fitVsDatCorrelation=0.863181413529289 cont.fitVsDatCorrelation=0.29161066347724 fstatistic=6364.02510265944,44,508 cont.fstatistic=1764.75187061969,44,508 residuals=-0.583939937710963,-0.117122171517098,0.00489060868426162,0.111382492354311,0.602321427331577 cont.residuals=-0.846209586878632,-0.298866662410759,0.0115594671142131,0.270897014602636,1.02223842328121 predictedValues: Include Exclude Both Lung 82.251441282295 113.226175012987 95.5694214873097 cerebhem 68.4001959830287 93.531377288873 77.0907677301205 cortex 83.8870706892036 100.247569953802 85.5305921474855 heart 65.9552756442234 96.844157804332 65.8363181273653 kidney 76.8069683376269 132.769939279826 75.2101330707715 liver 71.1105570355854 116.111277233827 72.7981532451084 stomach 67.5085860728474 98.2308829277163 69.1070236176138 testicle 64.1144013025091 96.8422903199146 62.1355074109814 diffExp=-30.9747337306918,-25.1311813058442,-16.360499264598,-30.8888821601086,-55.9629709421989,-45.0007201982419,-30.7222968548689,-32.7278890174055 diffExpScore=0.996279335211421 diffExp1.5=0,0,0,0,-1,-1,0,-1 diffExp1.5Score=0.75 diffExp1.4=0,0,0,-1,-1,-1,-1,-1 diffExp1.4Score=0.833333333333333 diffExp1.3=-1,-1,0,-1,-1,-1,-1,-1 diffExp1.3Score=0.875 diffExp1.2=-1,-1,0,-1,-1,-1,-1,-1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 79.2711760838137 86.2218988269819 99.0956257163184 cerebhem 89.218030050053 77.9432127732801 81.4596156530323 cortex 75.831742041019 98.7143569755134 77.324551502137 heart 88.180970407371 96.4519999020664 89.1414246636421 kidney 72.5468814907968 91.1600760069742 88.2241585189268 liver 76.1666685492151 90.234731711302 94.1929981501196 stomach 85.3704866344853 79.2713297378208 76.5800953459347 testicle 84.0915628272258 87.803239735398 85.4881418705461 cont.diffExp=-6.95072274316814,11.2748172767729,-22.8826149344944,-8.27102949469536,-18.6131945161774,-14.0680631620868,6.0991568966645,-3.71167690817225 cont.diffExpScore=1.58062657024091 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,-1,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,-1,0,-1,0,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.463681363611163 cont.tran.correlation=-0.472733453188646 tran.covariance=0.00621817709176002 cont.tran.covariance=-0.0031578935356434 tran.mean=89.2398853855373 cont.tran.mean=84.9048977345823 weightedLogRatios: wLogRatio Lung -1.46046858497766 cerebhem -1.37116981540489 cortex -0.805077331337596 heart -1.68287221472985 kidney -2.52586950141909 liver -2.21101467112361 stomach -1.65021018896680 testicle -1.800964343124 cont.weightedLogRatios: wLogRatio Lung -0.371070567097871 cerebhem 0.597630783004665 cortex -1.17626044775532 heart -0.405615580046053 kidney -1.00453009251396 liver -0.748752775449768 stomach 0.326881970803358 testicle -0.192356385393037 varWeightedLogRatios=0.274498234529789 cont.varWeightedLogRatios=0.378699134178346 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.29820112185925 0.100089204774441 42.9437033848514 3.45236621103161e-171 *** df.mm.trans1 -0.152202743883270 0.0862480603763228 -1.76470917976786 0.0782135102079224 . df.mm.trans2 0.436003826655359 0.0805513838944674 5.41274160139296 9.58434042220355e-08 *** df.mm.exp2 -0.160626243232150 0.108296876043749 -1.48320292422158 0.13864062225262 df.mm.exp3 0.00892483918521574 0.108296876043749 0.082410864572034 0.93435245746858 df.mm.exp4 -0.00440706656755365 0.108296876043749 -0.0406943092778902 0.967555586659378 df.mm.exp5 0.330311880236363 0.108296876043749 3.05005917347908 0.00240742566245924 ** df.mm.exp6 0.151778867936414 0.108296876043749 1.40150735165343 0.161673059342463 df.mm.exp7 -0.0153962927499050 0.108296876043749 -0.142167468835254 0.8870040918316 