fitVsDatCorrelation=0.786773115894158 cont.fitVsDatCorrelation=0.231581725425117 fstatistic=8313.2888885853,53,715 cont.fstatistic=3338.69082703259,53,715 residuals=-0.962523559621343,-0.111322946867601,-0.00424666700421506,0.106381832595070,0.784576402843675 cont.residuals=-1.02924413914272,-0.172407193257510,-9.24255691354502e-05,0.169100821943724,1.15463700891960 predictedValues: Include Exclude Both Lung 94.7988221550942 90.4180453082318 71.5474966694252 cerebhem 78.1665888896482 104.864431836759 64.187382833878 cortex 87.3148467372362 83.9105724670073 72.2708371912517 heart 92.594697469139 85.163892215243 81.7163520569049 kidney 101.223780130948 100.443438186737 78.9523482756365 liver 98.5054987303967 96.7096038440357 62.9045726917915 stomach 99.0773257637991 87.0013383669561 63.0559173269917 testicle 91.7762391045177 83.6600461180135 68.7805421259123 diffExp=4.38077684686242,-26.6978429471109,3.40427427022897,7.43080525389605,0.780341944211443,1.79589488636103,12.075987396843,8.1161929865042 diffExpScore=5.26451647665998 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,-1,0,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,-1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 91.186933299823 82.8572429044426 87.426197845234 cerebhem 89.023265443292 90.0865811248083 101.289946189580 cortex 90.8960458667862 89.4903288614783 92.28178033835 heart 90.3764294039205 89.741146778724 99.609399360716 kidney 88.1252934058416 88.7753741031573 93.2671340967424 liver 86.0092969177181 92.4466643815491 84.3422905593176 stomach 91.5741506360694 76.3260445107175 87.4318820990435 testicle 90.1380581539404 88.5035521330335 99.8305270048387 cont.diffExp=8.32969039538028,-1.0633156815163,1.40571700530788,0.63528262519651,-0.650080697315701,-6.43736746383097,15.2481061253519,1.63450602090690 cont.diffExpScore=1.76117390921156 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=-0.163642407414212 cont.tran.correlation=-0.672265225139426 tran.covariance=-0.00130756565286093 cont.tran.covariance=-0.000854924445023787 tran.mean=92.2268229577351 cont.tran.mean=88.4722754953314 weightedLogRatios: wLogRatio Lung 0.214238563000423 cerebhem -1.32390845986485 cortex 0.176957113298782 heart 0.375307196065939 kidney 0.035703349812861 liver 0.084287223659752 stomach 0.588914861809629 testicle 0.414168951031563 cont.weightedLogRatios: wLogRatio Lung 0.427714761589712 cerebhem -0.0533693064096983 cortex 0.070166702809559 heart 0.0317467478392685 kidney -0.0329445588387411 liver -0.324111645625773 stomach 0.806143536421443 testicle 0.0822061636691096 varWeightedLogRatios=0.351077019188445 cont.varWeightedLogRatios=0.118226164223833 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.6670235078868 0.0958312698197335 48.7004243673892 2.81998945705286e-229 *** df.mm.trans1 -0.113055010438125 0.085100022249894 -1.32849566250573 0.184438266783706 df.mm.trans2 -0.215952863852388 0.0774095366809332 -2.78974494760902 0.005415400042957 ** df.mm.exp2 0.0638647023232504 0.104359558727314 0.611967922268869 0.540753526581477 df.mm.exp3 -0.166987887224706 0.104359558727314 -1.60012067184987 0.110013551344766 df.mm.exp4 -0.216284038576115 0.104359558727314 -2.07248901024249 0.0385776732726325 * df.mm.exp5 0.0722446615035006 0.104359558727314 0.69226683577948 0.488994526113144 df.mm.exp6 0.234366895680909 0.104359558727314 2.24576357488533 0.0250238016632993 * df.mm.exp7 0.131962876159992 0.104359558727314 1.26450205203343 0.206461907368079 df.mm.exp8 -0.0706452690310205 0.104359558727314 -0.676941047782817 0.498662304689135 df.mm.trans1:exp2 -0.256779382780152 0.099094297066455 -2.59126297255986 0.00975749094568123 ** df.mm.trans2:exp2 0.0843598245306256 0.0834448657097715 1.01096483064682 0.312375252959543 df.mm.trans1:exp3 0.0847514166698866 0.099094297066455 0.855260284182148 0.392693563235715 df.mm.trans2:exp3 0.092295641757576 0.0834448657097715 1.10606735324601 0.269069378291421 df.mm.trans1:exp4 0.192758931176927 0.099094297066455 1.94520710962467 0.052141689844441 . df.mm.trans2:exp4 0.156417718573123 0.0834448657097715 1.87450380850462 0.0612685140791653 . df.mm.trans1:exp5 -0.00666793538233378 0.099094297066455 -0.067288790371681 0.946370611968504 df.mm.trans2:exp5 0.0329062397309109 0.0834448657097715 0.394347087157665 0.693442393699002 df.mm.trans1:exp6 -0.196011509046395 0.099094297066455 -1.97803016771939 0.0483088019139709 * df.mm.trans2:exp6 -0.167098046008252 0.0834448657097715 -2.00249643386608 0.0456085167712766 * df.mm.trans1:exp7 -0.0878192472387874 0.099094297066455 -0.886218983721068 0.37579749994504 df.mm.trans2:exp7 -0.170483237814465 0.0834448657097715 -2.04306443978735 0.0414121993953661 * df.mm.trans1:exp8 0.0382417152107604 0.099094297066455 0.385912371779726 0.69967636468886 df.mm.trans2:exp8 -0.00703707741879988 0.0834448657097715 -0.0843320599649049 0.932816029088977 df.mm.trans1:probe2 -0.381172250082457 0.0542761818234154 -7.02282727481789 5.06850775210253e-12 *** df.mm.trans1:probe3 -0.110163213658558 0.0542761818234154 -2.02967876437897 0.0427590389577979 * df.mm.trans1:probe4 -0.553160139841398 0.0542761818234154 -10.1915816709634 7.2327469366525e-23 *** df.mm.trans1:probe5 0.155099102904873 0.0542761818234154 2.85759052487295 0.00439277218896585 ** df.mm.trans1:probe6 -0.109884407475523 0.0542761818234154 -2.02454195899458 0.0432856577558866 * df.mm.trans1:probe7 0.475167633736050 0.0542761818234154 8.75462528447528 1.46639632398895e-17 *** df.mm.trans1:probe8 0.0251732011293248 0.0542761818234154 0.463798304958599 0.64293341960562 df.mm.trans1:probe9 -0.289675627787513 0.0542761818234154 -5.3370671638244 1.26954981002967e-07 *** df.mm.trans1:probe10 -0.270679496154030 0.0542761818234154 -4.98707696563239 7.7014974109777e-07 *** df.mm.trans1:probe11 0.227230506068845 0.0542761818234154 4.18656026336058 3.18545038001079e-05 *** df.mm.trans1:probe12 -0.00780353956766413 0.0542761818234154 -0.143774659629753 0.885718924951636 df.mm.trans1:probe13 0.135112917819034 0.0542761818234154 2.48935929683882 0.0130236146793683 * df.mm.trans1:probe14 0.0756246043371828 0.0542761818234154 1.39332948259373 0.163953158305596 df.mm.trans1:probe15 0.313419264498013 0.0542761818234155 5.7745267623597 1.15078209102159e-08 *** df.mm.trans1:probe16 0.142847374773610 0.0542761818234154 2.63186115851619 