fitVsDatCorrelation=0.7388479891988 cont.fitVsDatCorrelation=0.227842449761296 fstatistic=12808.7557185147,54,738 cont.fstatistic=6127.86229427418,54,738 residuals=-0.463943445888869,-0.0823017427734629,-0.00604660951253931,0.0734866609166916,0.602899974659113 cont.residuals=-0.424766377650409,-0.131541899647383,-0.0273934890711556,0.0875370095370546,0.83982313398086 predictedValues: Include Exclude Both Lung 57.6344284458474 52.5385143006386 68.2392013723365 cerebhem 62.4425291404176 69.5496840661025 56.5327190538781 cortex 68.1541500723703 51.8012811623463 70.6811214662191 heart 54.2737196040265 46.435899268575 53.5914371549137 kidney 56.0535556739543 50.5216781909497 62.1622791373048 liver 53.8451491127695 50.8529288067092 54.1515516692351 stomach 58.5177382397137 54.4619748690861 55.1986629501168 testicle 54.6931231373639 53.5389056759943 54.0692404683677 diffExp=5.09591414520887,-7.10715492568493,16.352868910024,7.83782033545151,5.53187748300461,2.99222030606031,4.05576337062755,1.15421746136960 diffExpScore=1.35798014696785 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=0,0,1,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,0,1,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 56.4487899682997 58.3957415281533 53.7546256041 cerebhem 56.401947674482 59.6783622959701 57.4152934673444 cortex 57.9597263036817 54.1254562255156 55.8913748438362 heart 57.4718444517682 61.498837731547 56.0193271590657 kidney 57.8696669286309 61.589112912182 54.6961206306688 liver 55.3650731046851 58.0347272264369 55.0277819412412 stomach 54.3742858863945 55.8387453339515 52.9379098681756 testicle 57.8134664165649 61.208945634968 54.7096783091592 cont.diffExp=-1.94695155985354,-3.27641462148806,3.8342700781661,-4.02699327977885,-3.71944598355113,-2.66965412175183,-1.46445944755707,-3.39547921840308 cont.diffExpScore=1.37749741174338 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=0.414738753611936 cont.tran.correlation=0.389911675434563 tran.covariance=0.00421829383625938 cont.tran.covariance=0.000420345648031022 tran.mean=55.9572037354291 cont.tran.mean=57.754670601452 weightedLogRatios: wLogRatio Lung 0.371019552698339 cerebhem -0.451460042431754 cortex 1.12063750673907 heart 0.610776652772655 kidney 0.412955638755648 liver 0.226269910888995 stomach 0.289708908058408 testicle 0.085127132401799 cont.weightedLogRatios: wLogRatio Lung -0.137341688363533 cerebhem -0.229292834067684 cortex 0.275522250059395 heart -0.276659517123455 kidney -0.254732123756017 liver -0.190136274862312 stomach -0.106550497013609 testicle -0.2331807928319 varWeightedLogRatios=0.199350702094295 cont.varWeightedLogRatios=0.0320946410513899 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.70218221542599 0.0676565201670821 54.7202576526728 5.8698898971281e-262 *** df.mm.trans1 0.446861927710533 0.0595993701312714 7.49776258920674 1.86536980250803e-13 *** df.mm.trans2 0.251117751888654 0.0537783316600122 4.66949687982562 3.58698516776576e-06 *** df.mm.exp2 0.548820995250391 0.0716167303245998 7.66330706195164 5.71007177345094e-14 *** df.mm.exp3 0.118361008435888 0.0716167303245997 1.65270053379179 0.0988171503562909 . df.mm.exp4 0.0580762633814317 0.0716167303245998 0.810931511648236 0.417666439639239 