fitVsDatCorrelation=0.882909720993375 cont.fitVsDatCorrelation=0.236429547046439 fstatistic=8083.36832614625,65,991 cont.fstatistic=1875.9761219973,65,991 residuals=-0.842464110389827,-0.102137311770709,-0.00782460204256998,0.086683647618543,1.38931045219917 cont.residuals=-0.809609468380576,-0.255694685135502,-0.0816870219183172,0.169173322985151,1.85893370826022 predictedValues: Include Exclude Both Lung 62.5897635231261 66.6173179465579 79.6285567156553 cerebhem 78.3784899091785 70.8774167879762 67.6495814955288 cortex 60.1006886259147 75.338168708326 73.3966868214408 heart 62.6760199054512 73.7890947333643 78.5313286298222 kidney 61.6409965402646 75.3695254078294 82.2146182769202 liver 64.0596903088047 68.4428508659102 73.7304122965384 stomach 68.4609362898403 70.4849159632381 81.7997143450542 testicle 62.9450316598328 76.4373974905347 83.9913278371727 diffExp=-4.02755442343178,7.50107312120231,-15.2374800824114,-11.1130748279131,-13.7285288675647,-4.38316055710547,-2.02397967339779,-13.4923658307019 diffExpScore=1.24349411216261 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,-1,0,0,-1 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 68.0219568695655 71.5739578799702 70.5473227575197 cerebhem 65.2220666980062 70.9200028482557 72.4059673268496 cortex 76.3631569555837 65.3361336447964 68.985381306047 heart 70.5037286084264 59.602204548302 69.1234759405357 kidney 63.9773626388107 71.7038254639098 73.0003840720577 liver 69.8628700149055 73.5977011606308 65.3830465090871 stomach 70.2954733837645 69.4167476948056 67.0106300337939 testicle 71.8207776605914 64.0358208739357 65.34047723359 cont.diffExp=-3.55200101040469,-5.69793615024957,11.0270233107873,10.9015240601244,-7.72646282509906,-3.73483114572527,0.878725688958866,7.78495678665567 cont.diffExpScore=4.71495883066836 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=-0.304299289838916 cont.tran.correlation=-0.54528043925935 tran.covariance=-0.00130667387466551 cont.tran.covariance=-0.00216082292202749 tran.mean=68.6380190416344 cont.tran.mean=68.8908616840162 weightedLogRatios: wLogRatio Lung -0.259914753517655 cerebhem 0.433701755330674 cortex -0.951090117121643 heart -0.688773072360458 kidney -0.848915086303435 liver -0.277502504072089 stomach -0.123558390765238 testicle -0.823328473943115 cont.weightedLogRatios: wLogRatio Lung -0.216087952407442 cerebhem -0.353417475195163 cortex 0.663982432348392 heart 0.700729591976767 kidney -0.480633523464746 liver -0.222513392814525 stomach 0.0534167386021408 testicle 0.483799419906057 varWeightedLogRatios=0.222790378062204 cont.varWeightedLogRatios=0.224490008118066 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.39182526805613 0.0863786810048996 50.8438565739041 2.06176713544725e-278 *** df.mm.trans1 -0.254144547259164 0.0739133391270718 -3.43841247413053 0.000609482207580114 *** df.mm.trans2 -0.163494079514676 0.0646301144560554 -2.52968884382593 0.0115704044417554 * df.mm.exp2 0.44996668430843 0.0816107597324579 5.51357058534368 4.48516402934069e-08 *** df.mm.exp3 0.163935863997159 0.0816107597324579 2.00875306803398 0.0448341898127254 * df.mm.exp4 0.117498699194444 0.0816107597324579 1.43974519511442 0.150255306743054 df.mm.exp5 0.076203539770463 0.0816107597324578 0.93374378599438 0.350663520463210 df.mm.exp6 0.127205514446628 0.0816107597324579 1.55868558096558 0.119390091049995 df.mm.exp7 0.119194752439860 0.0816107597324579 1.46052742102405 0.144462129938629 df.mm.exp8 0.0898268165829408 0.0816107597324579 1.10067369642701 0.271305975947561 df.mm.trans1:exp2 -0.225018900740011 0.0745514517353127 -3.01830340660457 0.00260696028027419 ** df.mm.trans2:exp2 -0.387979396512831 0.0511682834100626 -7.58241962904175 7.7855015634249e-14 *** df.mm.trans1:exp3 -0.204516307259815 0.0745514517353127 -2.74329074081520 0.00619243642498473 ** df.mm.trans2:exp3 -0.0409135420313326 0.0511682834100626 -0.799587934257077 0.424141190635714 df.mm.trans1:exp4 -0.116121525099539 0.0745514517353127 -1.55760246643910 0.119646790804763 df.mm.trans2:exp4 -0.0152523193195521 0.0511682834100626 -0.298081512669088 0.765703433713175 df.mm.trans1:exp5 -0.091478105085738 0.0745514517353127 -1.22704659609476 0.220096500326194 df.mm.trans2:exp5 0.0472349082616507 0.0511682834100626 0.923128647547352 0.356164865341308 df.mm.trans1:exp6 -0.103991947410068 0.0745514517353127 -1.39490170867874 0.163358036026145 df.mm.trans2:exp6 -0.100170984295837 0.0511682834100626 -1.95767724887439 0.0505482253150569 . df.mm.trans1:exp7 -0.0295331857203543 0.0745514517353127 -0.396145011705592 0.692083330504171 df.mm.trans2:exp7 -0.0627605964543618 0.0511682834100626 -1.22655270553828 0.220282094737806 df.mm.trans1:exp8 -0.0841667271287934 0.0745514517353127 -1.12897502556515 0.259181583646255 df.mm.trans2:exp8 0.0476806828026808 0.0511682834100626 0.931840578284947 0.351645876501444 df.mm.trans1:probe2 0.0366437025283786 0.0550598960926404 0.665524367621838 0.505870028409765 df.mm.trans1:probe3 -0.333268847553495 0.0550598960926404 -6.05284192677657 2.01635591656826e-09 *** df.mm.trans1:probe4 -0.158479849309681 0.0550598960926404 -2.87831726095219 0.00408393480651813 ** df.mm.trans1:probe5 -0.163822547879211 0.0550598960926404 -2.97535156266137 0.00299753635316236 ** df.mm.trans1:probe6 -0.164502976217066 0.0550598960926404 -2.98770952891527 0.00288001129084391 ** df.mm.trans1:probe7 -0.290667136741301 0.0550598960926404 -5.279107978196 1.59510894766287e-07 *** df.mm.trans1:probe8 0.123718401152627 0.0550598960926404 2.24697847130815 0.0248608173946244 * df.mm.trans1:probe9 -0.232585224661499 0.0550598960926404 -4.22422200489019 2.61855944416397e-05 *** df.mm.trans1:probe10 0.0990348927256713 0.0550598960926404 1.79867561971133 0.0723742037539869 . df.mm.trans1:probe11 -0.292416743719459 0.0550598960926404 -5.31088440899809 1.34695378022614e-07 *** df.mm.trans1:probe12 -0.218215123162875 0.0550598960926404 -3.96323165586292 7.92468348464062e-05 *** df.mm.trans1:probe13 -0.352503847877241 0.0550598960926404 -6.40218875974882 2.36052266883056e-10 *** df.mm.trans1:probe14 -0.332248193414774 0.0550598960926404 -6.03430476613603 2.25274629097584e-09 *** df.mm.trans1:probe15 -0.218817788741118 0.0550598960926404 -3.97417729181596 7.57443925300728e-05 *** df.mm.trans1:probe16 -0.187278899461684 0.0550598960926404 -3.40136674334764 0.000697278004467199 *** df.mm.trans1:probe17 0.435277389865663 0.0550598960926404 7.90552508732112 7.08155465564888e-15 *** df.mm.trans1:probe18 0.295567562706973 0.0550598960926404 5.36810970746601 9.91103289288667e-08 *** df.mm.trans1:probe19 0.389771829723995 0.0550598960926404 7.07905131292273 2.74548782414854e-12 *** df.mm.trans1:probe20 0.160852424972173 0.0550598960926404 2.92140807351929 0.00356358611355691 ** df.mm.trans1:probe21 0.561310714784732 0.0550598960926404 10.1945472951911 2.81732856236462e-23 *** df.mm.trans1:probe22 0.801629094117508 0.0550598960926404 14.5592191595991 1.16641080099546e-43 *** df.mm.trans2:probe2 -0.0802256706836774 0.0550598960926404 -1.45706178865094 0.145416102366534 df.mm.trans2:probe3 -0.278800654213874 0.0550598960926404 -5.0635884554664 4.90426236052309e-07 *** df.mm.trans2:probe4 -0.208720090995782 0.0550598960926404 -3.79078250791833 0.000159241887952852 *** df.mm.trans2:probe5 -0.308118762477328 0.0550598960926404 -5.59606509171223 2.83715680532667e-08 *** df.mm.trans2:probe6 0.229799635846997 0.0550598960926404 4.17363003120003 3.26119972564199e-05 *** df.mm.trans3:probe2 