fitVsDatCorrelation=0.92061604113647 cont.fitVsDatCorrelation=0.329740389724316 fstatistic=7033.02580057094,51,669 cont.fstatistic=1192.23708051762,51,669 residuals=-0.550048488573687,-0.113873742102785,-0.00830167571427693,0.0849741314263536,1.63693098435367 cont.residuals=-0.928102055544686,-0.333134820529456,-0.0953739461873894,0.261871047261629,1.75439679473853 predictedValues: Include Exclude Both Lung 87.2662822886054 64.8947659613149 57.8043102526569 cerebhem 84.7195637925435 61.6813803638224 52.3635366066604 cortex 86.529797517911 62.515509122924 55.6854373939194 heart 84.3201282100306 69.7672357474798 58.8127191540235 kidney 87.003289541782 66.3115444776813 64.8934424256199 liver 87.7913405347219 66.8242443620378 56.2554193886265 stomach 129.003952685254 75.2892306441215 91.582863707367 testicle 87.8097611851508 67.1693966638179 55.8127849677716 diffExp=22.3715163272905,23.0381834287211,24.0142883949871,14.5528924625507,20.6917450641007,20.9670961726841,53.7147220411325,20.6403645213329 diffExpScore=0.995024648102583 diffExp1.5=0,0,0,0,0,0,1,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,0,0,1,0 diffExp1.4Score=0.5 diffExp1.3=1,1,1,0,1,1,1,1 diffExp1.3Score=0.875 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 77.0245062272912 81.1889403359378 125.173436346525 cerebhem 90.37651581278 72.2051793002574 63.8913166247412 cortex 82.9604191198387 80.4141097470664 101.219450474503 heart 82.1274001178745 82.6942830205242 74.381370164442 kidney 76.7901722429039 73.9793942197742 74.7009748812803 liver 78.3179306652118 79.5907583754446 111.140253993005 stomach 79.4698712399687 69.703341742559 116.192526198887 testicle 89.4907741052924 96.3354135760745 105.263985118903 cont.diffExp=-4.16443410864657,18.1713365125226,2.5463093727723,-0.566882902649681,2.81077802312971,-1.27282771023272,9.76652949740964,-6.84463947078208 cont.diffExpScore=2.15160745673165 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=0,1,0,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.796794732876847 cont.tran.correlation=0.360549704858627 tran.covariance=0.00697092604823972 cont.tran.covariance=0.00215262265836620 tran.mean=79.3060889436999 cont.tran.mean=80.79181311555 weightedLogRatios: wLogRatio Lung 1.27982822811869 cerebhem 1.35853065528238 cortex 1.39715306737872 heart 0.822219150394322 kidney 1.17599180819634 liver 1.18396719579603 stomach 2.47206002177667 testicle 1.16324466846281 cont.weightedLogRatios: wLogRatio Lung -0.230127868535454 cerebhem 0.985827443222988 cortex 0.137251986361784 heart -0.0303471412246139 kidney 0.161183771389186 liver -0.0704318124970808 stomach 0.565144546027911 testicle -0.333935717407610 varWeightedLogRatios=0.233880545684765 cont.varWeightedLogRatios=0.190145224108757 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.5032341740183 0.106015908433704 42.4769663397674 4.08966296807277e-192 *** df.mm.trans1 -0.182527599473866 0.0951121678955925 -1.91907726963213 0.0553996909617793 . df.mm.trans2 -0.221387316173267 0.0876108969094845 -2.52693813193117 0.0117355418943355 * df.mm.exp2 0.0184503899669609 0.120047743195488 0.153692101790835 0.877898860844335 df.mm.exp3 -0.00848294721220644 0.120047743195488 -0.0706631127450074 0.943686996756905 df.mm.exp4 0.0207591711905933 0.120047743195488 0.172924293601994 0.862763239170417 df.mm.exp5 -0.0971044298759318 0.120047743195488 -0.808881760632563 0.418870725817177 df.mm.exp6 0.0624586535977777 0.120047743195488 0.520281780691779 0.603039320168945 df.mm.exp7 0.0792681852288112 0.120047743195488 0.660305501118248 0.509285079616182 df.mm.exp8 0.0757196541441613 0.120047743195488 0.630746169220843 0.528421913874128 df.mm.trans1:exp2 -0.0480679976604508 0.114709590511624 -0.419040792021480 0.67532082724848 df.mm.trans2:exp2 -0.0692352542352057 0.100126739325125 -0.691476170120645 0.489506233169733 df.mm.trans1:exp3 7.62164612642681e-06 0.114709590511624 6.64429721388854e-05 0.999947005837096 df.mm.trans2:exp3 -0.0288693535740784 0.100126739325125 -0.288328110639214 0.773184897906685 df.mm.trans1:exp4 -0.0551027260414159 0.114709590511624 -0.480367210759347 0.631123338689891 