df.mm.exp5 0.0263143175912540 0.0716167303245998 0.367432546445299 0.713401740171947 df.mm.exp6 0.130615996154809 0.0716167303245997 1.82381959582345 0.0685836450706238 . df.mm.exp7 0.263246532625567 0.0716167303245997 3.67576865674282 0.000254317930949209 *** df.mm.exp8 0.199233712636532 0.0716167303245998 2.78194371250284 0.00554138161998224 ** df.mm.trans1:exp2 -0.468694500506345 0.0675519235776081 -6.9382850359232 8.6909233289306e-12 *** df.mm.trans2:exp2 -0.268326125801593 0.0552697556544066 -4.85484552309939 1.47116957590457e-06 *** df.mm.trans1:exp3 0.0492909387843751 0.067551923577608 0.729674836390801 0.465820493478936 df.mm.trans2:exp3 -0.132492632979352 0.0552697556544066 -2.39719954269037 0.0167685911490442 * df.mm.trans1:exp4 -0.118156244007544 0.0675519235776081 -1.74911738629912 0.0806864257196206 . df.mm.trans2:exp4 -0.181549918527715 0.0552697556544066 -3.28479683649987 0.00106873639887535 ** df.mm.trans1:exp5 -0.0541268377625458 0.067551923577608 -0.80126271608478 0.423237463808996 df.mm.trans2:exp5 -0.0654583086793847 0.0552697556544066 -1.18434228457035 0.236658882948094 df.mm.trans1:exp6 -0.198623783295792 0.067551923577608 -2.940312766484 0.00338129810545716 ** df.mm.trans2:exp6 -0.163224784700329 0.0552697556544066 -2.95323876083239 0.00324447759556797 ** df.mm.trans1:exp7 -0.248036711790574 0.067551923577608 -3.67179346870283 0.000258234731858525 *** df.mm.trans2:exp7 -0.227290289659428 0.0552697556544066 -4.11238093905535 4.35619937296079e-05 *** df.mm.trans1:exp8 -0.251615835935887 0.067551923577608 -3.72477677333370 0.000210397855047071 *** df.mm.trans2:exp8 -0.180371620492627 0.0552697556544066 -3.26347779824582 0.00115123348447131 ** df.mm.trans1:probe2 -0.119637211455442 0.0394418258859358 -3.03325743086612 0.00250411526814853 ** df.mm.trans1:probe3 -0.293363189376182 0.0394418258859358 -7.43787040246505 2.84764259491181e-13 *** df.mm.trans1:probe4 -0.270922778031203 0.0394418258859358 -6.86892079526696 1.37515108660426e-11 *** df.mm.trans1:probe5 -0.09988764128867 0.0394418258859358 -2.53253086146521 0.0115304153515270 * df.mm.trans1:probe6 -0.280739752204597 0.0394418258859358 -7.11781835395971 2.60301135832203e-12 *** df.mm.trans1:probe7 -0.00632704172949705 0.0394418258859358 -0.160414524109371 0.872598446592241 df.mm.trans1:probe8 0.249916715797269 0.0394418258859358 6.3363373825547 4.09138824454815e-10 *** df.mm.trans1:probe9 -0.187584765240551 0.0394418258859358 -4.75598583551985 2.37541263191445e-06 *** df.mm.trans1:probe10 0.111937465763310 0.0394418258859358 2.83803965077654 0.00466377315921871 ** df.mm.trans1:probe11 -0.0492214233667154 0.0394418258859358 -1.24794991766004 0.212445200340982 df.mm.trans1:probe12 -0.141817086901178 0.0394418258859358 -3.59560146407287 0.000345186903543565 *** df.mm.trans1:probe13 -0.227556112623165 0.0394418258859358 -5.76941121542518 1.17046075166109e-08 *** df.mm.trans1:probe14 -0.140145126214997 0.0394418258859358 -3.55321091422823 0.000404765769542389 *** df.mm.trans1:probe15 -0.121750479215650 0.0394418258859358 -3.08683679015641 0.00209867480461172 ** df.mm.trans1:probe16 -0.148081079408152 0.0394418258859358 -3.75441745106824 0.000187409733980963 *** df.mm.trans1:probe17 -0.0955135748979227 0.0394418258859358 -2.42163167532214 0.0156910468372717 * df.mm.trans1:probe18 -0.192105583079073 0.0394418258859358 -4.87060572790505 1.36190972967112e-06 *** df.mm.trans1:probe19 -0.135664811582243 0.0394418258859358 -3.43961793185184 0.000615269582320852 *** df.mm.trans1:probe20 -0.0886604031145604 0.0394418258859358 -2.24787775725603 0.0248784651617504 * df.mm.trans1:probe21 -0.154465884825992 0.0394418258859358 -3.91629650393726 9.82564440883878e-05 *** df.mm.trans1:probe22 -0.171359260899772 0.0394418258859358 -4.34460771150242 1.59064267055398e-05 *** df.mm.trans2:probe2 -0.0351664053033774 0.0394418258859358 -0.89160185953554 0.372896969839013 df.mm.trans2:probe3 -0.0591580849427152 0.0394418258859358 -1.49988200631984 0.134072626357305 df.mm.trans2:probe4 0.0508751361373366 0.0394418258859358 1.28987781357956 0.197497130091211 df.mm.trans2:probe5 0.00305061648920619 0.0394418258859358 0.0773447075708019 0.938370289781226 df.mm.trans2:probe6 0.131110667194151 0.0394418258859358 3.32415308493368 0.000930663656241096 *** df.mm.trans3:probe2 -0.245915066382504 0.0394418258859358 -6.23488038037795 7.60487414853487e-10 *** df.mm.trans3:probe3 -0.304065427118839 0.0394418258859358 -7.7092127529335 4.09676556712140e-14 *** df.mm.trans3:probe4 -0.114972485600882 0.0394418258859358 -2.91498892402643 0.00366471589182842 ** df.mm.trans3:probe5 -0.220760496543857 0.0394418258859358 -5.59711655292752 3.07387109967689e-08 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.17205914336031 0.0977589310256845 42.6770127249466 1.80200295636401e-201 *** df.mm.trans1 -0.121266960430823 0.0861169137793166 -1.40816658550470 0.159502855202425 df.mm.trans2 -0.092102578860314 0.0777059210619205 -1.18527105272868 0.236291809424131 df.mm.exp2 -0.0449846012975583 0.103481157215857 -0.434712971016768 0.663897862185261 df.mm.exp3 -0.0885041633904138 0.103481157215857 -0.855268396407652 0.39268015022577 df.mm.exp4 0.0284695811111453 0.103481157215857 0.275118503475557 0.783302200817813 df.mm.exp5 0.0607385884681747 0.103481157215857 0.586953123663634 0.557414697500132 df.mm.exp6 -0.0489947636751342 0.103481157215857 -0.473465556371133 0.636021087825561 df.mm.exp7 -0.0669074714092438 0.103481157215857 -0.646566710398088 0.518113431404628 df.mm.exp8 0.0533273507516746 0.103481157215857 0.515333923454648 0.606474023133378 df.mm.trans1:exp2 0.0441544375345851 0.0976077962828591 0.452365888956547 0.651138353367525 df.mm.trans2:exp2 0.0667111472581402 0.0798609242314302 0.835341537806612 0.403795782556021 df.mm.trans1:exp3 0.114918703766821 0.0976077962828591 1.17735168852492 0.239434715516246 df.mm.trans2:exp3 0.0125658106493876 0.0798609242314302 0.157346171113333 0.875015071926384 df.mm.trans1:exp4 -0.0105082698458885 0.0976077962828591 -0.107658099517342 0.914296168658002 df.mm.trans2:exp4 0.0233057267117252 0.0798609242314302 0.291828913026191 0.770499449275006 df.mm.trans1:exp5 -0.035879083315308 0.0976077962828591 -0.367584195952272 0.713288690719456 df.mm.trans2:exp5 -0.00749644012436935 0.0798609242314302 -0.0938686873025074 0.925238945984362 df.mm.trans1:exp6 0.0296098543314587 0.097607796282859 0.303355423020225 0.761704509295466 df.mm.trans2:exp6 0.0427933721915207 0.0798609242314302 0.535848697011184 0.592224576060253 