fitVsDatCorrelation=0.926381310830731 cont.fitVsDatCorrelation=0.269522917852219 fstatistic=9017.27467618996,54,738 cont.fstatistic=1367.40500649082,54,738 residuals=-0.684194370503059,-0.0908055533073936,-0.00135328404220484,0.0799238035004437,0.963415844359081 cont.residuals=-0.839619412582024,-0.328699117507604,-0.0992318679595511,0.297290323989525,1.67320288242100 predictedValues: Include Exclude Both Lung 71.0833911204567 71.946446038385 128.672842514914 cerebhem 70.7765748325473 69.27466226811 66.2618452027112 cortex 63.1642704187782 64.3707963595926 94.487636622307 heart 62.9091683384254 60.6067567204513 84.398738315374 kidney 74.8157965930526 69.1706045607948 88.2448541031962 liver 69.402192969521 67.6869397613643 88.6245776400912 stomach 71.7789010195956 63.1843035103686 85.476970468341 testicle 73.7817503637738 64.8546389123237 83.0127133027668 diffExp=-0.863054917928253,1.50191256443736,-1.20652594081437,2.30241161797411,5.64519203225777,1.71525320815668,8.59459750922707,8.9271114514501 diffExpScore=1.11366815242989 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 68.855758591225 81.0934796619031 85.6565457069758 cerebhem 71.8816708819426 77.62661757817 68.7348533427176 cortex 67.0434291110802 79.8560529853147 83.1493498008286 heart 70.5918826597297 94.4613883663099 82.8933716328827 kidney 74.1410924243566 89.7857077858529 73.4791514173694 liver 70.6613696111823 65.4459236295269 57.2493777289025 stomach 73.7123476555351 72.7749139278425 90.1151433422903 testicle 74.1703441341004 63.5380787503712 67.8904234725741 cont.diffExp=-12.2377210706781,-5.74494669622742,-12.8126238742345,-23.8695057065802,-15.6446153614963,5.21544598165535,0.937433727692579,10.6322653837292 cont.diffExpScore=1.59735401519659 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,-1,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,0,-1,-1,0,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.547388148629046 cont.tran.correlation=-0.203207810359191 tran.covariance=0.0020904726899303 cont.tran.covariance=-0.00114916542685622 tran.mean=68.0504496117213 cont.tran.mean=74.7275036096527 weightedLogRatios: wLogRatio Lung -0.0515304013273951 cerebhem 0.0911320222582254 cortex -0.0786215994815853 heart 0.153730071633372 kidney 0.335449809604962 liver 0.105791753615534 stomach 0.536898405573196 testicle 0.546368398802981 cont.weightedLogRatios: wLogRatio Lung -0.705690267670746 cerebhem -0.331658382825087 cortex -0.750743684974318 heart -1.28235807868936 kidney -0.842730914224324 liver 0.323534113304525 stomach 0.0549560485458574 testicle 0.65433233492761 varWeightedLogRatios=0.0593416954795624 cont.varWeightedLogRatios=0.432148096072217 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.34981729194288 0.086138640978767 38.8886712615841 7.72349008666363e-181 *** df.mm.trans1 0.679374822506016 0.0758804729185002 8.95322335742032 2.76951974857416e-18 *** df.mm.trans2 1.00744466462833 0.0684692678822212 14.7138226504964 3.66438708939964e-43 *** df.mm.exp2 0.621490383690118 0.0911806845263281 6.81603112455978 1.94612420651717e-11 *** df.mm.exp3 0.0794271914278153 0.0911806845263281 0.87109667842953 0.383984639027106 df.mm.exp4 0.128043175488188 0.0911806845263281 1.40427960322250 