fitVsDatCorrelation=0.91876181955942 cont.fitVsDatCorrelation=0.296695537466862 fstatistic=9596.9201067251,51,669 cont.fstatistic=1629.45641352767,51,669 residuals=-0.623175168881614,-0.0992661239045317,-0.00353805846301329,0.0876350160953656,1.05148729284117 cont.residuals=-0.957737990692528,-0.291930357706103,-0.036343993677108,0.260761253107670,1.39483010733699 predictedValues: Include Exclude Both Lung 90.8794811079102 98.9375311638349 86.0913903087033 cerebhem 62.7000462969322 81.264297050064 80.321343660657 cortex 75.4058144252771 83.8923310258966 70.3640116334036 heart 97.3826549284405 92.305665852096 91.0251039681958 kidney 163.998127418976 94.3372983415948 152.798033412354 liver 83.0831254507364 100.444042947386 89.7084622933913 stomach 129.537047426337 94.8318396373941 130.403360562241 testicle 66.6081210756388 92.0914151185161 74.8033334447537 diffExp=-8.05805005592467,-18.5642507531317,-8.48651660061945,5.07698907634462,69.6608290773814,-17.3609174966494,34.7052077889425,-25.4832940428773 diffExpScore=5.76780770184613 diffExp1.5=0,0,0,0,1,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,1,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,0,0,1,0,1,-1 diffExp1.3Score=1.5 diffExp1.2=0,-1,0,0,1,-1,1,-1 diffExp1.2Score=2.5 cont.predictedValues: Include Exclude Both Lung 81.0569368080422 93.6361754642809 88.0478901369088 cerebhem 85.0532736143814 93.903625306054 86.0798631581521 cortex 83.4999557097798 82.1175388214761 78.8945144570298 heart 95.3608990729645 89.4621799431626 110.862015453747 kidney 91.850979363084 97.054540983984 90.2598432846685 liver 81.649340429167 97.0901551431098 98.1460379011713 stomach 90.8442942489283 78.0668856026544 83.7851526307418 testicle 92.386742892334 91.112260696537 81.3427322242298 cont.diffExp=-12.5792386562387,-8.85035169167254,1.38241688830374,5.89871912980192,-5.20356162089999,-15.4408147139427,12.7774086462739,1.27448219579695 cont.diffExpScore=2.91647895906891 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=0.390324397142513 cont.tran.correlation=-0.192906352393017 tran.covariance=0.0115496676758903 cont.tran.covariance=-0.000959690109313962 tran.mean=94.2311774541894 cont.tran.mean=89.0091115062463 weightedLogRatios: wLogRatio Lung -0.386713434714684 cerebhem -1.10689162354734 cortex -0.466721919277746 heart 0.243719270487384 kidney 2.66721696977472 liver -0.856709737994802 stomach 1.46825616323754 testicle -1.41270524898266 cont.weightedLogRatios: wLogRatio Lung -0.64447262663677 cerebhem -0.444744963167684 cortex 0.0737311529976144 heart 0.288980711230543 kidney -0.250604966116717 liver -0.777529195766418 stomach 0.672012705005423 testicle 0.062774466265801 varWeightedLogRatios=1.94981472440183 cont.varWeightedLogRatios=0.242138992344061 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.29328566194269 0.0884280910784566 48.5511516711758 1.93742038229797e-221 *** df.mm.trans1 0.253431275205432 0.078326250169564 3.23558544749421 0.00127374342698483 ** df.mm.trans2 0.455559373819506 0.0715259905480324 6.36914456310229 3.53457555265366e-10 *** df.mm.exp2 -0.498579917120112 0.0965240052095596 -5.1653463409197 3.17041210766299e-07 *** df.mm.exp3 -0.149876832389864 0.0965240052095596 -1.55274153889980 0.120957809567985 df.mm.exp4 -0.0559951854002999 0.0965240052095596 -0.580116679563088 0.562031263950344 df.mm.exp5 -0.0309988446716517 0.0965240052095596 -0.321151661748301 0.748195763729527 df.mm.exp6 -0.115736188571249 0.0965240052095596 -1.19904046998442 0.230936732296267 df.mm.exp7 -0.103173773502105 0.0965240052095597 -1.06889237840999 0.285503688203505 df.mm.exp8 -0.241867702189753 0.0965240052095596 -2.50577772508137 0.0124546204665469 * df.mm.trans1:exp2 0.127407857830320 0.0912066119044277 1.39691471012898 0.162902406925054 df.mm.trans2:exp2 0.301798033917013 0.0773913765200047 3.8996338802554 0.000106058763941691 *** df.mm.trans1:exp3 -0.0367730264922602 0.0912066119044277 -0.403183779382063 0.686941850435975 df.mm.trans2:exp3 -0.0150776170685897 0.0773913765200047 -0.194822960213046 0.845590696046741 df.mm.trans1:exp4 0.125109054057217 0.0912066119044277 1.37171035569564 