fitVsDatCorrelation=0.914818570959203 cont.fitVsDatCorrelation=0.322322034678896 fstatistic=8187.53898282256,53,715 cont.fstatistic=1479.23623209133,53,715 residuals=-0.87492023934546,-0.0976855286980537,-0.0048992348656701,0.0915469929157947,1.41826553719939 cont.residuals=-0.803836795807925,-0.319490260281296,-0.0700025731121993,0.285751108912923,1.87443723166444 predictedValues: Include Exclude Both Lung 61.1238267217996 123.792865008471 58.100231259258 cerebhem 63.5690802803748 112.927415768422 123.599135587933 cortex 59.8549217458418 107.473174255604 273.953638936548 heart 58.730344681717 106.733732833719 58.8775598583032 kidney 62.2159119910306 125.977915277451 62.1999051222188 liver 62.4867402587453 117.388413071047 57.7979804048984 stomach 63.4689123763352 110.695941528831 57.5323354162983 testicle 64.3825834799909 101.756055743336 66.5920465964333 diffExp=-62.669038286671,-49.358335488047,-47.6182525097618,-48.0033881520017,-63.7620032864201,-54.9016728123014,-47.2270291524956,-37.3734722633453 diffExpScore=0.997572304020506 diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.888888888888889 diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.888888888888889 diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.888888888888889 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 78.3899245083547 68.1790639178658 79.7345069105376 cerebhem 75.475602348448 68.6005801219072 64.5824611917077 cortex 71.8256244032247 74.7980597317359 75.0193112928025 heart 79.9594064505735 68.2176263435711 85.0360067819833 kidney 72.0863979131108 80.2846611416606 74.3091864041545 liver 76.9998838724475 78.2953038815075 73.362869352203 stomach 65.8830876380487 60.5494753998634 93.243674751675 testicle 78.2950781524965 61.9942143693201 67.0813198116415 cont.diffExp=10.2108605904889,6.87502222654081,-2.97243532851118,11.7417801070023,-8.19826322854972,-1.29542000906001,5.33361223818522,16.3008637831764 cont.diffExpScore=1.61370972985137 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=0,0,0,0,0,0,0,1 cont.diffExp1.2Score=0.5 tran.correlation=-0.0207387667209256 cont.tran.correlation=0.0666788483509012 tran.covariance=-3.04027200334257e-05 cont.tran.covariance=0.000742207015140421 tran.mean=87.6611146889196 cont.tran.mean=72.4896243871335 weightedLogRatios: wLogRatio Lung -3.15151942835361 cerebhem -2.55098032910035 cortex -2.56637333733234 heart -2.61153755508414 kidney -3.1629908646248 liver -2.80601506688545 stomach -2.46338921298870 testicle -2.01115600022059 cont.weightedLogRatios: wLogRatio Lung 0.598970712895503 cerebhem 0.408399470874169 cortex -0.17414542758192 heart 0.683244514673783 kidney -0.466583690480829 liver -0.072609944532865 stomach 0.349981573995312 testicle 0.99067962113099 varWeightedLogRatios=0.142617737853029 cont.varWeightedLogRatios=0.239715371939471 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.55823639706832 0.0927694660953652 49.1350935703623 2.11316475968961e-231 *** df.mm.trans1 -0.736203003047378 0.0823810812866918 -8.93655426159485 3.37645919728639e-18 *** df.mm.trans2 0.248940942734114 0.0749363063026115 3.32203380466644 0.000939092321366601 *** df.mm.exp2 -0.80751291840122 0.101025276648137 -7.99317700671806 5.28007391163819e-15 *** df.mm.exp3 -1.71313578722303 0.101025276648137 -16.9574966192842 1.82935721152422e-54 *** df.mm.exp4 -0.201508071673869 0.101025276648137 -1.99463023868477 0.0464624397314854 * df.mm.exp5 -0.0329779055824376 0.101025276648137 -0.326432222475341 0.744192887129086 df.mm.exp6 -0.0258530832751116 0.101025276648137 -0.255907077247170 0.798096194232559 df.mm.exp7 -0.0643515845481057 0.101025276648137 -0.636984987155611 0.52433851262658 df.mm.exp8 -0.280505491845865 0.101025276648137 -2.77658721809635 0.00563705149066844 ** df.mm.trans1:exp2 0.846738358544606 0.0959282398035965 8.82678927788333 8.21274497038998e-18 *** df.mm.trans2:exp2 0.715648467014781 0.080778806905686 8.8593592110161 6.31439886932635e-18 *** df.mm.trans1:exp3 1.69215769765457 0.0959282398035965 17.6398284917882 4.33360645524350e-58 *** df.mm.trans2:exp3 1.57176733646310 0.080778806905686 19.457669612504 5.3235851763971e-68 *** df.mm.trans1:exp4 0.161562857236419 0.0959282398035965 1.68420537651063 0.0925784275446463 . df.mm.trans2:exp4 0.0532356011595443 0.080778806905686 0.659029307299623 0.510089128459801 df.mm.trans1:exp5 0.0506869395273281 0.0959282398035965 0.528383921471973 0.597396854361336 df.mm.trans2:exp5 0.0504747962169913 0.080778806905686 0.624851964896233 0.532267570264311 df.mm.trans1:exp6 0.0479057087737247 0.0959282398035965 0.499391095591943 0.617657516937275 df.mm.trans2:exp6 -0.0272684357382906 0.080778806905686 -0.337569181606359 0.735786947417076 df.mm.trans1:exp7 0.102000048816740 0.0959282398035965 1.06329532393772 0.288007021226307 df.mm.trans2:exp7 -0.0474709636774582 0.080778806905686 -0.587666066086905 0.556942000373016 df.mm.trans1:exp8 0.332446892647195 0.0959282398035965 3.46557899246194 0.000560686606653307 *** df.mm.trans2:exp8 0.084474104924015 0.080778806905686 1.04574588508894 0.296031770995859 df.mm.trans1:probe2 0.168170509172772 0.0525420608421947 3.20068353766817 0.00143164769186097 ** df.mm.trans1:probe3 0.016478042996389 0.0525420608421947 0.313616229212617 0.753904001242735 df.mm.trans1:probe4 0.021051693680208 0.0525420608421947 0.400663646282068 0.68878747148725 df.mm.trans1:probe5 0.0388813520034475 0.0525420608421947 0.740004319971844 0.45954027858522 df.mm.trans1:probe6 0.573569285063098 0.0525420608421947 10.9163834815266 9.12016219979181e-26 *** df.mm.trans1:probe7 -0.0195442998040328 0.0525420608421947 -0.371974366645653 0.710022148908772 df.mm.trans1:probe8 -0.0637281100604 0.0525420608421947 -1.21289703979830 0.225569757852350 df.mm.trans1:probe9 0.291133858474675 0.0525420608421947 5.54096763256145 4.2312056027898e-08 *** df.mm.trans1:probe10 0.320135636693951 0.0525420608421947 6.09294023802092 1.81104768811157e-09 *** df.mm.trans1:probe11 0.187872933207361 0.0525420608421947 3.57566738334874 0.00037284677306188 *** df.mm.trans1:probe12 0.233474003573703 0.0525420608421947 4.44356387685137 1.0251621602763e-05 *** df.mm.trans1:probe13 0.0795231569830353 0.0525420608421948 1.51351423427939 0.130590975012780 df.mm.trans1:probe14 0.369969946325833 0.0525420608421947 7.0414053121556 4.47488540324837e-12 *** df.mm.trans1:probe15 0.298840085269018 0.0525420608421947 5.68763540064705 