fitVsDatCorrelation=0.754405951917875 cont.fitVsDatCorrelation=0.275269320516098 fstatistic=11500.5326404583,45,531 cont.fstatistic=5355.2136654672,45,531 residuals=-0.496361538563958,-0.074102076338109,-0.00317469161286491,0.0711040413449395,0.677549349685559 cont.residuals=-0.450795989603688,-0.121739677864849,-0.0232343737574843,0.0808001861185122,0.81103244441317 predictedValues: Include Exclude Both Lung 52.5423468570022 47.9329458049252 53.672628546825 cerebhem 55.6613664334201 48.2943105072941 49.4972252752476 cortex 61.3123760485756 49.5207897620339 57.6276114126854 heart 52.6362545777324 49.2303492124841 50.5229641949631 kidney 49.6737764655946 46.741352118435 54.7421254271143 liver 52.0036683490217 49.1862806273469 56.6824670176489 stomach 52.9215641002298 49.5589643573943 49.3655885014442 testicle 52.0570334176341 46.2457200741874 54.3185433658599 diffExp=4.60940105207698,7.36705592612607,11.7915862865418,3.40590536524832,2.93242434715962,2.81738772167474,3.3625997428355,5.81131334344669 diffExpScore=0.97679689152166 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=0,0,1,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 49.5369846550667 50.295249091602 52.7827288347451 cerebhem 53.1902886500459 51.0708542577912 58.6581667938074 cortex 50.9282385127031 49.842313116845 48.379703978395 heart 52.4364311248343 53.0608215502025 50.5229501009026 kidney 53.5054727641964 51.7218375862661 52.2925309483511 liver 48.0716200784365 53.5780065424041 56.9352013740164 stomach 54.7458463725242 51.438946122662 54.2868615478485 testicle 53.2201809773257 50.7363696330411 51.368706097096 cont.diffExp=-0.75826443653532,2.11943439225471,1.08592539585813,-0.624390425368262,1.78363517793026,-5.50638646396758,3.30690024986227,2.48381134428463 cont.diffExpScore=3.61274939901341 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=0.50439975735915 cont.tran.correlation=-0.196899521204122 tran.covariance=0.000864468657197892 cont.tran.covariance=-0.000216320916426168 tran.mean=50.969943669582 cont.tran.mean=51.7112163147467 weightedLogRatios: wLogRatio Lung 0.359526430875910 cerebhem 0.560550210321905 cortex 0.856318831018185 heart 0.262893914276712 kidney 0.235788850619044 liver 0.218535111630464 stomach 0.258388633127847 testicle 0.460836555427498 cont.weightedLogRatios: wLogRatio Lung -0.0594018540032362 cerebhem 0.160758520125943 cortex 0.0844810774884349 heart -0.0469407819354194 kidney 0.134355303935573 liver -0.425860907000818 stomach 0.247450939393572 testicle 0.188814743590325 varWeightedLogRatios=0.0482955005152696 cont.varWeightedLogRatios=0.0464209344956689 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.44353683633064 0.0712510563059934 48.3296250590601 1.44203525578709e-196 *** df.mm.trans1 0.465069732597046 0.0633082248630076 7.34611866946843 7.73638403600014e-13 *** df.mm.trans2 0.417607434101617 0.05914246345511 7.06104226481175 5.20025943931543e-12 *** df.mm.exp2 0.146164089293569 0.0812835412615504 1.79820031245008 0.0727131369163165 . df.mm.exp3 0.115853112603661 0.0812835412615504 1.42529607846285 0.154659157448146 df.mm.exp4 0.0889680885941108 0.0812835412615504 1.09454001650634 0.274214441254158 df.mm.exp5 -0.101046478024345 0.0812835412615504 -1.24313577455001 0.214366412826598 df.mm.exp6 -0.039055304431071 0.0812835412615504 -0.480482319359102 0.631082359136934 df.mm.exp7 0.124201100090361 0.0812835412615504 1.52799814283081 0.127108434401834 df.mm.exp8 -0.0570761943537023 0.0812835412615504 -0.702186364765349 0.482870623724865 df.mm.trans1:exp2 -0.0884972351141167 0.0763525920051074 -1.15905999770377 0.246952687437214 df.mm.trans2:exp2 -0.138653402302294 0.0682918343926908 -2.03030718877766 0.0428232133953054 * df.mm.trans1:exp3 0.0385091518208652 0.0763525920051073 0.504359456693877 0.614218016512149 df.mm.trans2:exp3 -0.0832636078585569 0.0682918343926908 -1.21923226398892 0.223297364232935 df.mm.trans1:exp4 -0.0871824070124468 0.0763525920051073 -1.14183952008617 0.254035368024392 df.mm.trans2:exp4 -0.0622608732085907 0.0682918343926908 -0.911688399678636 0.362346470116875 df.mm.trans1:exp5 0.0449041841054734 0.0763525920051074 0.588116040677043 0.556704447380999 df.mm.trans2:exp5 0.075872663375167 0.0682918343926908 1.11100637506506 0.267068412278776 df.mm.trans1:exp6 0.0287501144326198 0.0763525920051073 0.376544052763745 0.706662883784496 df.mm.trans2:exp6 0.0648669681571977 0.0682918343926908 0.949849549862733 0.342620809400892 df.mm.trans1:exp7 -0.117009656607725 0.0763525920051073 -1.53249095459520 0.125996871048825 df.mm.trans2:exp7 -0.0908410121400646 0.0682918343926908 -1.33018849102386 0.184027197268666 df.mm.trans1:exp8 0.0477966571210814 0.0763525920051073 0.625999142476843 0.531584484675614 df.mm.trans2:exp8 0.0212420432448069 0.0682918343926908 0.311048069417219 0.755886146622628 df.mm.trans1:probe2 0.0624199481918614 0.0381762960025537 1.63504464099100 0.102632314863995 df.mm.trans1:probe3 -0.087216305425098 0.0381762960025537 -2.28456698416379 0.022731878748995 * df.mm.trans1:probe4 -0.0626727488868081 0.0381762960025537 -1.64166656929252 0.101251211630422 df.mm.trans1:probe5 -0.105090407335044 0.0381762960025537 -2.75276593957712 0.00611164488513409 ** df.mm.trans1:probe6 -0.00482096130671047 0.0381762960025537 -0.126281536228344 0.899556871434349 df.mm.trans1:probe7 0.096256455966246 0.0381762960025537 2.52136707971374 0.0119815624550134 * df.mm.trans1:probe8 0.0162913742544109 0.0381762960025537 0.426740568370519 0.669741257542642 df.mm.trans1:probe9 -0.014289058122295 0.0381762960025537 -0.374291369737367 0.708336969285527 df.mm.trans1:probe10 0.0220791725320966 0.0381762960025537 0.578347688068526 0.56327460356033 df.mm.trans1:probe11 0.116245045815246 0.0381762960025537 3.04495349175494 0.0024426172211215 ** df.mm.trans1:probe12 0.1880652183833 0.0381762960025537 4.92623009761659 1.12157018562656e-06 *** df.mm.trans1:probe13 0.222515317724586 0.0381762960025537 5.82862511621613 9.70482416915486e-09 *** df.mm.trans1:probe14 0.172137982213962 0.0381762960025537 4.50902785860754 8.02022142304565e-06 *** df.mm.trans1:probe15 0.332310848069173 0.0381762960025537 8.70463829301156 4.02692923792828e-17 *** df.mm.trans2:probe2 0.0353136328202915 0.0381762960025537 0.925014643063573 0.355378470115539 df.mm.trans2:probe3 0.0410006871106398 0.0381762960025537 1.07398284809760 0.283318162492944 df.mm.trans2:probe4 -0.0205763152768873 0.0381762960025537 -0.538981447427769 0.590125550087459 df.mm.trans2:probe5 0.0110845520773864 0.0381762960025537 0.290351690395657 0.771660554138616 df.mm.trans2:probe6 0.0111066560179361 0.0381762960025537 0.290930686863733 0.771217929596083 df.mm.trans3:probe2 -0.537585904593031 0.0381762960025537 -14.0816674450835 1.70559470807443e-38 *** df.mm.trans3:probe3 -0.492346621252348 0.0381762960025537 -12.8966576856858 2.68291482685738e-33 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.72114837605266 0.104353404297179 35.6590990118109 4.87271723013112e-143 *** df.mm.trans1 0.109642436920457 0.0927204328886627 1.18250566250175 0.237534338021475 df.mm.trans2 0.178292172900937 0.0866193109272279 2.05834208321891 0.0400446892729074 * df.mm.exp2 -0.0190831531724952 0.119046856057060 -0.160299514027893 0.872706102697878 df.mm.exp3 0.105755306906661 0.119046856057060 0.888350271560068 0.374754589770268 df.mm.exp4 