fitVsDatCorrelation=0.980622190941909 cont.fitVsDatCorrelation=0.274747258699519 fstatistic=6392.91213588733,42,462 cont.fstatistic=254.850947002511,42,462 residuals=-0.62196959240599,-0.137037055141347,-0.00556983559923187,0.117907470468515,0.87787803456909 cont.residuals=-1.9329560545135,-0.799936518270983,-0.263827051855311,0.839713927946141,3.12302824427672 predictedValues: Include Exclude Both Lung 98.5753941169531 1066.17214105835 70.1488739655355 cerebhem 118.826990359519 862.859525053137 84.9850949863625 cortex 82.7655703052174 448.003911574983 84.4357942246542 heart 85.5841875664716 455.559074938559 118.230738458447 kidney 102.381010602168 1145.15688830406 85.9509048239293 liver 103.435666206056 635.935073520143 76.3081045825367 stomach 93.4366750361059 537.530746820805 69.9958190175618 testicle 100.310625224359 638.75413560508 97.120730558087 diffExp=-967.596746941399,-744.032534693618,-365.238341269765,-369.974887372088,-1042.77587770189,-532.499407314087,-444.094071784700,-538.443510380721 diffExpScore=0.999800225959521 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 199.871354783017 181.78431122972 182.322393858726 cerebhem 141.998459736202 255.265420516460 167.375626339359 cortex 144.548297141794 179.867471011551 149.388941366596 heart 168.324498704051 125.024432287837 179.799878783362 kidney 157.372323848218 135.734429066191 153.940627482573 liver 167.431864317171 141.921839517102 171.665840540460 stomach 167.720316497079 131.041968709089 122.771613036440 testicle 122.064930040356 179.064367144748 199.352938575526 cont.diffExp=18.0870435532968,-113.266960780258,-35.3191738697573,43.3000664162139,21.6378947820266,25.5100248000686,36.6783477879898,-56.9994371043924 cont.diffExpScore=5.71592644582605 cont.diffExp1.5=0,-1,0,0,0,0,0,0 cont.diffExp1.5Score=0.5 cont.diffExp1.4=0,-1,0,0,0,0,0,-1 cont.diffExp1.4Score=0.666666666666667 cont.diffExp1.3=0,-1,0,1,0,0,0,-1 cont.diffExp1.3Score=1.5 cont.diffExp1.2=0,-1,-1,1,0,0,1,-1 cont.diffExp1.2Score=2.5 tran.correlation=0.590937068457759 cont.tran.correlation=-0.342567073719834 tran.covariance=0.0289045225665537 cont.tran.covariance=-0.0132396576500863 tran.mean=410.955476018248 cont.tran.mean=162.439767784412 weightedLogRatios: wLogRatio Lung -13.7653854572144 cerebhem -11.4374443986351 cortex -8.88372182029969 heart -8.83750908239794 kidney -14.0915781601661 liver -10.0742074398366 stomach -9.46962349050113 testicle -10.2446131555602 cont.weightedLogRatios: wLogRatio Lung 0.498001992183633 cerebhem -3.07850892928179 cortex -1.11115930242314 heart 1.48014245396597 kidney 0.737301796617375 liver 0.832769289082911 stomach 1.23363127676850 testicle -1.91448550107368 varWeightedLogRatios=4.3060780542034 cont.varWeightedLogRatios=2.76519677572113 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 7.47605231275548 0.122803052044248 60.8783917688113 8.3075769507341e-223 *** df.mm.trans1 -2.50921167885146 0.108211063533059 -23.1881251040924 1.84814944688091e-79 *** df.mm.trans2 -0.79239700974404 0.102311794840234 -7.74492335885042 6.10630943984757e-14 *** df.mm.exp2 -0.216587410763754 0.140964160411112 -1.536471469997 0.125107333633377 df.mm.exp3 -1.22720929111454 0.140964160411112 -8.70582485317878 5.62147040277371e-17 *** df.mm.exp4 -1.51364418175618 0.140964160411112 -10.7377944673436 3.66431990670588e-24 *** df.mm.exp5 -0.093810078224206 0.140964160411112 -0.665488858661772 0.506069965490284 df.mm.exp6 -0.55276484138949 0.140964160411112 -3.92131474963132 0.000101422377803476 *** df.mm.exp7 -0.736197613837688 0.140964160411112 -5.22258715754855 2.67372106289216e-07 *** df.mm.exp8 -0.820195607827767 0.140964160411112 -5.81846907352708 1.11108599746451e-08 *** df.mm.trans1:exp2 0.403434305567872 0.130507453056170 3.09127406995090 0.00211347625149315 ** df.mm.trans2:exp2 0.00500923859869055 0.119136459935685 0.0420462266664191 0.966480011916823 df.mm.trans1:exp3 1.05239977056105 0.130507453056170 8.0639055158643 6.4021674870777e-15 *** df.mm.trans2:exp3 0.360181179827595 0.119136459935685 3.02326575778765 0.00263973384943745 ** df.mm.trans1:exp4 1.37232304525065 0.130507453056170 10.5152848600915 2.49075012407085e-23 *** df.mm.trans2:exp4 0.663339507699434 0.119136459935685 5.56789674678546 4.38022987164713e-08 *** df.mm.trans1:exp5 0.131689652395901 0.130507453056170 1.00905848142805 0.313474769117876 df.mm.trans2:exp5 0.165276930321034 0.119136459935685 1.38729093016746 0.166021907373102 df.mm.trans1:exp6 0.600893000383441 0.130507453056170 4.60428110664928 5.35738661191701e-06 *** df.mm.trans2:exp6 0.0360312390003913 0.119136459935685 0.302436710137624 0.762455285147127 df.mm.trans1:exp7 0.682659870439929 0.130507453056170 5.23081137861234 2.56381896479276e-07 *** df.mm.trans2:exp7 0.0513535007330273 0.119136459935685 0.431047730986386 0.66663470930739 df.mm.trans1:exp8 0.837645553726504 0.130507453056170 6.4183733121049 3.41980892431842e-10 *** df.mm.trans2:exp8 0.30788514899525 0.119136459935685 2.58430667791758 0.0100632570324425 * df.mm.trans1:probe2 -0.0340940939058393 0.0652537265280849 -0.522485009207334 0.60158312442877 df.mm.trans1:probe3 0.133895348866145 0.0652537265280849 2.05191880970226 0.0407406225438114 * df.mm.trans1:probe4 -0.0140683478435483 0.0652537265280849 -0.215594550565528 0.829398950284155 df.mm.trans1:probe5 -0.0712029826094601 0.0652537265280849 -1.09117113148802 0.275766323693527 df.mm.trans1:probe6 -0.0636130303096908 0.0652537265280849 -0.974856666343984 0.330141509721324 df.mm.trans1:probe7 -1.02080197573863 0.0652537265280849 -15.6435812949209 1.42508755023781e-44 *** df.mm.trans1:probe8 -0.859749321390658 0.0652537265280849 -13.1754823384780 7.02458872360975e-34 *** df.mm.trans1:probe9 -0.99411914014133 0.0652537265280849 -15.234672302025 9.3305855046999e-43 *** df.mm.trans1:probe10 -0.799965330967604 0.0652537265280849 -12.2593049244981 4.13680719907588e-30 *** df.mm.trans1:probe11 -0.885863316313165 0.0652537265280849 -13.5756739644884 1.45109958792031e-35 *** df.mm.trans1:probe12 -1.03070214972496 0.0652537265280849 -15.7952994344522 2.99446256260050e-45 *** df.mm.trans2:probe2 0.223698131760549 0.0652537265280849 3.42812807272042 0.000662164743205976 *** df.mm.trans2:probe3 0.84915116423501 0.0652537265280849 13.0130677497712 3.34273685053724e-33 *** df.mm.trans2:probe4 0.605794731262455 0.0652537265280849 9.28368023551458 6.39820544077751e-19 *** df.mm.trans2:probe5 0.583874955595135 0.0652537265280849 8.94776416093015 8.83786263366887e-18 *** df.mm.trans2:probe6 0.33105396376567 0.0652537265280849 5.0733342198194 5.67286449817549e-07 *** df.mm.trans3:probe2 0.215970327613682 0.0652537265280849 3.30970105624127 0.00100703424386352 ** df.mm.trans3:probe3 0.0125580637356027 0.0652537265280849 0.192449755803568 0.847474485370744 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.08746782546704 0.602716022723937 8.44090356595225 4.09327799514046e-16 *** df.mm.trans1 0.0228681620049059 0.531098704321065 0.0430582146385382 0.965673731059156 df.mm.trans2 0.0815543923050395 0.502145158750893 0.162411985625650 