1.87806278940076e-08 *** df.mm.trans1:probe16 0.255983495208969 0.0525420608421947 4.87197287479438 1.36146000852624e-06 *** df.mm.trans1:probe17 0.64613109858097 0.0525420608421947 12.2974068436631 1.15353589200901e-31 *** df.mm.trans1:probe18 1.20679599393872 0.0525420608421947 22.968189191574 7.22073301898636e-88 *** df.mm.trans1:probe19 0.831336502070595 0.0525420608421947 15.8223048115193 1.43330453206609e-48 *** df.mm.trans1:probe20 0.65339446978211 0.0525420608421947 12.4356460197578 2.79558260326443e-32 *** df.mm.trans1:probe21 0.837888212899426 0.0525420608421947 15.9469994033151 3.29618413275711e-49 *** df.mm.trans1:probe22 0.615219464156757 0.0525420608421947 11.7090851461748 4.28351327407132e-29 *** df.mm.trans2:probe2 0.020923974661951 0.0525420608421947 0.398232850530821 0.690577435195883 df.mm.trans2:probe3 0.0940514039802256 0.0525420608421947 1.79002122247737 0.0738735397007273 . df.mm.trans2:probe4 0.00994206314556388 0.0525420608421947 0.189221035227833 0.849973281009966 df.mm.trans2:probe5 -0.0105746476114347 0.0525420608421947 -0.201260617530681 0.840552035593513 df.mm.trans2:probe6 -1.89384148935644e-05 0.0525420608421947 -0.000360442940189275 0.999712508654872 df.mm.trans3:probe2 0.0284114803181919 0.0525420608421947 0.540737836749936 0.588856704253621 df.mm.trans3:probe3 0.0306851088881125 0.0525420608421947 0.584010379422924 0.559397525381021 df.mm.trans3:probe4 -0.0943153593632149 0.0525420608421947 -1.79504491927872 0.0730687729303442 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.32526969412482 0.217444827226754 19.8913432399768 2.07041126714176e-70 *** df.mm.trans1 0.0288639349955069 0.193095214849283 0.149480322534331 0.881216788017146 df.mm.trans2 -0.134163213638559 0.175645208092844 -0.763830764842965 0.445220075442171 df.mm.exp2 0.179037082125967 0.236795841895943 0.756082035446562 0.449849038193449 df.mm.exp3 0.066157189015262 0.236795841895943 0.279384927055999 0.78003023284773 df.mm.exp4 -0.0439831973662801 0.236795841895943 -0.18574311531031 0.852698895731965 df.mm.exp5 0.150078880363625 0.236795841895943 0.633790184667073 0.526420452407016 df.mm.exp6 0.203743108299037 0.236795841895943 0.86041674831676 0.389847771779271 df.mm.exp7 -0.449004137417917 0.236795841895943 -1.8961656329051 0.0583410392991066 . df.mm.exp8 0.0764897041531203 0.236795841895943 0.323019625432159 0.746774821440891 df.mm.trans1:exp2 -0.216923030970826 0.224848761216507 -0.964750838729106 0.334995804717095 df.mm.trans2:exp2 -0.172873627887065 0.189339601169352 -0.91303471022124 0.361532045255419 df.mm.trans1:exp3 -0.153611295964595 0.224848761216507 -0.683176083041357 0.494716890332748 df.mm.trans2:exp3 0.0264972191686927 0.189339601169352 0.139945468380873 0.888742487979322 df.mm.trans1:exp4 0.0638068787178645 0.224848761216507 0.283776874609617 0.776663530032337 df.mm.trans2:exp4 0.0445486425637977 0.189339601169352 0.235284337183914 0.81405542111044 df.mm.trans1:exp5 -0.233908914895613 0.224848761216507 -1.04029443449049 0.298554770644316 df.mm.trans2:exp5 0.0133621658913431 0.189339601169352 0.0705724835629682 0.943757744460774 df.mm.trans1:exp6 -0.221634599770216 0.224848761216507 -0.985705229466681 