df.mm.exp8 0.0251198170751387 0.108296876043749 0.231953293509510 0.816667698314423 df.mm.trans1:exp2 -0.0237789795754415 0.0977564422312795 -0.243247186913609 0.807912104913904 df.mm.trans2:exp2 -0.0304641578622571 0.0859327099223566 -0.354511778923097 0.723102491617349 df.mm.trans1:exp3 0.0107657459082255 0.0977564422312795 0.110128249990472 0.912351156126834 df.mm.trans2:exp3 -0.130669380224575 0.0859327099223566 -1.52060118135039 0.128982103429516 df.mm.trans1:exp4 -0.216396975544111 0.0977564422312796 -2.2136339110229 0.0272974501214212 * df.mm.trans2:exp4 -0.151877234571736 0.0859327099223566 -1.76739724266769 0.077762031303591 . df.mm.trans1:exp5 -0.398797423316876 0.0977564422312796 -4.07950017629908 5.23939484097414e-05 *** df.mm.trans2:exp5 -0.171081396732118 0.0859327099223566 -1.99087631341658 0.0470297394549152 * df.mm.trans1:exp6 -0.2973239733173 0.0977564422312795 -3.0414770273029 0.00247584277215236 ** df.mm.trans2:exp6 -0.126617217222708 0.0859327099223566 -1.47344611076634 0.141250132958908 df.mm.trans1:exp7 -0.182129829062711 0.0977564422312795 -1.86309796986898 0.063025364797836 . df.mm.trans2:exp7 -0.126670418302297 0.0859327099223566 -1.47406521238243 0.141083433949623 df.mm.trans1:exp8 -0.274231721757919 0.0977564422312795 -2.80525472796075 0.00522043031756775 ** df.mm.trans2:exp8 -0.181423401798132 0.0859327099223566 -2.11122635329498 0.0352407244433640 * df.mm.trans1:probe2 0.0269609925095992 0.0570774652965285 0.472357915151481 0.63687418949754 df.mm.trans1:probe3 0.146090247398666 0.0570774652965285 2.55950832153633 0.0107704442410140 * df.mm.trans1:probe4 0.037787656632973 0.0570774652965285 0.6620416032257 0.50824476553792 df.mm.trans1:probe5 -0.0892615093544723 0.0570774652965285 -1.56386603523372 0.118471735999521 df.mm.trans1:probe6 0.046448696679668 0.0570774652965285 0.813783450935637 0.416150287542128 df.mm.trans1:probe7 0.648271168464299 0.0570774652965285 11.3577427640909 8.5219340244274e-27 *** df.mm.trans1:probe8 0.773629446438493 0.0570774652965285 13.5540259613726 6.06428698950904e-36 *** df.mm.trans1:probe9 0.527481497690617 0.0570774652965285 9.24150178972119 6.65140607981551e-19 *** df.mm.trans1:probe10 0.777776343968912 0.0570774652965285 13.6266798101179 2.92833934084945e-36 *** df.mm.trans1:probe11 0.669586454581481 0.0570774652965285 11.7311876254990 2.7318279819518e-28 *** df.mm.trans1:probe12 0.919532094974298 0.0570774652965285 16.1102475416025 1.80428507639660e-47 *** df.mm.trans2:probe2 -0.00660689996333864 0.0570774652965285 -0.115753212393271 0.907893873949672 df.mm.trans2:probe3 -0.086899869992658 0.0570774652965285 -1.52248999743062 0.128508567439873 df.mm.trans2:probe4 0.0691586076856377 0.0570774652965285 1.21166220900570 0.226205005527256 df.mm.trans2:probe5 -0.0115601477400097 0.0570774652965285 -0.202534357122420 0.839580089790427 df.mm.trans2:probe6 -0.0170850858811915 0.0570774652965285 -0.299331545162897 0.76480951124253 df.mm.trans3:probe2 0.355602412444593 0.0570774652965285 6.2301717603816 9.78618867365141e-10 *** df.mm.trans3:probe3 -0.18642732640446 0.0570774652965285 -3.26621593015621 0.00116356384329726 ** df.mm.trans3:probe4 -0.149164970845634 0.0570774652965285 -2.61337762759249 0.00923103469968828 ** df.mm.trans3:probe5 -0.130756588490053 0.0570774652965285 -2.29086186309688 0.0223800613249593 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.22196453233661 0.189622093041089 22.2651509886127 3.79346384174983e-77 *** df.mm.trans1 0.0810865274671439 0.163399617033114 0.496246741206935 0.619934979801145 df.mm.trans2 0.255924059893708 0.152607087306239 1.67701293833189 0.0941551218358222 . df.mm.exp2 0.213242810930115 0.205171780028679 