fitVsDatCorrelation=0.791366146770779 cont.fitVsDatCorrelation=0.296033759835156 fstatistic=12001.4619071537,43,485 cont.fstatistic=4909.60470543566,43,485 residuals=-0.465519150754191,-0.0719455509744657,-0.00351532288005701,0.0708215278821867,0.390254837499751 cont.residuals=-0.392647808867882,-0.131051426153781,-0.036791146144556,0.103016003715792,0.64962453660535 predictedValues: Include Exclude Both Lung 46.1385486733969 46.2549494863019 73.4797089723512 cerebhem 47.1870634986943 50.8256764666242 61.9010787476098 cortex 48.6364272408214 53.432030071988 65.5845761815336 heart 50.8974267738102 48.9821375424606 66.8886189044708 kidney 46.845204441472 45.3899396004136 72.6993392976948 liver 50.8919475941293 49.7490451633822 65.9751703438103 stomach 49.5493368322183 47.5012466828040 64.3293104704232 testicle 48.3256233110092 49.5520279963509 71.0124281800996 diffExp=-0.116400812904963,-3.63861296792989,-4.79560283116663,1.91528923134968,1.45526484105843,1.14290243074705,2.04809014941436,-1.22640468534173 diffExpScore=3.87585487441353 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 50.9749558502182 49.8798135056775 48.2984475585479 cerebhem 53.26791831395 50.3736423594747 56.7051010879855 cortex 51.007109939543 50.8505165219065 51.9197822827289 heart 51.0810297896429 56.5415707977931 56.8452143181051 kidney 50.0366052811065 53.593349734902 48.0542367155756 liver 53.115141254503 52.1325642749647 58.7028063940142 stomach 53.4497631000664 50.961403119527 59.4237142822196 testicle 51.0611944827456 49.442881061021 58.6993493292515 cont.diffExp=1.09514234454070,2.89427595447533,0.156593417636557,-5.46054100815022,-3.55674445379544,0.982576979538315,2.48835998053941,1.61831342172457 cont.diffExpScore=14.9859586902365 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=0.331254234275949 cont.tran.correlation=-0.257685031789342 tran.covariance=0.000684441017483871 cont.tran.covariance=-0.000285385113499750 tran.mean=48.7599144609923 cont.tran.mean=51.7355912116901 weightedLogRatios: wLogRatio Lung -0.00965769076667854 cerebhem -0.289050210862880 cortex -0.369698622571621 heart 0.149998922575263 kidney 0.120901497933536 liver 0.0889991612343214 stomach 0.163864748529331 testicle -0.0975006645126494 cont.weightedLogRatios: wLogRatio Lung 0.0851451532815898 cerebhem 0.220526195561884 cortex 0.0120850957527365 heart -0.404646534028949 kidney -0.27104723714697 liver 0.074000462177622 stomach 0.188544816769538 testicle 0.126151191892022 varWeightedLogRatios=0.0420552795004454 cont.varWeightedLogRatios=0.050007720827405 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.11186801738762 0.0660239414753915 47.1324181478544 3.43814717364229e-183 *** df.mm.trans1 0.649437212240434 0.0574222428570003 11.3098545080822 1.75370661732263e-26 *** df.mm.trans2 0.690401277880256 0.0539083224740294 12.8069516207420 1.49637276822744e-32 *** df.mm.exp2 0.288175867097511 0.0732469865839254 3.93430338280638 9.56231106003413e-05 *** df.mm.exp3 0.310634641106611 0.0732469865839254 4.24092041999148 2.66661512364597e-05 *** df.mm.exp4 0.249431255928084 0.0732469865839254 3.40534495084366 0.000715661654865935 *** df.mm.exp5 0.00699886110248144 0.0732469865839254 0.0955515227163952 0.923916234625897 df.mm.exp6 0.278609545071014 0.0732469865839254 3.80369975701031 0.000160753307429798 *** df.mm.exp7 0.230901494936808 0.0732469865839254 3.15236852334183 0.00171980628406871 ** df.mm.exp8 0.149322233176142 0.0732469865839254 2.03861264661107 0.0420304998697843 * df.mm.trans1:exp2 -0.265704887894554 