fitVsDatCorrelation=0.932797054904156 cont.fitVsDatCorrelation=0.258624250917349 fstatistic=4310.81613627202,55,761 cont.fstatistic=588.156448977827,55,761 residuals=-1.77087640773826,-0.140051767984578,0.01050671182271,0.155700086202133,1.23206711555237 cont.residuals=-1.73127686480813,-0.595551644800122,-0.172163128994079,0.57311723035561,2.53702134828790 predictedValues: Include Exclude Both Lung 153.268535094502 80.176572502844 77.9798214510543 cerebhem 277.184013742654 95.726572492251 132.358186623518 cortex 352.10306972712 96.2639370827919 136.931433026057 heart 562.029053980104 161.633310479941 244.514324797938 kidney 458.302038443101 101.752495161233 194.493771536428 liver 94.5694487771992 88.8956320172356 59.7391866372724 stomach 185.821006246622 97.8463976137177 92.4742457573088 testicle 131.000860312793 88.6908506179263 79.689128731617 diffExp=73.0919625916581,181.457441250403,255.839132644328,400.395743500163,356.549543281868,5.67381675996351,87.974608632904,42.3100096948669 diffExpScore=0.999287897519872 diffExp1.5=1,1,1,1,1,0,1,0 diffExp1.5Score=0.857142857142857 diffExp1.4=1,1,1,1,1,0,1,1 diffExp1.4Score=0.875 diffExp1.3=1,1,1,1,1,0,1,1 diffExp1.3Score=0.875 diffExp1.2=1,1,1,1,1,0,1,1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 197.495045278342 121.790914384457 181.096181153184 cerebhem 169.053075305306 123.807793068262 201.846366384480 cortex 184.056275499796 169.764720360694 143.664512195577 heart 156.047400200506 196.080626981682 200.282079719117 kidney 174.877826720571 179.951883236994 223.200210490211 liver 190.80394092757 154.715592813854 162.006802965133 stomach 167.092688248149 125.211049423844 210.539140357455 testicle 201.448527652307 204.171527366453 200.746422319680 cont.diffExp=75.7041308938854,45.2452822370446,14.2915551391021,-40.0332267811758,-5.07405651642335,36.0883481137156,41.8816388243045,-2.72299971414608 cont.diffExpScore=1.56893967775177 cont.diffExp1.5=1,0,0,0,0,0,0,0 cont.diffExp1.5Score=0.5 cont.diffExp1.4=1,0,0,0,0,0,0,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=1,1,0,0,0,0,1,0 cont.diffExp1.3Score=0.75 cont.diffExp1.2=1,1,0,-1,0,1,1,0 cont.diffExp1.2Score=1.25 tran.correlation=0.804705759653481 cont.tran.correlation=0.0378333642176821 tran.covariance=0.0977307420248052 cont.tran.covariance=0.000410287424792215 tran.mean=189.078987143252 cont.tran.mean=169.773055466799 weightedLogRatios: wLogRatio Lung 3.05073341462245 cerebhem 5.414898989313 cortex 6.76362929070889 heart 7.11399325673576 kidney 8.08934889237085 liver 0.279559947842662 stomach 3.14541065144783 testicle 1.82549102133247 cont.weightedLogRatios: wLogRatio Lung 2.43831328886193 cerebhem 1.54946039330915 cortex 0.418271348792342 heart -1.17936098683451 kidney -0.148111765484189 liver 1.07898691243701 stomach 1.43531685976088 testicle -0.0713251955141417 varWeightedLogRatios=7.79504092226728 cont.varWeightedLogRatios=1.33606765803987 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.25031792291021 0.148196279209998 35.4281359214855 3.6254721280108e-163 *** df.mm.trans1 -0.00794097936062478 0.129767488824324 -0.0611939048260004 0.951220851150448 df.mm.trans2 -0.921628813459731 0.116371545464525 -7.91970932224737 8.43323658202475e-15 *** df.mm.exp2 0.240692957782925 0.153466249476395 1.56837714223248 0.117208830744416 df.mm.exp3 0.451564617148081 0.153466249476396 2.94243599937285 0.00335536016965134 ** df.mm.exp4 0.857637125576058 0.153466249476395 5.58844129248086 3.19256303084901e-08 *** df.mm.exp5 0.419698867056875 0.153466249476396 2.73479588175789 0.00638710824909789 ** df.mm.exp6 -0.113163496898795 0.153466249476396 -0.737383609001278 0.461116389373816 df.mm.exp7 0.221279795453444 0.153466249476396 