df.mm.trans2:exp4 0.0516383537632981 0.100126739325125 0.515729905031875 0.60621345490762 df.mm.trans1:exp5 0.0940861984806249 0.114709590511624 0.820212137982398 0.41238732012095 df.mm.trans2:exp5 0.118701464049374 0.100126739325125 1.18551213042037 0.236235934504936 df.mm.trans1:exp6 -0.0564599451480224 0.114709590511624 -0.492198994837323 0.622740148454284 df.mm.trans2:exp6 -0.033159672062648 0.100126739325125 -0.331176989145467 0.740614506986154 df.mm.trans1:exp7 0.311610699474237 0.114709590511624 2.71651827963469 0.00676777330126582 ** df.mm.trans2:exp7 0.0693019473021146 0.100126739325125 0.69214225659623 0.489088149138696 df.mm.trans1:exp8 -0.0695111446132497 0.114709590511624 -0.605975004384708 0.544736890035957 df.mm.trans2:exp8 -0.041268889844954 0.100126739325125 -0.412166521381949 0.680349407791109 df.mm.trans1:probe2 0.171432210029562 0.0573547952558122 2.98897780499338 0.00290175133281032 ** df.mm.trans1:probe3 -0.252998811541388 0.0573547952558122 -4.41111872883462 1.19824201837341e-05 *** df.mm.trans1:probe4 -0.468747918225833 0.0573547952558122 -8.17277641974202 1.50950655642837e-15 *** df.mm.trans1:probe5 -0.212290812383859 0.0573547952558122 -3.70136117541709 0.000232097468097389 *** df.mm.trans1:probe6 -0.587137819077537 0.0573547952558122 -10.2369438589886 6.0094107712782e-23 *** df.mm.trans1:probe7 0.166785550647912 0.0573547952558122 2.90796174764499 0.00375846518793317 ** df.mm.trans1:probe8 -0.542397848524027 0.0573547952558122 -9.4568875384323 5.26429460724033e-20 *** df.mm.trans1:probe9 -0.248537158871330 0.0573547952558122 -4.33332832525706 1.69438689292021e-05 *** df.mm.trans1:probe10 0.320175922999217 0.0573547952558122 5.58237409045185 3.45027908410956e-08 *** df.mm.trans1:probe11 0.252093865739295 0.0573547952558122 4.3953406967092 1.28602591569285e-05 *** df.mm.trans1:probe12 0.092485023831416 0.0573547952558122 1.61250726149256 0.107323205699355 df.mm.trans1:probe13 0.143514734508509 0.0573547952558122 2.50222730058417 0.0125790303281971 * df.mm.trans1:probe14 0.284099187851246 0.0573547952558122 4.95336417093139 9.24802282746502e-07 *** df.mm.trans1:probe15 0.39650077586326 0.0573547952558122 6.91312337695215 1.10823042962423e-11 *** df.mm.trans1:probe16 0.647216697683265 0.0573547952558122 11.2844391614784 3.72937754035381e-27 *** df.mm.trans1:probe17 -0.157644906317395 0.0573547952558122 -2.74859156264566 0.00614661299218154 ** df.mm.trans1:probe18 0.971216912449195 0.0573547952558122 16.9334910554105 8.50948448843651e-54 *** df.mm.trans1:probe19 0.888880489670255 0.0573547952558122 15.497928040815 1.62953631454891e-46 *** df.mm.trans1:probe20 0.786158523648588 0.0573547952558122 13.7069362752005 7.23746322638043e-38 *** df.mm.trans1:probe21 0.907377434797144 0.0573547952558122 15.8204284532808 3.98713300066564e-48 *** df.mm.trans2:probe2 -0.206723709570618 0.0573547952558122 -3.60429687959995 0.000336248630949991 *** df.mm.trans2:probe3 0.00921256164380683 0.0573547952558122 0.160624087362133 0.872437971139974 df.mm.trans2:probe4 -0.311182762250857 0.0573547952558122 -5.42557533093665 8.08256166605082e-08 *** df.mm.trans2:probe5 -0.222653172884146 0.0573547952558122 -3.88203238963847 0.000113854997793218 *** df.mm.trans2:probe6 -0.250371883132689 0.0573547952558122 -4.36531735517470 1.47033322803210e-05 *** df.mm.trans3:probe2 -0.128413572733402 0.0573547952558122 -2.23893350435051 0.0254882110343514 * df.mm.trans3:probe3 0.00164583184970508 0.0573547952558122 0.0286956276692192 0.977115901443984 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.90872431800018 0.256324041193547 15.2491522051525 2.78331771664659e-45 *** df.mm.trans1 0.479813248768083 0.229961103025618 2.08649742262991 0.037311696356759 * df.mm.trans2 0.455421994387384 0.211824616514732 2.14999560429139 0.0319133604587079 * df.mm.exp2 0.71511036687662 0.290250049512852 2.46377345353375 0.0139987979254549 * df.mm.exp3 0.277059931739523 0.290250049512852 0.954556018869016 0.340147045588519 df.mm.exp4 0.603014259950253 0.290250049512852 2.07756815532792 0.0381300281072696 * df.mm.exp5 0.420167718999266 0.290250049512852 