0.324611216062956 df.mm.trans2:exp6 -0.0653930201161902 0.189339601169352 -0.345374236093907 0.729914625901377 df.mm.trans1:exp7 0.275190504063923 0.224848761216507 1.22389157305137 0.221396279966781 df.mm.trans2:exp7 0.330327406418362 0.189339601169352 1.74462925018472 0.0814790914938674 . df.mm.trans1:exp8 -0.07770036714999 0.224848761216507 -0.345567245866087 0.72976960957589 df.mm.trans2:exp8 -0.171586177130915 0.189339601169352 -0.906235019357848 0.365116760127934 df.mm.trans1:probe2 0.0564169793497724 0.123154738545374 0.458098324239362 0.647020992092901 df.mm.trans1:probe3 0.0543761328488955 0.123154738545374 0.441526923699016 0.658965067361307 df.mm.trans1:probe4 0.085516656209885 0.123154738545374 0.694383807070308 0.487667102344114 df.mm.trans1:probe5 -0.0732771436536763 0.123154738545374 -0.595000602649803 0.552031320087122 df.mm.trans1:probe6 0.105862990324967 0.123154738545374 0.859593317929571 0.390301368202568 df.mm.trans1:probe7 -0.151193134650254 0.123154738545374 -1.22766802508821 0.21997562949801 df.mm.trans1:probe8 0.0890595392708865 0.123154738545374 0.72315154351998 0.469823275780544 df.mm.trans1:probe9 0.225746695033528 0.123154738545374 1.83303296080935 0.0672132938909793 . df.mm.trans1:probe10 -0.109547760488709 0.123154738545374 -0.88951315867029 0.374026529399393 df.mm.trans1:probe11 -0.0424403642462561 0.123154738545374 -0.344610079543305 0.730488863696261 df.mm.trans1:probe12 0.165033054793074 0.123154738545374 1.34004632499196 0.180655977192230 df.mm.trans1:probe13 -0.0457895016782728 0.123154738545374 -0.371804627407029 0.710148476877014 df.mm.trans1:probe14 -0.222236585176715 0.123154738545374 -1.80453133839293 0.0715687092645267 . df.mm.trans1:probe15 0.0271155012989266 0.123154738545374 0.220174242738832 0.825798304110647 df.mm.trans1:probe16 0.088383289777917 0.123154738545374 0.717660488113123 0.473201052763956 df.mm.trans1:probe17 -0.0465820060500002 0.123154738545374 -0.378239656875550 0.70536483715646 df.mm.trans1:probe18 -0.00501075722449203 0.123154738545374 -0.040686678268948 0.967557040189885 df.mm.trans1:probe19 0.233055914258093 0.123154738545374 1.89238284300550 0.0588437044070938 . df.mm.trans1:probe20 -0.0616853972252854 0.123154738545374 -0.500877172521939 0.616611720694532 df.mm.trans1:probe21 -0.2013549889815 0.123154738545374 -1.63497557105620 0.102494312400213 df.mm.trans1:probe22 0.0251570635966758 0.123154738545374 0.204271990617781 0.838199086351629 df.mm.trans2:probe2 0.0428799501836867 0.123154738545374 0.348179458542625 0.727807899178564 df.mm.trans2:probe3 -0.00271655957128849 0.123154738545374 -0.0220581002677995 0.982407763678735 df.mm.trans2:probe4 -0.0435953261926258 0.123154738545374 -0.353988216024380 0.72345207584996 df.mm.trans2:probe5 0.160676777743518 0.123154738545374 1.30467393817997 0.192423665890845 df.mm.trans2:probe6 0.153065723421584 0.123154738545374 1.24287319537600 0.214321987715846 df.mm.trans3:probe2 0.355006553626392 0.123154738545374 2.88260571878521 0.00406235075105549 ** df.mm.trans3:probe3 0.230621033540630 0.123154738545374 1.87261193734468 0.0615298788423062 . df.mm.trans3:probe4 0.0493005592166704 0.123154738545374 0.400313944871124 0.68904487369655