df.mm.trans1:exp7 0.0294649727018472 0.097607796282859 0.30187109866163 0.762835374390811 df.mm.trans2:exp7 0.0221324925260829 0.0798609242314302 0.277137946236945 0.781751792595474 df.mm.trans1:exp8 -0.0294394741303366 0.097607796282859 -0.301609863673425 0.763034454579051 df.mm.trans2:exp8 -0.00627696954623638 0.0798609242314302 -0.0785987591133588 0.937373080409471 df.mm.trans1:probe2 -0.0205569698076727 0.0569906747611041 -0.360707605127405 0.718421259136271 df.mm.trans1:probe3 -0.0334034440095517 0.0569906747611041 -0.586121223333003 0.557973265653645 df.mm.trans1:probe4 -0.0218827830297642 0.0569906747611041 -0.383971292171805 0.701110324392112 df.mm.trans1:probe5 -0.00973592960180287 0.0569906747611041 -0.170833731002736 0.864401330440735 df.mm.trans1:probe6 -0.0671456483884125 0.0569906747611041 -1.17818658350470 0.239101990003060 df.mm.trans1:probe7 0.0225792479601952 0.0569906747611041 0.396191974473785 0.692077931520861 df.mm.trans1:probe8 -0.0402138146390786 0.0569906747611041 -0.705620960054404 0.480646549115741 df.mm.trans1:probe9 -0.0236828204059492 0.0569906747611041 -0.415556062552756 0.677855679294408 df.mm.trans1:probe10 -0.0229513210343178 0.0569906747611041 -0.402720640359605 0.687270375380777 df.mm.trans1:probe11 0.00372815172329419 0.0569906747611041 0.0654168728291429 0.947859806246243 df.mm.trans1:probe12 0.0185069734741673 0.0569906747611041 0.324736872334738 0.74547225301542 df.mm.trans1:probe13 -0.0289620902420298 0.0569906747611041 -0.508189986579984 0.611471974019033 df.mm.trans1:probe14 -0.0043026435376353 0.0569906747611041 -0.0754973257584911 0.9398394855011 df.mm.trans1:probe15 -0.0793134750449087 0.0569906747611041 -1.39169215625851 0.164434940369886 df.mm.trans1:probe16 -0.00146532419533971 0.0569906747611041 -0.0257116484667380 0.979494283414136 df.mm.trans1:probe17 -0.0143194784801049 0.0569906747611041 -0.251260027015470 0.801683021417537 df.mm.trans1:probe18 0.0102953126053016 0.0569906747611041 0.180649073702986 0.856692629980778 df.mm.trans1:probe19 -0.0666286461531677 0.0569906747611041 -1.16911488471516 0.242734821244065 df.mm.trans1:probe20 -0.0479913074199253 0.0569906747611041 -0.84209052833814 0.400010081289919 df.mm.trans1:probe21 0.00470788079707846 0.0569906747611041 0.0826079146599536 0.934185707545837 df.mm.trans1:probe22 -0.0486367271985058 0.0569906747611041 -0.853415535127164 0.393705810860196 df.mm.trans2:probe2 0.0245245149398239 0.0569906747611041 0.430325049538837 0.667084841893231 df.mm.trans2:probe3 -0.124824391383993 0.0569906747611041 -2.19025993124731 0.0288175932437278 * df.mm.trans2:probe4 -0.0183774084301596 0.0569906747611041 -0.322463429450428 0.74719296700805 df.mm.trans2:probe5 -0.0177695022562191 0.0569906747611041 -0.311796663764836 0.755283123570335 df.mm.trans2:probe6 -0.00340277286256918 0.0569906747611041 -0.0597075377126707 0.952404732278643 df.mm.trans3:probe2 0.0217211709065442 0.0569906747611041 0.381135527831454 0.703212379889962 df.mm.trans3:probe3 0.0186918130571382 0.0569906747611041 0.327980202647035 0.743019657354072 df.mm.trans3:probe4 0.0266166002780398 0.0569906747611041 0.467034306746014 0.640613184350378 df.mm.trans3:probe5 0.0616710006909647 0.0569906747611041 1.08212441683626 0.27955069712291