1.44760602006602 0.148195705470968 df.mm.exp6 0.115679205799028 0.290250049512852 0.398550167323592 0.690351853901058 df.mm.exp7 -0.0468246935773169 0.290250049512852 -0.161325359481958 0.871885856674995 df.mm.exp8 0.494297789152993 0.290250049512852 1.70300673499491 0.0890312812568019 . df.mm.trans1:exp2 -0.555249547970405 0.277343525495358 -2.00202815976570 0.0456849366923462 * df.mm.trans2:exp2 -0.832377623221884 0.242085276016837 -3.43836534347505 0.00062152836256234 *** df.mm.trans1:exp3 -0.202819949670619 0.277343525495358 -0.731295058387844 0.464855007329796 df.mm.trans2:exp3 -0.286649311717247 0.242085276016837 -1.18408404027558 0.236800322698163 df.mm.trans1:exp4 -0.538866192303877 0.277343525495358 -1.94295573095286 0.052440661216224 . df.mm.trans2:exp4 -0.58464282457708 0.242085276016837 -2.41502843211504 0.0160011255823232 * df.mm.trans1:exp5 -0.423214686532074 0.277343525495358 -1.52595841484375 0.127492821368427 df.mm.trans2:exp5 -0.513160156192649 0.242085276016837 -2.1197495553466 0.0343947710211425 * df.mm.trans1:exp6 -0.09902626337391 0.277343525495358 -0.357052731615209 0.721164926213989 df.mm.trans2:exp6 -0.135560255631717 0.242085276016837 -0.559969023569564 0.575688011413641 df.mm.trans1:exp7 0.0780790313826969 0.277343525495358 0.281524622733634 0.7783951011425 df.mm.trans2:exp7 -0.105706080280806 0.242085276016837 -0.436648118464894 0.662507449266294 df.mm.trans1:exp8 -0.344285885718944 0.277343525495358 -1.24136983224693 0.214904230982736 df.mm.trans2:exp8 -0.323240830858323 0.242085276016837 -1.33523540207312 0.182253457357043 df.mm.trans1:probe2 0.209017802584065 0.138671762747679 1.50728452889421 0.13220978826623 df.mm.trans1:probe3 0.227809219865299 0.138671762747679 1.6427945772912 0.100895337805816 df.mm.trans1:probe4 -0.187537009848992 0.138671762747679 -1.35238065870862 0.176710517673786 df.mm.trans1:probe5 -0.182971759007194 0.138671762747679 -1.31945938655241 0.187466955259965 df.mm.trans1:probe6 0.043399390767532 0.138671762747679 0.312964874085430 0.754404887045249 df.mm.trans1:probe7 -0.125654953004108 0.138671762747679 -0.906132225583256 0.365192103490479 df.mm.trans1:probe8 -0.0166181668940922 0.138671762747679 -0.119838145595148 0.90464734219175 df.mm.trans1:probe9 -0.151649883241672 0.138671762747679 -1.09358877565873 0.274528981097395 df.mm.trans1:probe10 -0.100693194901401 0.138671762747679 -0.726126162285957 0.468015388376419 df.mm.trans1:probe11 -0.204293491009181 0.138671762747679 -1.47321622629766 0.141163071314649 df.mm.trans1:probe12 -0.242950471699276 0.138671762747679 -1.75198228453573 0.0802350101378571 . df.mm.trans1:probe13 0.0714704414282496 0.138671762747679 0.515392896232912 0.606448756953311 df.mm.trans1:probe14 -0.0452858282503199 0.138671762747679 -0.326568490606988 0.744096417619982 df.mm.trans1:probe15 -0.0994111183136624 0.138671762747679 -0.716880757436872 0.473697873104756 df.mm.trans1:probe16 -0.132271725745315 0.138671762747679 -0.953847583130464 0.340505310701886 df.mm.trans1:probe17 0.0925718832512748 0.138671762747679 0.667561163260861 0.504643961165482 df.mm.trans1:probe18 -0.0867525255799243 0.138671762747679 -0.625596183829979 0.531793192160588 df.mm.trans1:probe19 -0.178390419995303 0.138671762747679 -1.28642209820246 0.198740788091652 df.mm.trans1:probe20 0.133780126966394 0.138671762747679 0.96472507679746 0.335031121538891 df.mm.trans1:probe21 -0.089502705947888 0.138671762747679 -0.645428486481008 0.518870840932661 df.mm.trans2:probe2 0.121487061657810 0.138671762747679 0.876076421404282 0.381302937352387 df.mm.trans2:probe3 0.180718186831289 0.138671762747679 1.30320826136836 0.192952006429436 df.mm.trans2:probe4 -0.123351580389927 0.138671762747679 -0.889521975821218 0.374042360430587 df.mm.trans2:probe5 -0.0161554091518796 0.138671762747679 -0.116501072978176 0.907290398833034 df.mm.trans2:probe6 0.130996245750890 0.138671762747679 0.944649748119554 0.345178755199032 df.mm.trans3:probe2 -0.0350249843872545 0.138671762747679 -0.252574739754224 0.80067443443017 df.mm.trans3:probe3 -0.0077528998838364 0.138671762747679 -0.0559082810387521 0.955431566483533