0.160656303807438 df.mm.exp5 0.388986976786046 0.0911806845263281 4.26611160912843 2.24795294018847e-05 *** df.mm.exp6 0.287899825320355 0.0911806845263281 3.15746505760466 0.00165625157145619 ** df.mm.exp7 0.288896825497822 0.0911806845263281 3.16839939290436 0.00159600928708875 ** df.mm.exp8 0.371763419335009 0.0911806845263281 4.07721680601842 5.05300073223977e-05 *** df.mm.trans1:exp2 -0.625816012337397 0.086005471137249 -7.27646746261885 8.78026404870387e-13 *** df.mm.trans2:exp2 -0.659333205421172 0.0703681127486582 -9.36977246748378 8.65100550081712e-20 *** df.mm.trans1:exp3 -0.197542102268106 0.086005471137249 -2.29685506812543 0.0219062162963204 * df.mm.trans2:exp3 -0.190689171077892 0.0703681127486582 -2.70988042210252 0.00688697673361178 ** df.mm.trans1:exp4 -0.250204972508854 0.086005471137249 -2.90917506991587 0.00373276515735735 ** df.mm.trans2:exp4 -0.299558828824526 0.0703681127486582 -4.25702519398942 2.33895867512174e-05 *** df.mm.trans1:exp5 -0.337811640355131 0.086005471137249 -3.92779245190164 9.37692661640542e-05 *** df.mm.trans2:exp5 -0.428333031228389 0.0703681127486582 -6.08703309634457 1.84822309450603e-09 *** df.mm.trans1:exp6 -0.31183506997471 0.086005471137249 -3.62575852270001 0.000307919000922933 *** df.mm.trans2:exp6 -0.348928614667238 0.0703681127486582 -4.95861834341849 8.81570661292269e-07 *** df.mm.trans1:exp7 -0.279159960946396 0.086005471137249 -3.24583956410061 0.00122390424128614 ** df.mm.trans2:exp7 -0.418762954603139 0.0703681127486582 -5.95103290746026 4.11625134024459e-09 *** df.mm.trans1:exp8 -0.334505714004615 0.086005471137249 -3.88935389320529 0.000109581788718786 *** df.mm.trans2:exp8 -0.475537015406379 0.0703681127486582 -6.75784807679736 2.84413239586559e-11 *** df.mm.trans1:probe2 0.484602163732696 0.050216376354347 9.6502814204105 7.83207010535864e-21 *** df.mm.trans1:probe3 0.0674242640294325 0.050216376354347 1.34267481894073 0.179790115648029 df.mm.trans1:probe4 -0.0372465334540742 0.050216376354347 -0.741720852003492 0.458492453781188 df.mm.trans1:probe5 0.0763557968083999 0.050216376354347 1.52053577640893 0.128804679279595 df.mm.trans1:probe6 -0.113883511612190 0.050216376354347 -2.26785602387123 0.0236262394115116 * df.mm.trans1:probe7 -0.033955455718131 0.050216376354347 -0.676182914484463 0.499136364914437 df.mm.trans1:probe8 0.326574400884239 0.050216376354347 6.503344617696 1.44763564439259e-10 *** df.mm.trans1:probe9 -0.0345255856457183 0.050216376354347 -0.687536380604046 0.491960795504396 df.mm.trans1:probe10 0.555221508199702 0.050216376354347 11.0565825037202 2.11174566648338e-26 *** df.mm.trans1:probe11 0.867474077947691 0.050216376354347 17.2747247198094 2.07670529394973e-56 *** df.mm.trans1:probe12 0.878531150151112 0.050216376354347 17.4949132918680 1.36168801239526e-57 *** df.mm.trans1:probe13 0.737159546716573 0.050216376354347 14.6796642894915 5.41294257237125e-43 *** df.mm.trans1:probe14 1.07130804599488 0.050216376354347 21.3338381574031 4.90624909404248e-79 *** df.mm.trans1:probe15 1.25808995738398 0.050216376354347 25.0533799672520 1.0136221905283e-100 *** df.mm.trans1:probe16 1.04198406068678 0.050216376354347 20.7498855220880 1.08007800931634e-75 *** df.mm.trans1:probe17 -0.16549008097572 0.050216376354347 -3.29554008054972 0.00102927290140268 ** df.mm.trans1:probe18 -0.166891387757401 0.050216376354347 -3.32344545491989 0.000932992747665873 *** df.mm.trans1:probe19 -0.0337775862310681 0.050216376354347 -0.672640853109747 0.501386352672506 df.mm.trans1:probe20 -0.195536740237488 0.050216376354347 -3.89388391662715 0.000107595021975934 *** df.mm.trans1:probe21 -0.137526798361037 0.050216376354347 -2.73868423700253 0.00631732435477958 ** df.mm.trans1:probe22 -0.110028195050077 0.050216376354347 -2.19108193457994 0.0287578161749941 * df.mm.trans2:probe2 -0.111086295996744 0.050216376354347 -2.21215276890698 0.0272616263219536 * df.mm.trans2:probe3 -0.207330297221589 0.050216376354347 -4.12873871580424 4.0641345744758e-05 *** df.mm.trans2:probe4 -0.217302056013486 0.050216376354347 -4.32731454934372 1.71739076114712e-05 *** df.mm.trans2:probe5 -0.285705701302371 0.050216376354347 -5.68949259274139 1.83748883159701e-08 *** df.mm.trans2:probe6 -0.0733147621815654 0.050216376354347 -1.45997715295558 0.144721894368765 df.mm.trans3:probe2 -0.274496225621702 0.050216376354347 -5.46626908490461 6.29213901892364e-08 *** df.mm.trans3:probe3 -0.0716942190476315 0.050216376354347 -1.42770594480430 0.153799516323095 df.mm.trans3:probe4 -0.121891269702010 0.050216376354347 -2.42732109624749 0.0154490624035508 * df.mm.trans3:probe5 -0.428736788687869 0.050216376354347 -8.5377882637833 7.76663790031715e-17 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.12539249842301 0.22027054050458 18.7287527827046 2.49125921662746e-64 *** df.mm.trans1 0.0752042956772725 0.194038733297652 0.387573627178396 0.698443340591107 df.mm.trans2 0.283451151089545 0.175087074430248 1.61891534261982 0.105892722540963 df.mm.exp2 0.219404513482410 0.233163867411638 0.940988481268686 0.347018661754298 df.mm.exp3 -0.0123428794294909 0.233163867411638 -0.0529365015536486 0.9577968266925 df.mm.exp4 0.210280446176327 0.233163867411638 0.901856915098636 0.36742711302235 df.mm.exp5 0.329123252272379 0.233163867411638 1.41155341059483 0.158502953373535 df.mm.exp6 0.214435685317124 0.233163867411638 0.919678026006275 0.358041537676332 df.mm.exp7 -0.0908173663449028 0.233163867411638 -0.389500171502002 0.697018553139164 df.mm.exp8 0.062838026405956 0.233163867411638 0.269501561727911 0.78761907368913 df.mm.trans1:exp2 -0.176397067747453 0.219930003520983 -0.802060041483262 0.422776413629224 df.mm.trans2:exp2 -0.263096694225491 0.179942730153531 -1.46211349578286 0.144135794285757 df.mm.trans1:exp3 -0.0143303768261367 0.219930003520983 -0.0651588077875397 0.948065203804741 df.mm.trans2:exp3 -0.00303400347844961 0.179942730153531 -0.0168609394547972 0.986552111286226 df.mm.trans1:exp4 -0.185379146124787 0.219930003520983 -0.842900664561214 0.399557094583937 df.mm.trans2:exp4 -0.0576918430337679 0.179942730153531 -0.320612246932921 0.748595015939382 df.mm.trans1:exp5 -0.25516718138961 0.219930003520983 -1.16021996682806 0.246334448968643 df.mm.trans2:exp5 -0.227300004836167 0.179942730153531 -1.26317970524416 0.206923526957166 df.mm.trans1:exp6 -0.188550521801335 0.219930003520983 -0.857320596474896 0.391546040891726 df.mm.trans2:exp6 -0.42881403625394 0.179942730153531 -2.38305840912865 0.0174215646967285 * df.mm.trans1:exp7 0.158973829584576 0.219930003520983 0.722838298729026 0.470008241247517 df.mm.trans2:exp7 -0.0174138858858441 0.179942730153531 -0.0967746008465372 0.922931668796087 df.mm.trans1:exp8 0.0115125077872877 0.219930003520983 0.0523462356339632 0.95826697847981 df.mm.trans2:exp8 -0.306801194020286 0.179942730153531 -1.70499354855023 0.088616473740807 . df.mm.trans1:probe2 0.0730675615294946 0.128411456647894 0.569011234954269 0.569521638378659 df.mm.trans1:probe3 0.0282257314302412 0.128411456647894 0.219806956225383 0.826082285823365 df.mm.trans1:probe4 -0.0310525401901709 0.128411456647894 -0.241820636575422 0.808986325172075 df.mm.trans1:probe5 0.0172477314747105 0.128411456647894 0.134316142227123 0.893189209812662 df.mm.trans1:probe6 0.0191652071599325 0.128411456647894 0.149248421131798 0.881398387860183 df.mm.trans1:probe7 -0.0928778971944946 0.128411456647894 -0.723283573125156 0.469734853664077 df.mm.trans1:probe8 0.0729944161745498 0.128411456647894 0.568441617905649 0.569908058679703 df.mm.trans1:probe9 0.222820226093581 0.128411456647894 1.73520519048824 0.0831219184661804 . df.mm.trans1:probe10 0.226446894231946 0.128411456647894 1.76344774947042 0.0782387652612842 . df.mm.trans1:probe11 0.206036664254750 0.128411456647894 1.60450375405137 0.109030880895975 df.mm.trans1:probe12 0.185808884208499 0.128411456647894 1.44698058147560 0.148326969677302 df.mm.trans1:probe13 -0.044848377154187 0.128411456647894 -0.349255263704094 0.72699730899112 df.mm.trans1:probe14 0.0376226510858612 0.128411456647894 0.292985159330628 0.769615855390913 df.mm.trans1:probe15 0.0283688101308058 0.128411456647894 0.220921176905527 0.825214913709467 df.mm.trans1:probe16 0.065703083706334 0.128411456647894 0.511660605848378 0.60904161881256 df.mm.trans1:probe17 -0.064761191540803 0.128411456647894 -0.504325651552877 0.614183086860541 df.mm.trans1:probe18 -0.075682732025302 0.128411456647894 -0.589376789275316 0.555788916944392 df.mm.trans1:probe19 -0.0191720194229881 0.128411456647894 -0.149301471406543 0.881356543647398 df.mm.trans1:probe20 -0.0756925084531556 0.128411456647894 -0.589452922886045 0.55573788456377 df.mm.trans1:probe21 0.0244582359140054 0.128411456647894 0.190467708664580 0.848995025040616 df.mm.trans1:probe22 0.0443819842281488 0.128411456647894 0.345623244114775 0.72972436980692 df.mm.trans2:probe2 -0.0819358684403369 0.128411456647894 -0.638072883675842 0.523624169657442 df.mm.trans2:probe3 -0.0207947120949854 0.128411456647894 -0.161938137280109 0.871398894628128 df.mm.trans2:probe4 0.021346372107312 0.128411456647894 0.166234171502657 0.868018199526698 df.mm.trans2:probe5 -0.0468273138278469 0.128411456647894 -0.364666168037079 0.715465084014999 df.mm.trans2:probe6 -0.0174404717792105 0.128411456647894 -0.135817101016403 0.892002908702277 df.mm.trans3:probe2 -0.0801528971623129 0.128411456647894 -0.624188053423406 0.532696989035891 df.mm.trans3:probe3 -0.104520863891380 0.128411456647894 -0.81395279377585 0.41593453728566 df.mm.trans3:probe4 -0.0648699084730107 0.128411456647894 -0.505172281090814 0.613588660563883 df.mm.trans3:probe5 0.0810321723239829 0.128411456647894 0.631035379858468 0.528212769351272