.168940431310043 0.136614876388098 1.23661811785500 0.216569259782766 df.mm.trans1:probe10 0.0731859809508054 0.136614876388098 0.535710186809357 0.592298510264518 df.mm.trans1:probe11 0.140895541663053 0.136614876388098 1.03133381508756 0.302676632401900 df.mm.trans1:probe12 0.140254969769000 0.136614876388098 1.02664492679816 0.304878728365037 df.mm.trans1:probe13 0.171981845488312 0.136614876388098 1.25888080445751 0.208417795671334 df.mm.trans1:probe14 0.141554046592852 0.136614876388098 1.03615397045577 0.300423960583694 df.mm.trans1:probe15 0.123022656162297 0.136614876388098 0.900507026868817 0.368104649349692 df.mm.trans1:probe16 0.11870734890461 0.136614876388098 0.868919637766123 0.385135502471421 df.mm.trans1:probe17 0.171073831846785 0.136614876388098 1.25223428348165 0.210827736192870 df.mm.trans1:probe18 0.264950977609456 0.136614876388098 1.93940063201300 0.0527819886299011 . df.mm.trans1:probe19 0.0238311690002554 0.136614876388098 0.174440512119305 0.861560671002714 df.mm.trans1:probe20 0.00321420000617294 0.136614876388098 0.0235274524352822 0.981235043194502 df.mm.trans1:probe21 0.0854782470362406 0.136614876388098 0.625687694460245 0.531687172464228 df.mm.trans1:probe22 0.232153758718049 0.136614876388098 1.69933000604226 0.0896216274648655 . df.mm.trans2:probe2 -0.0739648954518212 0.136614876388098 -0.541411721822302 0.588365202450704 df.mm.trans2:probe3 0.0329764481447239 0.136614876388098 0.241382556692024 0.809316642187373 df.mm.trans2:probe4 -0.130072548165180 0.136614876388098 -0.952111158053296 0.341310315579411 df.mm.trans2:probe5 -0.229295219398364 0.136614876388098 -1.67840593543398 0.093634157313144 . df.mm.trans2:probe6 -0.0893037938556949 0.136614876388098 -0.65369011206363 0.513487648302965 df.mm.trans3:probe2 0.115656851497298 0.136614876388098 0.846590463316288 0.397460873488872 df.mm.trans3:probe3 -0.0227623195234043 0.136614876388098 -0.166616697428622 0.867711141194823 df.mm.trans3:probe4 -0.0622627407160372 0.136614876388098 -0.455753738993695 0.648683070083634 df.mm.trans3:probe5 -0.0706832319961823 0.136614876388098 -0.517390447255421 0.605017765671222 df.mm.trans3:probe6 0.0601397058662847 0.136614876388098 0.440213448610374 0.659894043634317 df.mm.trans3:probe7 -0.00139814914709537 0.136614876388098 -0.0102342379106905 0.991836795139358 df.mm.trans3:probe8 -0.0411721502188727 0.136614876388098 -0.301373842347227 0.763202889994115 df.mm.trans3:probe9 -0.01163083701986 0.136614876388098 -0.0851359480560439 0.932173289188067 df.mm.trans3:probe10 0.0717364763883625 0.136614876388098 0.525100035113102 0.599650102836788