0.181450413205696 0.0550598960926404 3.29550954655633 0.00101727260848334 ** df.mm.trans3:probe3 0.465364103393190 0.0550598960926404 8.45196116262545 1.01311571463514e-16 *** df.mm.trans3:probe4 0.0829962698860278 0.0550598960926404 1.50738152041521 0.132031592739866 df.mm.trans3:probe5 0.249697636183556 0.0550598960926404 4.53501829650078 6.4640231295078e-06 *** df.mm.trans3:probe6 0.292637451213636 0.0550598960926404 5.31489290719442 1.31844219856492e-07 *** df.mm.trans3:probe7 0.479646419797033 0.0550598960926404 8.7113571553061 1.24136419986969e-17 *** df.mm.trans3:probe8 1.36204670721586 0.0550598960926404 24.7375459068097 1.34695181165400e-105 *** df.mm.trans3:probe9 0.164393106667348 0.0550598960926404 2.98571407382881 0.00289869842750717 ** df.mm.trans3:probe10 0.535032452884809 0.0550598960926404 9.71728046824855 2.20928580163041e-21 *** df.mm.trans3:probe11 0.218401105509278 0.0550598960926404 3.96660947455857 7.81499486616434e-05 *** df.mm.trans3:probe12 0.0746545232441388 0.0550598960926404 1.35587838957287 0.175446741803618 df.mm.trans3:probe13 0.236843845065175 0.0550598960926404 4.30156723628167 1.86381525245199e-05 *** df.mm.trans3:probe14 1.29412979424595 0.0550598960926404 23.5040362602306 1.92299434691949e-97 *** df.mm.trans3:probe15 0.267576740151723 0.0550598960926404 4.85973928649475 1.36576144493557e-06 *** df.mm.trans3:probe16 0.546107670391223 0.0550598960926404 9.9184290045222 3.5866229049071e-22 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.19256945269823 0.178747774636474 23.4552260089661 4.02129370291542e-97 *** df.mm.trans1 0.0143681743721841 0.152952612047476 0.0939387316100511 0.92517681518626 df.mm.trans2 0.0763521964887571 0.133742365582837 0.570890130109644 0.56820355046205 df.mm.exp2 -0.0772164481292706 0.168881273930790 -0.457223268939308 0.647610786930764 df.mm.exp3 0.0468728457687163 0.168881273930790 0.277549101079883 0.7814163970011 df.mm.exp4 -0.126814362608808 0.168881273930790 -0.750908372829886 0.45288606479716 df.mm.exp5 -0.0936694002624924 0.168881273930790 -0.554646457136979 0.57926165721795 df.mm.exp6 0.130607004939878 0.168881273930790 0.773365820259044 0.439490341308714 df.mm.exp7 0.0537061925565321 0.168881273930790 0.318011531453402 0.750543188855779 df.mm.exp8 0.0197266247734973 0.168881273930790 0.11680765021693 0.907036148359233 df.mm.trans1:exp2 0.0351837578929312 0.154273090750827 0.228061535046044 0.819645449269531 df.mm.trans2:exp2 0.0680376784513845 0.105885117544204 0.642561296897845 0.520657428617567 df.mm.trans1:exp3 0.0687969470906133 0.154273090750827 0.445942625222503 0.655736101947287 df.mm.trans2:exp3 -0.138058905260129 0.105885117544204 -1.30385561693779 0.192585598902625 df.mm.trans1:exp4 0.162649410893933 0.154273090750827 1.05429540629762 0.292004663959679 df.mm.trans2:exp4 -0.0562243659839202 0.105885117544204 -0.530994036630767 0.59554189138585 df.mm.trans1:exp5 0.0323681640865224 0.154273090750827 0.209810822671606 0.833858450668195 df.mm.trans2:exp5 0.0954822091498688 0.105885117544204 0.90175287485522 0.367407263677218 df.mm.trans1:exp6 -0.103903232224717 0.154273090750827 -0.673501980928973 0.500785029488073 df.mm.trans2:exp6 -0.10272450496883 0.105885117544204 -0.97015054949479 0.33220817242693 df.mm.trans1:exp7 -0.0208293339995213 0.154273090750827 -0.135015989490763 0.892626649511999 df.mm.trans2:exp7 -0.0843093239516848 0.105885117544204 -0.796233936431038 0.426086769179158 df.mm.trans1:exp8 0.0346166436797845 0.154273090750827 0.224385494004883 0.822503610559706 df.mm.trans2:exp8 -0.131015287878274 0.105885117544204 -1.23733430076780 0.216256098623371 df.mm.trans1:probe2 0.165740307188875 0.113938228551055 1.45465055316888 0.146082679611880 df.mm.trans1:probe3 -0.0802074155971107 