fitVsDatCorrelation=0.84350284945289 cont.fitVsDatCorrelation=0.267692535826451 fstatistic=10549.7499413551,58,830 cont.fstatistic=3268.71113819145,58,830 residuals=-0.578342296084075,-0.0884756860974938,-0.00881022229234901,0.0843509745163222,0.718566366389718 cont.residuals=-0.526179249284952,-0.180446320616679,-0.0626908394430888,0.123798198851622,1.36487285378274 predictedValues: Include Exclude Both Lung 51.8436666302357 40.4492314581808 53.1821901671222 cerebhem 57.3368894491281 45.3189317662165 55.1430187066866 cortex 50.7095471420089 41.8187644318195 53.2698682420087 heart 52.0426060677284 42.6219780185743 57.9855470133076 kidney 61.471688095179 66.94319803499 104.128341999173 liver 52.7283872326291 44.0728561161573 52.0675729120281 stomach 53.6140670209272 42.0058164386051 54.3044582121641 testicle 54.1750127611048 43.8174921576158 53.1874814283152 diffExp=11.3944351720550,12.0179576829116,8.89078271018937,9.42062804915405,-5.471509939811,8.65553111647186,11.6082505823221,10.357520603489 diffExpScore=1.14649319424631 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=1,1,1,1,0,0,1,1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 52.8367840480862 61.5225269319903 53.5612500619016 cerebhem 50.7957354809266 52.0746298522434 53.5353226596492 cortex 53.4871436920688 51.2491328075973 54.0115476670369 heart 52.0190517293576 54.5185026333341 52.8172298864175 kidney 54.0259265107081 50.0461881109466 58.297345805051 liver 51.3622424064299 51.8516835069032 46.51588545782 stomach 50.6764993520389 54.5474504292951 51.1039425432875 testicle 51.7543790300931 51.5080624101375 58.0465927723512 cont.diffExp=-8.68574288390412,-1.27889437131673,2.23801088447149,-2.49945090397645,3.97973839976144,-0.489441100473321,-3.87095107725619,0.246316619955607 cont.diffExpScore=2.04997329798134 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=0.889366152382257 cont.tran.correlation=-0.0531766350081899 tran.covariance=0.00908788281692356 cont.tran.covariance=-0.000109853976539384 tran.mean=50.0606333013188 cont.tran.mean=52.7672461832598 weightedLogRatios: wLogRatio Lung 0.94909475755864 cerebhem 0.924725941068028 cortex 0.738253353951212 heart 0.76925946896821 kidney -0.354817092042814 liver 0.694915274337115 stomach 0.941806644671817 testicle 0.824584271733553 cont.weightedLogRatios: wLogRatio Lung -0.615374050018728 cerebhem -0.0979760682919468 cortex 0.169178294799778 heart -0.186550715119972 kidney 0.302337176031832 liver -0.0374018636772715 stomach -0.291657269432185 testicle 0.0188162326366877 varWeightedLogRatios=0.186270545095409 cont.varWeightedLogRatios=0.0802478875860266 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.68922025484120 0.0710093889821758 51.9539783079565 4.2783483872264e-263 *** df.mm.trans1 0.320531890924261 0.0614762592142346 5.21391338739822 2.3358290903965e-07 *** df.mm.trans2 -0.0151737690952539 0.0544643284921429 -0.278600131780618 0.78062110591636 df.mm.exp2 0.178182072332516 0.070393392281923 2.53123292622272 0.0115498286335676 * df.mm.exp3 0.00953164673033358 0.070393392281923 0.135405418340399 0.892324171127615 df.mm.exp4 -0.0303178771610256 0.070393392281923 -0.430692088819978 0.666804090122813 df.mm.exp5 0.00224013211399819 0.070393392281923 0.0318230453367916 0.974620819061548 df.mm.exp6 0.123898845188219 0.070393392281923 1.76009197982686 0.0787605227545317 . df.mm.exp7 0.0504563926541273 