0.871052442325496 df.mm.exp2 0.083161233260207 0.691850541947372 0.120201153599057 0.904376058046708 df.mm.exp3 -0.135437553943466 0.691850541947372 -0.195761289081664 0.844883081129647 df.mm.exp4 -0.532159914895551 0.691850541947372 -0.76918334615691 0.442177525773447 df.mm.exp5 -0.361970555562775 0.691850541947372 -0.52319183641011 0.601091596683002 df.mm.exp6 -0.364415130983725 0.691850541947372 -0.526725222991075 0.598637211932063 df.mm.exp7 -0.107228671539253 0.691850541947372 -0.154988201985697 0.876898306064063 df.mm.exp8 -0.597496663532706 0.691850541947372 -0.863621009605507 0.388244242754815 df.mm.trans1:exp2 -0.425018956150399 0.64052913777341 -0.663543515955946 0.507313481038753 df.mm.trans2:exp2 0.256321754191151 0.584720429163051 0.438366339548015 0.661325644620858 df.mm.trans1:exp3 -0.188622691577631 0.64052913777341 -0.294479486496612 0.768523803890741 df.mm.trans2:exp3 0.124836980340799 0.584720429163051 0.213498578319704 0.831032280635457 df.mm.trans1:exp4 0.360379637674822 0.640529137773409 0.562628015530354 0.573961111328567 df.mm.trans2:exp4 0.157848210247702 0.584720429163051 0.269955011617502 0.787315404099078 df.mm.trans1:exp5 0.122911109342363 0.640529137773409 0.191889957995702 0.84791271336796 df.mm.trans2:exp5 0.069849923633081 0.584720429163051 0.119458668022018 0.904963910456645 df.mm.trans1:exp6 0.187317685790873 0.64052913777341 0.292442099421148 0.770079910138121 df.mm.trans2:exp6 0.116870729955757 0.584720429163051 0.199874545384094 0.841666643690345 df.mm.trans1:exp7 -0.0681474526081587 0.64052913777341 -0.106392431802636 0.915317178236038 df.mm.trans2:exp7 -0.220074565895946 0.584720429163051 -0.376375708662948 0.706810386133305 df.mm.trans1:exp8 0.104375846631684 0.64052913777341 0.162952534828490 0.870627058383586 df.mm.trans2:exp8 0.582421116569453 0.584720429163051 0.996067671866897 0.319738667003183 df.mm.trans1:probe2 0.06545165601529 0.320264568886705 0.204367458575925 0.838156355058488 df.mm.trans1:probe3 0.20754796698632 0.320264568886705 0.648051602173143 0.517273506362349 df.mm.trans1:probe4 0.355736487164639 0.320264568886705 1.11075817222380 0.267250201314049 df.mm.trans1:probe5 0.349100591304088 0.320264568886705 1.09003812853112 0.27626455468432 df.mm.trans1:probe6 0.229010772279619 0.320264568886705 0.715067461491918 0.474928542910453 df.mm.trans1:probe7 0.45554210661865 0.320264568886705 1.42239308020301 0.155586967018938 df.mm.trans1:probe8 0.89459429146748 0.320264568886705 2.79329772436971 0.00543408191447583 ** df.mm.trans1:probe9 0.117386604045885 0.320264568886705 0.366530098705396 0.714137356200647 df.mm.trans1:probe10 0.0842851094870698 0.320264568886705 0.263173381245573 0.79253427398616 df.mm.trans1:probe11 -0.0363974471051056 0.320264568886705 -0.113648060513311 0.90956615585466 df.mm.trans1:probe12 0.0878110522239372 0.320264568886705 0.274182849914318 0.78406665586735 df.mm.trans2:probe2 -0.159421495280285 0.320264568886705 -0.497780618800454 0.618875523149485 df.mm.trans2:probe3 0.303962620670773 0.320264568886705 0.949098496057181 0.343066714703648 df.mm.trans2:probe4 -0.125533924996828 0.320264568886705 -0.391969443991902 0.695261535391935 df.mm.trans2:probe5 0.261534768053714 0.320264568886705 0.816620986089265 0.414565930320098 df.mm.trans2:probe6 0.0236460019841952 0.320264568886705 0.07383271295477 0.941175452403791 df.mm.trans3:probe2 0.48512629463058 0.320264568886705 1.51476729479306 0.130515174860650 df.mm.trans3:probe3 -0.443467889455125 0.320264568886705 -1.38469232171606 0.166814912274710