1.03933791918317 0.299142025865526 df.mm.exp3 0.339022368619124 0.205171780028679 1.65238303519000 0.099074382541736 . df.mm.exp4 0.324499034236257 0.205171780028679 1.58159681702278 0.114364014602824 df.mm.exp5 0.0832557256386362 0.205171780028679 0.405785462440295 0.685071098789867 df.mm.exp6 0.0562790273388432 0.205171780028679 0.274301988952751 0.783964062468718 df.mm.exp7 0.247826306680133 0.205171780028679 1.20789665443021 0.227649042275112 df.mm.exp8 0.224913488063565 0.205171780028679 1.09622038680040 0.273501739294443 df.mm.trans1:exp2 -0.0950342443329167 0.185202602277835 -0.51313665771472 0.608078804235673 df.mm.trans2:exp2 -0.314186482881518 0.16280217584795 -1.92986660801729 0.0541798099718137 . df.mm.trans1:exp3 -0.383379986359973 0.185202602277835 -2.07005723269935 0.0389517690001505 * df.mm.trans2:exp3 -0.203716164135231 0.16280217584795 -1.25131106555660 0.211396936865377 df.mm.trans1:exp4 -0.217982432689545 0.185202602277835 -1.17699443748925 0.239748901178380 df.mm.trans2:exp4 -0.212377752104131 0.16280217584795 -1.30451421179089 0.192649001359764 df.mm.trans1:exp5 -0.171897314677108 0.185202602277835 -0.928158203842262 0.353766295711464 df.mm.trans2:exp5 -0.0275628796079766 0.16280217584795 -0.169302894536982 0.865625793231816 df.mm.trans1:exp6 -0.0962296641256234 0.185202602277835 -0.519591317519734 0.603574766378677 df.mm.trans2:exp6 -0.0107888142740940 0.16280217584795 -0.0662694722469208 0.947189359249796 df.mm.trans1:exp7 -0.173700438544738 0.185202602277835 -0.937894157038673 0.348744492491359 df.mm.trans2:exp7 -0.331873977300991 0.16280217584795 -2.03851069908885 0.0420161659694842 * df.mm.trans1:exp8 -0.165881832387227 0.185202602277835 -0.8956776543473 0.370849156693379 df.mm.trans2:exp8 -0.206739281188794 0.16280217584795 -1.26988033244641 0.204708630424763 df.mm.trans1:probe2 0.165435876157778 0.108135022746946 1.52990096968793 0.126663677864075 df.mm.trans1:probe3 0.0239722669786649 0.108135022746946 0.221688277948247 0.824645642442944 df.mm.trans1:probe4 0.0956050035260124 0.108135022746946 0.884126170202456 0.377046347353238 df.mm.trans1:probe5 0.104183572981251 0.108135022746946 0.963458187132015 0.335776030060781 df.mm.trans1:probe6 0.114443344891882 0.108135022746946 1.05833745612371 0.290404600538407 df.mm.trans1:probe7 0.174505269319411 0.108135022746946 1.61377197587301 0.107197853396385 df.mm.trans1:probe8 0.0987524024428222 0.108135022746946 0.913232363893053 0.361553553397909 df.mm.trans1:probe9 0.239502060755891 0.108135022746946 2.21484265385847 0.0272138053500673 * df.mm.trans1:probe10 0.108445469463146 0.108135022746946 1.00287091738009 0.316400596530080 df.mm.trans1:probe11 -0.000232819347306013 0.108135022746946 -0.00215304294012912 0.998282966817395 df.mm.trans1:probe12 0.0623874500413902 0.108135022746946 0.576940277595239 0.564235370339511 df.mm.trans2:probe2 0.0333749060102335 0.108135022746946 0.308641041194734 0.757721125578182 df.mm.trans2:probe3 0.0915767998204592 0.108135022746946 0.846874560102184 0.397463820325887 df.mm.trans2:probe4 -0.061950026128103 0.108135022746946 -0.572895113483045 0.566969194802537 df.mm.trans2:probe5 -0.209205788652826 0.108135022746946 -1.93467188833357 0.0535854274618265 . df.mm.trans2:probe6 -0.0844042922415477 0.108135022746946 -0.78054537833749 0.435433478679181 df.mm.trans3:probe2 0.000186252288048272 0.108135022746946 0.00172240485382922 0.998626396594601 df.mm.trans3:probe3 -0.0216323850775723 0.108135022746946 -0.200049757498048 0.841521723014555 df.mm.trans3:probe4 0.0467535629676304 0.108135022746946 0.432362816226910 0.665661212842983 df.mm.trans3:probe5 0.160243472654813 0.108135022746946 1.48188319181113 0.138991397554671