0.170613461404766 df.mm.trans2:exp4 -0.0133879424213296 0.0773913765200047 -0.172990105917925 0.86271152856847 df.mm.trans1:exp5 0.621319608937219 0.0912066119044277 6.81222113138331 2.14438036289726e-11 *** df.mm.trans2:exp5 -0.0166131679730835 0.0773913765200047 -0.214664329801514 0.830094451631984 df.mm.trans1:exp6 0.0260435613280325 0.0912066119044278 0.285544663750065 0.77531527598237 df.mm.trans2:exp6 0.130848321787741 0.0773913765200047 1.69073516548602 0.091353187094414 . df.mm.trans1:exp7 0.457606448924253 0.0912066119044278 5.01725082611076 6.72502078450546e-07 *** df.mm.trans2:exp7 0.0607903349020489 0.0773913765200047 0.785492358910755 0.432443042444251 df.mm.trans1:exp8 -0.068840034941561 0.0912066119044278 -0.75477022448434 0.45065247272558 df.mm.trans2:exp8 0.170160775869084 0.0773913765200047 2.19870460406012 0.0282405491690644 * df.mm.trans1:probe2 -0.621754179267235 0.0499559187336743 -12.4460563438326 3.92169008161651e-32 *** df.mm.trans1:probe3 -0.112046008000285 0.0499559187336743 -2.24289755529523 0.0252300339651595 * df.mm.trans1:probe4 0.321904049327450 0.0499559187336743 6.44376197030005 2.22938596630326e-10 *** df.mm.trans1:probe5 -0.382906927151515 0.0499559187336743 -7.66489610956558 6.31433333965808e-14 *** df.mm.trans1:probe6 -0.110721254002442 0.0499559187336743 -2.21637909599303 0.0270011959655702 * df.mm.trans1:probe7 -0.123255678144055 0.0499559187336743 -2.46728878716369 0.0138633606800834 * df.mm.trans1:probe8 0.00297676351528646 0.0499559187336743 0.05958780442326 0.95250172677516 df.mm.trans1:probe9 -0.149665400427245 0.0499559187336743 -2.9959493133365 0.00283709594036498 ** df.mm.trans1:probe10 -0.306279330846318 0.0499559187336743 -6.13099185462205 1.49272730987025e-09 *** df.mm.trans1:probe11 -0.266854084234837 0.0499559187336743 -5.34179114305741 1.26295410260814e-07 *** df.mm.trans1:probe12 -0.335376534445728 0.0499559187336743 -6.71344943596557 4.0599693486887e-11 *** df.mm.trans1:probe13 -0.231059703558659 0.0499559187336743 -4.62527182795872 4.49203758598813e-06 *** df.mm.trans1:probe14 -0.386124954794976 0.0499559187336743 -7.72931345439749 3.97452567713125e-14 *** df.mm.trans1:probe15 0.127037919287652 0.0499559187336743 2.54300035927511 0.0112146007654702 * df.mm.trans1:probe16 0.270283959218619 0.0499559187336743 5.41044917339145 8.76466660147815e-08 *** df.mm.trans1:probe17 0.269805756102869 0.0499559187336743 5.40087667171653 9.22485127681104e-08 *** df.mm.trans1:probe18 0.407039709839762 0.0499559187336743 8.1479776602604 1.81940409810583e-15 *** df.mm.trans1:probe19 0.435289489447177 0.0499559187336743 8.7134718063699 2.30605934905683e-17 *** df.mm.trans1:probe20 0.299321804174683 0.0499559187336743 5.99171853430284 3.39231942289598e-09 *** df.mm.trans2:probe2 -0.249479873078382 0.0499559187336743 -4.9940002987116 7.55479051144296e-07 *** df.mm.trans2:probe3 -0.434711503635922 0.0499559187336743 -8.70190188981334 2.52740171730224e-17 *** df.mm.trans2:probe4 -0.298814237507594 0.0499559187336743 -5.98155824339127 3.5994259137898e-09 *** df.mm.trans2:probe5 -0.275857915799499 0.0499559187336743 -5.52202667455996 4.79961747648562e-08 *** df.mm.trans2:probe6 -0.284700301338137 0.0499559187336743 -5.69903043633198 1.80705103112179e-08 *** df.mm.trans3:probe2 -0.566653660623019 0.0499559187336743 -11.3430735533856 2.12789755018859e-27 *** df.mm.trans3:probe3 -0.893571628930148 0.0499559187336743 -17.8872023892498 8.94732399286801e-59 *** df.mm.trans3:probe4 -0.727242314240906 0.0499559187336743 -14.5576807048228 6.59491273223517e-42 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.28537448286019 0.213889919340465 20.035420538164 2.63259144289522e-70 *** df.mm.trans1 0.0271575676377490 0.189455580536564 0.143345303214797 0.886060642919414 df.mm.trans2 0.225828074158877 0.173007108515912 1.30531095569467 0.192235725027558 df.mm.exp2 0.073583537931469 0.233472321260151 0.31517028457295 0.752730647391402 df.mm.exp3 0.00819826488915007 0.233472321260151 0.0351145045584011 0.971998909917094 df.mm.exp4 -0.113489484580372 0.233472321260151 -0.486093957381417 0.627059729757133 df.mm.exp5 0.136060157218824 