0.113938228551055 -0.703955262576072 0.481626006531597 df.mm.trans1:probe4 0.0342852390011985 0.113938228551055 0.300910760481372 0.763545664823944 df.mm.trans1:probe5 0.0751668297908174 0.113938228551055 0.659715626148562 0.509589620590597 df.mm.trans1:probe6 -0.00815406854080698 0.113938228551055 -0.0715656952411996 0.94296198112973 df.mm.trans1:probe7 0.124121083234077 0.113938228551055 1.08937171318632 0.276254830294816 df.mm.trans1:probe8 0.0349619882376632 0.113938228551055 0.306850375701575 0.759021724994713 df.mm.trans1:probe9 0.0841478123506086 0.113938228551055 0.738538885681395 0.460361911998811 df.mm.trans1:probe10 -0.0662351033020706 0.113938228551055 -0.581324671660932 0.561153847805987 df.mm.trans1:probe11 0.0537423679104852 0.113938228551055 0.471679861921001 0.637259142357411 df.mm.trans1:probe12 0.0286273182014615 0.113938228551055 0.2512529689597 0.80167062232995 df.mm.trans1:probe13 -0.024713564577031 0.113938228551055 -0.216903184219306 0.828328440291807 df.mm.trans1:probe14 -0.105720320148116 0.113938228551055 -0.92787400236562 0.353698861160667 df.mm.trans1:probe15 -0.0889588749529241 0.113938228551055 -0.780764069129457 0.43512772815769 df.mm.trans1:probe16 -0.0740846876953647 0.113938228551055 -0.650218005295454 0.515702114255794 df.mm.trans1:probe17 0.108770396499148 0.113938228551055 0.954643563291914 0.339990779969205 df.mm.trans1:probe18 0.128528211162016 0.113938228551055 1.12805168902923 0.259571115496901 df.mm.trans1:probe19 -0.08551457118741 0.113938228551055 -0.750534498165304 0.453111015546175 df.mm.trans1:probe20 -0.0038793445701304 0.113938228551055 -0.0340477872919718 0.972845900354974 df.mm.trans1:probe21 0.0223841899930312 0.113938228551055 0.19645899605154 0.844291203471702 df.mm.trans1:probe22 0.166923209825542 0.113938228551055 1.46503251760444 0.143229217782771 df.mm.trans2:probe2 -0.00311186941751645 0.113938228551055 -0.0273118992377702 0.978216464957084 df.mm.trans2:probe3 -0.00511024167256856 0.113938228551055 -0.0448509840600047 0.964235122198427 df.mm.trans2:probe4 -0.0129853419679332 0.113938228551055 -0.113968262742601 0.909286036574764 df.mm.trans2:probe5 -0.0214449931934206 0.113938228551055 -0.188215961105724 0.850745893939854 df.mm.trans2:probe6 0.0824645671370454 0.113938228551055 0.723765571799225 0.469380476924678 df.mm.trans3:probe2 -0.0698369554743223 0.113938228551055 -0.612936995444234 0.540058633068173 df.mm.trans3:probe3 0.192937096999134 0.113938228551055 1.69334822432033 0.0907034724702386 . df.mm.trans3:probe4 -0.0518519262514761 0.113938228551055 -0.45508804999756 0.649145575101482 df.mm.trans3:probe5 -0.130568094717749 0.113938228551055 -1.14595510548281 0.252090366477224 df.mm.trans3:probe6 -0.0440850877173152 0.113938228551055 -0.386920950746227 0.698897800670962 df.mm.trans3:probe7 0.0352182495306223 0.113938228551055 0.309099500479254 0.757310811433272 df.mm.trans3:probe8 -0.0849599776744855 0.113938228551055 -0.745667005314333 0.456045419238452 df.mm.trans3:probe9 -0.163362493454308 0.113938228551055 -1.43378122981002 0.151950136575702 df.mm.trans3:probe10 -0.0741663182630198 0.113938228551055 -0.650934451116081 0.515239697776263 df.mm.trans3:probe11 0.00534616753799041 0.113938228551055 0.0469216311853999 0.962585141582067 df.mm.trans3:probe12 0.0668639247393505 0.113938228551055 0.586843639660318 0.557442383336091 df.mm.trans3:probe13 -0.0636708002600829 0.113938228551055 -0.558818590299148 0.576411780741071 df.mm.trans3:probe14 -0.0272031659186085 0.113938228551055 -0.238753632249240 0.81134598911144 df.mm.trans3:probe15 0.0345510826094331 0.113938228551055 0.30324398622672 0.761767576851814 df.mm.trans3:probe16 -0.0573092078844199 0.113938228551055 -0.502984894650524 0.615086657224063