0.00867500204001364 ** df.mm.trans1:probe17 -0.0815965489478603 0.0542761818234154 -1.50335830942070 0.133188215509924 df.mm.trans1:probe18 0.0131122127673943 0.0542761818234154 0.241583183025920 0.809172420548595 df.mm.trans1:probe19 0.0201650679927787 0.0542761818234154 0.371527018211867 0.710355103970188 df.mm.trans1:probe20 0.105287787907018 0.0542761818234154 1.93985251669998 0.0527905326800845 . df.mm.trans1:probe21 0.042059048722089 0.0542761818234154 0.774908022434699 0.43865019847121 df.mm.trans1:probe22 0.0163371683394861 0.0542761818234154 0.301000692949223 0.763501475010382 df.mm.trans2:probe2 0.0839144591609623 0.0542761818234154 1.54606415451207 0.122531547738391 df.mm.trans2:probe3 0.133039390016777 0.0542761818234154 2.45115602364244 0.0144781655848471 * df.mm.trans2:probe4 0.0125504811960724 0.0542761818234154 0.231233678833649 0.817199421123775 df.mm.trans2:probe5 0.198529254992663 0.0542761818234154 3.65776015045728 0.000273115498639829 *** df.mm.trans2:probe6 0.105698611479062 0.0542761818234154 1.94742164846721 0.0518753036881564 . df.mm.trans3:probe2 -0.305713360728110 0.0542761818234154 -5.63255097277719 2.55343319824836e-08 *** df.mm.trans3:probe3 0.111831319313420 0.0542761818234155 2.06041242321092 0.0397203931845688 * df.mm.trans3:probe4 -0.0767344200084354 0.0542761818234154 -1.41377704603626 0.157862613828658 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.49338786410199 0.151036536582561 29.7503370096531 3.4374170407597e-127 *** df.mm.trans1 0.0423948409164467 0.1341233675386 0.316088402002326 0.752027658879034 df.mm.trans2 -0.117964574078485 0.122002644238586 -0.966901781635124 0.333920066489370 df.mm.exp2 -0.0875539822975531 0.164477694380005 -0.532315233549364 0.594673055816233 df.mm.exp3 0.0197645472578655 0.164477694380005 0.120165517472551 0.904385790175932 df.mm.exp4 -0.0595794444289994 0.164477694380005 -0.362234190195715 0.717284068130188 df.mm.exp5 -0.0298346911615375 0.164477694380005 -0.181390499629745 0.856112482752671 df.mm.exp6 0.0869680751728338 0.164477694380005 0.52875300508472 0.597140894192127 df.mm.exp7 -0.0779325320462571 0.164477694380005 -0.473818242285205 0.635774169856826 df.mm.exp8 -0.0783246448650684 0.164477694380005 -0.476202230097591 0.634075787702265 df.mm.trans1:exp2 0.06354011574921 0.156179287326096 0.406840861147873 0.684246629149795 df.mm.trans2:exp2 0.171206040736389 0.131514729337392 1.30180126286214 0.193403579957986 df.mm.trans1:exp3 -0.0229596584300801 0.156179287326096 -0.147008344212387 0.883166875291555 df.mm.trans2:exp3 0.0572468528188378 0.131514729337392 0.435288527051409 0.663484374742174 df.mm.trans1:exp4 0.0506513295529465 0.156179287326096 0.324315281623667 0.745794204637606 df.mm.trans2:exp4 0.139389661846006 0.131514729337392 1.05987871129181 0.289557682252831 df.mm.trans1:exp5 -0.00431732987775289 0.156179287326096 -0.0276434215552443 0.977954263064285 df.mm.trans2:exp5 0.09882482208976 0.131514729337392 0.751435391211824 0.452637927601971 df.mm.trans1:exp6 -0.145424292661884 0.156179287326096 -0.931136869374 0.352097052125611 df.mm.trans2:exp6 0.0225446398147146 0.131514729337392 0.171422926757336 