0.233472321260151 0.582767826543415 0.560245976082754 df.mm.exp6 -0.0650705870048157 0.233472321260151 -0.278707928432808 0.78055510435848 df.mm.exp7 -0.01823061735362 0.233472321260151 -0.0780847050957538 0.937784021854532 df.mm.exp8 0.182716625764906 0.233472321260151 0.782605084742829 0.434135976896872 df.mm.trans1:exp2 -0.025457560654883 0.220610607168335 -0.115395904946029 0.908165951727794 df.mm.trans2:exp2 -0.0707313431228631 0.187194307596494 -0.377849861093682 0.705661981136801 df.mm.trans1:exp3 0.0214960052522583 0.220610607168335 0.0974386749946975 0.922407216691517 df.mm.trans2:exp3 -0.139463442519021 0.187194307596494 -0.745019676664749 0.456521468121422 df.mm.trans1:exp4 0.276006284757538 0.220610607168335 1.25110160522306 0.211334743244080 df.mm.trans2:exp4 0.067888651267141 0.187194307596494 0.362664079580231 0.716970379682624 df.mm.trans1:exp5 -0.0110445142723527 0.220610607168335 -0.0500633873144879 0.960086818749145 df.mm.trans2:exp5 -0.100203857367712 0.187194307596494 -0.535293293125698 0.592624950138098 df.mm.trans1:exp6 0.072352497069077 0.220610607168335 0.327964724805227 0.743040947619439 df.mm.trans2:exp6 0.101293769497955 0.187194307596494 0.541115650355665 0.588608003664774 df.mm.trans1:exp7 0.132225774845635 0.220610607168335 0.599362725767493 0.549133945614056 df.mm.trans2:exp7 -0.163620214498679 0.187194307596494 -0.874066186090287 0.382395863658851 df.mm.trans1:exp8 -0.0518849638954708 0.220610607168335 -0.235187983757646 0.814134802561195 df.mm.trans2:exp8 -0.210041044339933 0.187194307596494 -1.12204824514582 0.26224447848294 df.mm.trans1:probe2 0.0793591158786445 0.120833405971008 0.656764702119589 0.511558076252472 df.mm.trans1:probe3 -0.0660173604202118 0.120833405971008 -0.546350240562213 0.585007363342498 df.mm.trans1:probe4 0.238753330626313 0.120833405971008 1.97588844498514 0.0485778789662 * df.mm.trans1:probe5 0.0566529524040175 0.120833405971008 0.468851738050075 0.639328355850566 df.mm.trans1:probe6 0.0435075740959113 0.120833405971008 0.360062465725333 0.718914060984995 df.mm.trans1:probe7 0.204950762194461 0.120833405971008 1.69614321923223 0.0903239847390411 . df.mm.trans1:probe8 0.270901327459838 0.120833405971008 2.24194067263846 0.0252921467487183 * df.mm.trans1:probe9 0.271320470971885 0.120833405971008 2.24540944444605 0.0250676124497479 * df.mm.trans1:probe10 0.0276679871228763 0.120833405971008 0.228976307508164 0.818957309903763 df.mm.trans1:probe11 0.221660912726166 0.120833405971008 1.83443403705222 0.0670335223488828 . df.mm.trans1:probe12 -0.0491934200059602 0.120833405971008 -0.407117713935526 0.684051758430423 df.mm.trans1:probe13 0.163032263109071 0.120833405971008 1.34923171120565 0.177718998378148 df.mm.trans1:probe14 0.099715345325361 0.120833405971008 0.825229947993736 0.409535211140293 df.mm.trans1:probe15 0.0693883480784806 0.120833405971008 0.574248052687757 0.565992980801822 df.mm.trans1:probe16 0.0160295174953188 0.120833405971008 0.132657996077383 0.894503753083205 df.mm.trans1:probe17 0.00394957219887072 0.120833405971008 0.0326860951003761 0.973934661633981 df.mm.trans1:probe18 0.0592348852394962 0.120833405971008 0.49021944522286 0.624139350887414 df.mm.trans1:probe19 0.0558831217213508 0.120833405971008 0.462480729333733 0.643886996377996 df.mm.trans1:probe20 0.216078032870642 0.120833405971008 1.78823092119480 0.0741911953760123 . df.mm.trans2:probe2 -0.151282787012122 0.120833405971008 -1.25199472609768 0.211009323256219 df.mm.trans2:probe3 0.212163536750478 0.120833405971008 1.75583511070923 0.0795741566175698 . df.mm.trans2:probe4 0.136134521489261 0.120833405971008 1.12662984540818 0.260302966767156 df.mm.trans2:probe5 -0.0315363170130789 0.120833405971008 -0.260990052872013 0.79418049738209 df.mm.trans2:probe6 0.116663463780017 0.120833405971008 0.965490154337028 0.334648240615844 df.mm.trans3:probe2 -0.186307102023423 0.120833405971008 -1.54185095194722 0.123582672157625 df.mm.trans3:probe3 -0.0417745114756928 0.120833405971008 -0.345719887145413 0.729661921590738 df.mm.trans3:probe4 -0.0138353949161346 0.120833405971008 -0.114499751165288 0.908875998354855