0.070393392281923 0.716777399390716 0.47371310130469 df.mm.exp8 0.123872973988466 0.070393392281923 1.75972445669842 0.0788228921817817 . df.mm.trans1:exp2 -0.0774706401278758 0.0652564211678411 -1.18717267575277 0.235499170986452 df.mm.trans2:exp2 -0.0645048508898941 0.0489917276157506 -1.31664781033678 0.188320139450997 df.mm.trans1:exp3 -0.031650226604857 0.0652564211678411 -0.485013214614571 0.627794952570054 df.mm.trans2:exp3 0.0237658583963895 0.0489917276157506 0.485099414798936 0.627733834051875 df.mm.trans1:exp4 0.0341478284241725 0.0652564211678411 0.523286870671983 0.600914308105152 df.mm.trans2:exp4 0.0826402698002118 0.0489917276157506 1.68682089450635 0.09201356285646 . df.mm.trans1:exp5 0.168103801233380 0.0652564211678411 2.57604996144383 0.0101654671980950 * df.mm.trans2:exp5 0.501556693650554 0.0489917276157506 10.2375792416291 3.02686125416901e-23 *** df.mm.trans1:exp6 -0.106977656791665 0.0652564211678411 -1.63934299302923 0.101520789960851 df.mm.trans2:exp6 -0.0381024033343427 0.0489917276157506 -0.777731367899199 0.436949020332734 df.mm.trans1:exp7 -0.0168776938823073 0.0652564211678411 -0.258636523123103 0.79597982406863 df.mm.trans2:exp7 -0.0126959408519390 0.0489917276157506 -0.259144583581847 0.795587943663084 df.mm.trans1:exp8 -0.0798859703053902 0.0652564211678411 -1.22418558780478 0.221229590596497 df.mm.trans2:exp8 -0.0438875155278912 0.0489917276157506 -0.895814817393407 0.370611399812069 df.mm.trans1:probe2 0.0147753837659984 0.0437753381098947 0.337527576118450 0.735804568638375 df.mm.trans1:probe3 -0.259117863746116 0.0437753381098947 -5.9192658454315 4.72929562131535e-09 *** df.mm.trans1:probe4 -0.00254489499646948 0.0437753381098947 -0.0581353590023843 0.953654802510544 df.mm.trans1:probe5 0.407113308142010 0.0437753381098947 9.30006084978677 1.21381744549972e-19 *** df.mm.trans1:probe6 0.345994025197813 0.0437753381098947 7.90385728898817 8.58833579915245e-15 *** df.mm.trans1:probe7 0.0565553623019663 0.0437753381098947 1.29194575630663 0.196735539939473 df.mm.trans1:probe8 0.309225764326104 0.0437753381098947 7.06392634934802 3.42711175774844e-12 *** df.mm.trans1:probe9 -0.114525182111636 0.0437753381098947 -2.61620325636615 0.00905295439102284 ** df.mm.trans1:probe10 0.201752814235147 0.0437753381098947 4.60882366524871 4.68630995680908e-06 *** df.mm.trans1:probe11 -0.238889043225235 0.0437753381098947 -5.45716043644305 6.39054969301255e-08 *** df.mm.trans1:probe12 -0.260246562963713 0.0437753381098947 -5.94504975176626 4.06775403738579e-09 *** df.mm.trans1:probe13 -0.23141598106059 0.0437753381098947 -5.28644645712702 1.59564883948467e-07 *** df.mm.trans1:probe14 -0.231939428128304 0.0437753381098947 -5.29840403621869 1.49782522012633e-07 *** df.mm.trans1:probe15 -0.176273455092910 0.0437753381098947 -4.02677541062936 6.17271423590571e-05 *** df.mm.trans1:probe16 -0.30520322940137 0.0437753381098947 -6.97203591289644 6.37481281806946e-12 *** df.mm.trans1:probe17 -0.216855027602052 0.0437753381098947 -4.95381730822167 8.81961309650948e-07 *** df.mm.trans1:probe18 -0.258328635905963 0.0437753381098947 -5.90123679359023 5.25301828860691e-09 *** df.mm.trans1:probe19 -0.230116684448657 0.0437753381098947 -5.25676543881778 1.86596736865208e-07 *** df.mm.trans1:probe20 -0.24641948124191 0.0437753381098947 -5.62918510471107 2.47719751507946e-08 *** df.mm.trans1:probe21 -0.253235181071778 0.0437753381098947 -5.78488235627216 1.02770113606343e-08 *** df.mm.trans1:probe22 -0.217406367627855 0.0437753381098947 -4.96641207161148 8.28140212236725e-07 *** df.mm.trans2:probe2 0.099654419750168 0.0437753381098947 2.27649685994413 0.0230701408359602 * df.mm.trans2:probe3 0.117563287660054 0.0437753381098947 2.68560547413523 0.00738441548915809 ** df.mm.trans2:probe4 0.0471280023382407 0.0437753381098947 1.07658796877661 0.281977200575659 df.mm.trans2:probe5 0.0309769420896699 0.0437753381098947 0.707634559255822 0.479370872993788 df.mm.trans2:probe6 0.094694714866298 0.0437753381098947 2.16319779480798 0.0308107873165538 * df.mm.trans3:probe2 -0.0231674063425464 0.0437753381098947 -0.529234206812666 0.596784499761437 df.mm.trans3:probe3 0.200628966259832 0.0437753381098947 4.58315058026892 5.28421377215878e-06 *** df.mm.trans3:probe4 -0.0407090651497187 0.0437753381098947 -0.929954328337148 0.352665117230415 df.mm.trans3:probe5 0.0644766909341768 0.0437753381098947 1.47289989565159 0.141157134796573 df.mm.trans3:probe6 -0.0832879920390999 0.0437753381098947 -1.90262361492244 0.0574355073678737 . df.mm.trans3:probe7 -0.0876569843622425 0.0437753381098947 -2.00242849392017 0.0455635466907057 * df.mm.trans3:probe8 -0.186197140444363 0.0437753381098947 -4.25347121196253 2.34429707324917e-05 *** df.mm.trans3:probe9 -0.0317803378475765 0.0437753381098947 -0.725987261772697 0.468051240682433 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.00186918267823 0.127377977479192 31.4172768470276 2.26447951331997e-143 *** df.mm.trans1 -0.0942370388543343 0.110277270005258 -0.854546352569677 0.393048918297543 df.mm.trans2 0.131430217086243 0.0976991367977117 1.34525463984776 0.178910434956796 df.mm.exp2 -0.205636544720251 0.126272991012801 -1.62850775190243 0.103796745431286 df.mm.exp3 -0.178843007169476 0.126272991012801 -1.41632035271380 0.157056907037290 df.mm.exp4 -0.122472443314892 0.126272991012800 -0.969902132930996 0.332377823237235 df.mm.exp5 -0.268931327698858 0.126272991012801 -2.12976128578119 0.0334849546140329 * df.mm.exp6 -0.0582883101032901 0.126272991012801 -0.461605523364702 0.644485185291956 df.mm.exp7 -0.115113550294005 0.126272991012800 -0.911624484149072 0.362231117946092 df.mm.exp8 -0.278783772173519 0.126272991012801 -2.20778624104388 0.0275319747441951 * df.mm.trans1:exp2 0.16624133282567 0.117058195613772 1.42015970734912 0.155936696808406 df.mm.trans2:exp2 0.0389110245466552 0.0878822824186283 0.442763017479474 0.658052520296287 df.mm.trans1:exp3 0.191076711643064 0.117058195613772 1.63232237299738 0.102990890691702 df.mm.trans2:exp3 -0.00386169510370244 0.0878822824186283 -0.0439416796813174 0.964961460730377 df.mm.trans1:exp4 0.106874858477098 0.117058195613772 0.913006201032914 0.361504400832708 df.mm.trans2:exp4 0.00160918590590631 0.0878822824186283 0.0183106976926354 0.98539539399606 df.mm.trans1:exp5 0.291187763881402 0.117058195613772 2.48754700475789 0.0130580563644064 * df.mm.trans2:exp5 0.0624742696190306 0.0878822824186283 0.710885833863915 0.477354697172934 df.mm.trans1:exp6 0.0299840133638331 0.117058195613772 0.256146211776269 0.797901410667462 df.mm.trans2:exp6 -0.112727686276708 0.0878822824186283 -1.28271231896013 0.199950972650998 df.mm.trans1:exp7 0.0733682140730356 0.117058195613772 0.626766999852882 0.530984328787932 df.mm.trans2:exp7 -0.00521887607508376 0.0878822824186283 -0.0593848490441291 0.952659875172993 