0.066865044702157 -3.97374875135592 8.15031637270394e-05 *** df.mm.trans2:exp2 -0.193942672102284 0.0598059141090341 -3.24286778308748 0.00126486969675924 ** df.mm.trans1:exp3 -0.257910656659330 0.066865044702157 -3.85718214663827 0.000130181351161525 *** df.mm.trans2:exp3 -0.166392735027535 0.0598059141090341 -2.78221205220906 0.00560942312163358 ** df.mm.trans1:exp4 -0.151267685596275 0.066865044702157 -2.26228347367568 0.0241215650970029 * df.mm.trans2:exp4 -0.192144038380761 0.0598059141090341 -3.21279327041899 0.00140194949419332 ** df.mm.trans1:exp5 0.00820098509760546 0.066865044702157 0.122649811035584 0.902435214056744 df.mm.trans2:exp5 -0.0258768494681402 0.0598059141090341 -0.432680443960162 0.665439270371507 df.mm.trans1:exp6 -0.180553631942529 0.066865044702157 -2.70026936715267 0.00717055762558245 ** df.mm.trans2:exp6 -0.205786748499109 0.0598059141090341 -3.44090967531961 0.000629849144839088 *** df.mm.trans1:exp7 -0.159581415465003 0.066865044702157 -2.38661943883895 0.0173864765300668 * df.mm.trans2:exp7 -0.204314012439544 0.0598059141090341 -3.41628441740816 0.000688170505892224 *** df.mm.trans1:exp8 -0.103009107255942 0.066865044702157 -1.54055243236261 0.124077916260759 df.mm.trans2:exp8 -0.0804675191088106 0.0598059141090341 -1.34547762219816 0.179099733446870 df.mm.trans1:probe2 -0.0211993584448169 0.0366234932919627 -0.578845886595675 0.562961883041061 df.mm.trans1:probe3 0.0576799368653079 0.0366234932919627 1.57494361352925 0.115921508422315 df.mm.trans1:probe4 -0.00326862259005019 0.0366234932919627 -0.089249339597201 0.928920596745112 df.mm.trans1:probe5 0.151045790350973 0.0366234932919627 4.12428681084120 4.37560646220612e-05 *** df.mm.trans1:probe6 0.00771660926938999 0.0366234932919627 0.210701071246075 0.833209032615488 df.mm.trans1:probe7 0.152679802296031 0.0366234932919627 4.1689033069256 3.62541933122677e-05 *** df.mm.trans1:probe8 0.0876509419917922 0.0366234932919627 2.39329823872994 0.0170768780694574 * df.mm.trans1:probe9 0.0991781939977937 0.0366234932919627 2.70804844330781 0.00700711359280608 ** df.mm.trans1:probe10 0.319228589651549 0.0366234932919627 8.71649755272269 4.57711489490103e-17 *** df.mm.trans1:probe11 0.173797095246724 0.0366234932919627 4.74550840525268 2.74065267481736e-06 *** df.mm.trans1:probe12 0.100988105791746 0.0366234932919627 2.75746786322835 0.00604487095632499 ** df.mm.trans2:probe2 0.079966363134008 0.0366234932919627 2.18347175395083 0.0294796189160897 * df.mm.trans2:probe3 -0.00937307154576642 0.0366234932919627 -0.255930570878213 0.798112959228555 df.mm.trans2:probe4 0.092206620431642 0.0366234932919627 2.51769048071330 0.0121337637154322 * df.mm.trans2:probe5 0.109918844755611 0.0366234932919627 3.00132059711883 0.00282665624037631 ** df.mm.trans2:probe6 0.0462730320725419 0.0366234932919627 1.26347947487295 0.207023915075306 df.mm.trans3:probe2 -0.133249021282256 0.0366234932919627 -3.63834821053235 0.000303816169317046 *** df.mm.trans3:probe3 -0.382443087173573 0.0366234932919627 -10.4425616673083 3.66334643438083e-23 *** df.mm.trans3:probe4 -0.103096722771367 0.0366234932919627 -2.81504339166883 0.00507550237464038 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.93428982030283 0.103157533517994 38.1386573149944 4.42108041797384e-148 *** df.mm.trans1 -0.0337449535224045 0.0897180145539666 -0.376122384006909 0.70699038383321 df.mm.trans2 -0.0345079069842344 0.0842277734143796 -0.409697485584287 0.682208763747976 df.mm.exp2 -0.106613275397077 0.114443008169084 -0.931584000654381 0.352015042390237 df.mm.exp3 -0.0523959790807953 0.114443008169084 -0.457834689240104 0.647276253566494 df.mm.exp4 -0.0354941243622606 0.114443008169084 -0.310146726568212 0.756582604525367 df.mm.exp5 0.0582981130689314 0.114443008169084 0.509407381032826 0.610698396003471 df.mm.exp6 -0.109787127377665 0.114443008169084 -0.959317035912407 0.337877037411435 df.mm.exp7 -0.138434040929075 0.114443008169084 -1.20963301422963 0.227009022114212 df.mm.exp8 -0.202137165306227 0.114443008169084 -1.76626924213298 0.0779797085017862 . df.mm.trans1:exp2 0.15061306719025 0.104471695204926 1.44166385828062 0.150042590492477 df.mm.trans2:exp2 0.116464962675213 0.093442324881141 1.24638340091984 0.213225329273909 df.mm.trans1:exp3 0.0530265623140594 0.104471695204926 0.507568698009978 0.611986576256099 df.mm.trans2:exp3 0.0716698773349154 0.093442324881141 0.76699587072646 0.443457163092517 df.mm.trans1:exp4 0.0375728652995328 0.104471695204926 0.359646363790997 0.719268148519544 df.mm.trans2:exp4 0.160853876358794 0.093442324881141 1.72142416793890 0.0858115548531108 . df.mm.trans1:exp5 -0.0768777202308347 0.104471695204926 -0.735871281499127 0.462164767596718 df.mm.trans2:exp5 0.0135104932550741 0.093442324881141 0.144586441660773 0.885097464471137 df.mm.trans1:exp6 0.150914710661407 0.104471695204926 1.44455118073255 0.149229708773644 df.mm.trans2:exp6 0.153960533007836 0.093442324881141 1.6476530651787 0.100071548264692 df.mm.trans1:exp7 0.185841795858357 0.104471695204926 1.77887221504180 0.0758869675042105 . df.mm.trans2:exp7 0.159886203611485 0.093442324881141 1.71106833883747 0.0877078982714926 . df.mm.trans1:exp8 0.203827520200647 0.104471695204926 1.95103104052089 0.0516287680659495 . df.mm.trans2:exp8 0.193338868700639 0.093442324881141 2.06907168616114 0.0390685297144753 * df.mm.trans1:probe2 0.0600462043085558 0.057221504084542 1.04936431275628 0.294533088223119 df.mm.trans1:probe3 0.0768749567136465 0.057221504084542 1.34346270590978 0.179750431855564 df.mm.trans1:probe4 0.0373059753316411 0.057221504084542 0.651957265515483 0.514737576670313 df.mm.trans1:probe5 0.0104769738438401 0.057221504084542 0.183095044624498 0.854799957862956 df.mm.trans1:probe6 0.0272504130900406 0.057221504084542 0.476226787918392 0.634127273899489 df.mm.trans1:probe7 0.0388349157799087 0.057221504084542 0.678676948486569 0.497666279597576 df.mm.trans1:probe8 0.0698097900823092 0.057221504084542 1.21999222493643 0.223060711560815 df.mm.trans1:probe9 0.0460218607835249 0.057221504084542 0.804275621897842 0.421631859177184 df.mm.trans1:probe10 0.0198379542208007 0.057221504084542 0.346687046035894 0.72897680772995 df.mm.trans1:probe11 0.0358283090685970 0.057221504084542 0.626133647512348 0.531521841378088 df.mm.trans1:probe12 0.0703459839476693 0.057221504084542 1.22936272076554 0.219531914980904 df.mm.trans2:probe2 0.063720097730643 0.057221504084542 1.11356908124085 0.266015669503136 df.mm.trans2:probe3 -0.0269542785299218 0.057221504084542 -0.471051555899302 0.637815715995794 df.mm.trans2:probe4 0.00608076511644633 0.057221504084542 0.106267131801749 0.915414347785503 df.mm.trans2:probe5 0.0522101501291633 0.057221504084542 0.912421841481574 0.362000004421777 df.mm.trans2:probe6 0.00328795181901783 0.057221504084542 0.0574600732997169 0.954202375429958 df.mm.trans3:probe2 -0.074293176562849 0.057221504084542 -1.29834365159441 0.194786300889753 df.mm.trans3:probe3 0.0541376059499491 0.057221504084542 0.946105958172008 0.344565659716287 df.mm.trans3:probe4 0.0656054054161365 0.057221504084542 1.14651661933261 0.252146777153243