0.86393973095744 df.mm.trans1:exp7 0.082169954010554 0.156179287326096 0.526125809749576 0.598963946230164 df.mm.trans2:exp7 -0.00417240633775658 0.131514729337392 -0.0317257721532663 0.974699595797096 df.mm.trans1:exp8 0.0667555077446699 0.156179287326096 0.427428687168274 0.669195808077465 df.mm.trans2:exp8 0.144248171218070 0.131514729337392 1.09682141266483 0.273088731028576 df.mm.trans1:probe2 -0.0274976782860478 0.0855429186835836 -0.32144891370564 0.74796416491353 df.mm.trans1:probe3 0.0321980776455576 0.0855429186835836 0.376396762479612 0.70673361639274 df.mm.trans1:probe4 -0.0599134741667923 0.0855429186835836 -0.700390810704127 0.483911110323387 df.mm.trans1:probe5 0.000482409844311982 0.0855429186835835 0.00563938958052597 0.995502015002302 df.mm.trans1:probe6 0.0788454514613033 0.0855429186835836 0.921706351322268 0.356992660442746 df.mm.trans1:probe7 -0.0204792982670098 0.0855429186835836 -0.239403782126736 0.810861107036565 df.mm.trans1:probe8 -0.0152380486827361 0.0855429186835836 -0.178133373483554 0.858668689490066 df.mm.trans1:probe9 0.0607224115927688 0.0855429186835836 0.709847320236712 0.478030251253121 df.mm.trans1:probe10 -0.0825376347808641 0.0855429186835836 -0.96486811592394 0.334937093967673 df.mm.trans1:probe11 -0.100419192777109 0.0855429186835836 -1.17390421466156 0.240824257192774 df.mm.trans1:probe12 -0.0521116153641185 0.0855429186835836 -0.609186782097945 0.542594151907261 df.mm.trans1:probe13 -0.0644166221061604 0.0855429186835835 -0.75303278281201 0.45167807995781 df.mm.trans1:probe14 -0.0284530839379528 0.0855429186835836 -0.332617642416417 0.739520419520118 df.mm.trans1:probe15 -0.0511580573957408 0.0855429186835836 -0.598039652878462 0.550002851448558 df.mm.trans1:probe16 0.0325756330985054 0.0855429186835836 0.380810400203903 0.703457056198935 df.mm.trans1:probe17 -0.0670610253875902 0.0855429186835836 -0.783945958585346 0.433331441531326 df.mm.trans1:probe18 0.00188043213081734 0.0855429186835836 0.0219823237242221 0.982468188804344 df.mm.trans1:probe19 -0.00357319451693227 0.0855429186835836 -0.0417707809356988 0.966693089456866 df.mm.trans1:probe20 -0.07849714794273 0.0855429186835836 -0.917634669832634 0.359119584521488 df.mm.trans1:probe21 -0.0876464969814023 0.0855429186835836 -1.02459091097417 0.305902768777846 df.mm.trans1:probe22 -0.0623502742110914 0.0855429186835836 -0.728877096673777 0.466315515437498 df.mm.trans2:probe2 0.0921593986062922 0.0855429186835836 1.07734690403986 0.281688663380074 df.mm.trans2:probe3 0.0342634413798998 0.0855429186835836 0.400540943741206 0.688877784156043 df.mm.trans2:probe4 0.241411054139022 0.0855429186835836 2.82210448104983 0.00490343753520824 ** df.mm.trans2:probe5 0.0494364927199576 0.0855429186835836 0.577914495796189 0.563503802764738 df.mm.trans2:probe6 -0.000311667561928028 0.0855429186835836 -0.00364340575145514 0.997094005501042 df.mm.trans3:probe2 0.0901006908572065 0.0855429186835836 1.05328053150117 0.292568248029014 df.mm.trans3:probe3 0.080259794484512 0.0855429186835836 0.93824007550393 0.348437869469985 df.mm.trans3:probe4 0.0415469279362790 0.0855429186835836 0.485685180908518 0.627339188660279