0.154166597022917 0.119046856057060 1.29500771485325 0.19588033993806 df.mm.exp5 0.114364332211966 0.119046856057060 0.960666547608373 0.337157087068251 df.mm.exp6 -0.0425293058973003 0.119046856057060 -0.357248459185817 0.721047747467966 df.mm.exp7 0.0943687107501343 0.119046856057060 0.792702250825533 0.428305339846747 df.mm.exp8 0.107605368309604 0.119046856057060 0.903890886946468 0.366463142718831 df.mm.trans1:exp2 0.0902394333499667 0.111825049560372 0.806969759501411 0.420045201616991 df.mm.trans2:exp2 0.0343864997003573 0.100019364961710 0.343798420571070 0.731133979034351 df.mm.trans1:exp3 -0.0780573083813039 0.111825049560372 -0.698030617363263 0.485463597863986 df.mm.trans2:exp3 -0.114801643838457 0.100019364961710 -1.14779416848334 0.251570391063083 df.mm.trans1:exp4 -0.0972845521314744 0.111825049560372 -0.869971017351998 0.384709333853535 df.mm.trans2:exp4 -0.100638386200040 0.100019364961710 -1.00618901388313 0.314782939274467 df.mm.trans1:exp5 -0.0372999442187136 0.111825049560372 -0.333556250279828 0.738846109963786 df.mm.trans2:exp5 -0.0863948706149625 0.100019364961710 -0.86378143520544 0.388097966020540 df.mm.trans1:exp6 0.0125017343850480 0.111825049560372 0.111797262189440 0.911026405165774 df.mm.trans2:exp6 0.105757342880346 0.100019364961710 1.05736866976543 0.290824154320917 df.mm.trans1:exp7 0.00561323439168807 0.111825049560372 0.0501965741464537 0.959984617235298 df.mm.trans2:exp7 -0.071883739722882 0.100019364961710 -0.718698221593399 0.472643006294455 df.mm.trans1:exp8 -0.0358872575496580 0.111825049560372 -0.320923242965194 0.74839482466694 df.mm.trans2:exp8 -0.0988729863063718 0.100019364961710 -0.988538432974686 0.323339247488003 df.mm.trans1:probe2 0.143575054709488 0.055912524780186 2.56785139419008 0.0105055578884551 * df.mm.trans1:probe3 0.0495717033453534 0.055912524780186 0.886593898956258 0.375698934321617 df.mm.trans1:probe4 0.0580376896041035 0.055912524780186 1.03800874369870 0.299738419650767 df.mm.trans1:probe5 0.0954209794584942 0.055912524780186 1.70661188765185 0.0884788767637918 . df.mm.trans1:probe6 0.098230378580687 0.055912524780186 1.75685821677467 0.0795183046979007 . df.mm.trans1:probe7 0.0619378176042155 0.055912524780186 1.10776284647702 0.268465775625596 df.mm.trans1:probe8 0.115718182836521 0.055912524780186 2.06962900157799 0.0389701578593525 * df.mm.trans1:probe9 0.134821021366298 0.055912524780186 2.41128480418890 0.0162348778180859 * df.mm.trans1:probe10 0.0980156540378782 0.055912524780186 1.75301785106675 0.0801759824982484 . df.mm.trans1:probe11 0.0644776291941325 0.055912524780186 1.15318758091535 0.249352225390098 df.mm.trans1:probe12 0.0873388600321509 0.055912524780186 1.56206253206261 0.118869013052137 df.mm.trans1:probe13 0.0610643240225009 0.055912524780186 1.09214034355574 0.275266681428173 df.mm.trans1:probe14 0.0985152200168884 0.055912524780186 1.76195262875698 0.0786526609082552 . df.mm.trans1:probe15 0.127992847702438 0.055912524780186 2.28916236935512 0.0224615694495082 * df.mm.trans2:probe2 0.0673062152907617 0.055912524780186 1.20377707061823 0.229212107401453 df.mm.trans2:probe3 0.0312809784781885 0.055912524780186 0.559462814479695 0.576081798291835 df.mm.trans2:probe4 0.0232757108985357 0.055912524780186 0.416287960345945 0.677367506143173 df.mm.trans2:probe5 -0.0136171586786499 0.055912524780186 -0.243543977528903 0.807678080348083 df.mm.trans2:probe6 0.0579849040998472 0.055912524780186 1.03706467071213 0.300177741525802 df.mm.trans3:probe2 -0.0640217234655857 0.055912524780186 -1.14503367031412 0.252711034520207 df.mm.trans3:probe3 -0.064675138759508 0.055912524780186 -1.15672005536812 0.247906864101752