df.mm.trans1:exp8 0.258085203948617 0.117058195613772 2.20475979999006 0.0277445755850108 * df.mm.trans2:exp8 0.101118719922267 0.0878822824186283 1.15061554091855 0.250221817787018 df.mm.trans1:probe2 0.154333990774926 0.0785250248147585 1.96541155050892 0.0496997017355278 * df.mm.trans1:probe3 0.105318770587071 0.0785250248147585 1.34121282782806 0.180218247679541 df.mm.trans1:probe4 0.0467774656796157 0.0785250248147585 0.595701380419355 0.55153713331809 df.mm.trans1:probe5 0.0557585597051492 0.0785250248147585 0.710073761029485 0.477857842684576 df.mm.trans1:probe6 0.0449769860540978 0.0785250248147585 0.572772643627926 0.566953767876451 df.mm.trans1:probe7 0.158591522843036 0.0785250248147585 2.01963034353896 0.04374266254309 * df.mm.trans1:probe8 0.0840019252202712 0.0785250248147585 1.06974719738622 0.285044108157878 df.mm.trans1:probe9 0.0309945546493371 0.0785250248147585 0.39470926271534 0.693158870099033 df.mm.trans1:probe10 0.0937269408958988 0.0785250248147585 1.19359326682165 0.232978150564431 df.mm.trans1:probe11 0.044164100297657 0.0785250248147585 0.562420711127957 0.573981273734269 df.mm.trans1:probe12 0.0500850641696853 0.0785250248147585 0.63782296519914 0.523764845418157 df.mm.trans1:probe13 0.108118205339844 0.0785250248147585 1.37686305219128 0.168925842997037 df.mm.trans1:probe14 0.137384444907963 0.0785250248147585 1.74956257870729 0.0805633870420517 . df.mm.trans1:probe15 -0.0119604140108533 0.0785250248147585 -0.152313406319426 0.878976768140815 df.mm.trans1:probe16 0.0366096048533603 0.0785250248147585 0.466215769300587 0.6411833920538 df.mm.trans1:probe17 0.0381032071879451 0.0785250248147585 0.485236487066779 0.6276366509675 df.mm.trans1:probe18 0.0819895631615682 0.0785250248147585 1.04412018149606 0.296733788707894 df.mm.trans1:probe19 0.234866441200039 0.0785250248147585 2.9909757017472 0.00286310726315116 ** df.mm.trans1:probe20 0.0494522663981185 0.0785250248147585 0.629764416054334 0.529022046642129 df.mm.trans1:probe21 0.136496795710808 0.0785250248147585 1.73825854920524 0.0825361121025383 . df.mm.trans1:probe22 0.167049633444944 0.0785250248147585 2.12734263808278 0.0336858821011485 * df.mm.trans2:probe2 0.0675617811434441 0.0785250248147585 0.860385352348798 0.389825111990511 df.mm.trans2:probe3 -0.078456119457361 0.0785250248147585 -0.999122504481087 0.318026537997311 df.mm.trans2:probe4 -0.0765778101987983 0.0785250248147585 -0.975202623360467 0.329743892082123 df.mm.trans2:probe5 -0.0172654369776961 0.0785250248147585 -0.219871779963463 0.826025079016161 df.mm.trans2:probe6 -0.103702421159210 0.0785250248147585 -1.32062895113813 0.186989132471254 df.mm.trans3:probe2 -0.0983705524981156 0.0785250248147585 -1.25272870311309 0.210657284532498 df.mm.trans3:probe3 -0.0909442924458337 0.0785250248147585 -1.15815681256227 0.247133188105598 df.mm.trans3:probe4 -0.0979341911860769 0.0785250248147585 -1.24717173177729 0.212686334427879 df.mm.trans3:probe5 -0.0498013995899971 0.0785250248147585 -0.634210555265397 0.526118170429515 df.mm.trans3:probe6 -0.00568009383100489 0.0785250248147585 -0.072334823763562 0.94235287856656 df.mm.trans3:probe7 -0.124603163874364 0.0785250248147585 -1.58679560010715 0.112939813657801 df.mm.trans3:probe8 0.0298707258505473 0.0785250248147585 0.380397534684168 0.703747672162769 df.mm.trans3:probe9 -0.086665540449685 0.0785250248147585 -1.10366778812398 0.270057145916608