1.44187921584334 0.149747841353634 df.mm.exp8 -0.0777453286007186 0.153466249476396 -0.506595612168632 0.612585328221295 df.mm.trans1:exp2 0.351797123102874 0.143964103029016 2.44364473991110 0.0147658829199774 * df.mm.trans2:exp2 -0.063428392137995 0.114843625194770 -0.552302246036063 0.580903459719804 df.mm.trans1:exp3 0.380167813027904 0.143964103029016 2.64071254589960 0.00844282045249776 ** df.mm.trans2:exp3 -0.268702212370803 0.114843625194770 -2.33972248712191 0.0195557321173038 * df.mm.trans1:exp4 0.441724906168963 0.143964103029016 3.068298950051 0.00222917214079208 ** df.mm.trans2:exp4 -0.156538230291371 0.114843625194770 -1.36305545933341 0.173268189075044 df.mm.trans1:exp5 0.675638057998114 0.143964103029016 4.69310087572274 3.19081124270178e-06 *** df.mm.trans2:exp5 -0.181386879333877 0.114843625194770 -1.57942488341214 0.114654173618693 df.mm.trans1:exp6 -0.369693545104604 0.143964103029016 -2.56795643723833 0.0104197771680934 * df.mm.trans2:exp6 0.216395145779007 0.114843625194770 1.88425909937979 0.0599108740602315 . df.mm.trans1:exp7 -0.0286874313722769 0.143964103029016 -0.199267947833461 0.842106379272657 df.mm.trans2:exp7 -0.0221122764510103 0.114843625194770 -0.192542480381552 0.847368665585825 df.mm.trans1:exp8 -0.0792422952323612 0.143964103029016 -0.550430930802173 0.582185367783905 df.mm.trans2:exp8 0.178670704086264 0.114843625194770 1.55577380793445 0.120177644105062 df.mm.trans1:probe2 -0.139201407780617 0.0881596484246388 -1.57896963370504 0.114758570214198 df.mm.trans1:probe3 -0.590737865850028 0.0881596484246388 -6.70077383934903 4.03425652074386e-11 *** df.mm.trans1:probe4 1.38103999544972 0.0881596484246388 15.6652166850492 3.78971037845874e-48 *** df.mm.trans1:probe5 0.258861073155021 0.0881596484246388 2.93627615105909 0.00342194562362014 ** df.mm.trans1:probe6 -0.414918667204372 0.0881596484246388 -4.70644648224811 2.99449576764845e-06 *** df.mm.trans1:probe7 -0.627184588086919 0.0881596484246388 -7.11419112138422 2.60202087809846e-12 *** df.mm.trans1:probe8 -0.83111575953176 0.0881596484246388 -9.42739421473783 4.94040347809260e-20 *** df.mm.trans1:probe9 -0.71322339490613 0.0881596484246388 -8.09013429217351 2.35109981228898e-15 *** df.mm.trans1:probe10 0.0660747796546674 0.0881596484246388 0.74949005395762 0.453793510806268 df.mm.trans1:probe11 -0.326624507423569 0.0881596484246388 -3.70492071214163 0.000226765840537650 *** df.mm.trans1:probe12 -0.655484708618338 0.0881596484246388 -7.4352010282648 2.81732245791512e-13 *** df.mm.trans1:probe13 -1.25387684278047 0.0881596484246388 -14.2227976765620 7.00702674271166e-41 *** df.mm.trans1:probe14 -0.983374022352459 0.0881596484246388 -11.1544685116692 7.2436112944534e-27 *** df.mm.trans1:probe15 -0.333993078833354 0.0881596484246388 -3.78850284457361 0.000163520356188624 *** df.mm.trans1:probe16 -0.256892852353563 0.0881596484246388 -2.91395050847057 0.00367350093918794 ** df.mm.trans1:probe17 0.116723774300752 0.0881596484246388 1.32400453480177 0.185899082283138 df.mm.trans1:probe18 -0.162571703092898 0.0881596484246388 -1.84406024749371 0.0655629111269717 . df.mm.trans1:probe19 -0.0545155115791752 0.0881596484246388 -0.618372606439969 0.536514780603363 df.mm.trans1:probe20 0.00259431255647516 0.0881596484246388 0.0294274376410751 0.976531405707267 df.mm.trans1:probe21 -0.424712014388524 0.0881596484246388 -4.81753298677885 1.75443225037202e-06 *** df.mm.trans1:probe22 0.0579409702823219 0.0881596484246388 0.657227782978868 0.511233182491788 df.mm.trans2:probe2 0.0733105133652743 0.0881596484246388 0.831565400671284 0.405915143519964 df.mm.trans2:probe3 0.117314313345884 0.0881596484246388 1.33070305340621 0.183685314712101 df.mm.trans2:probe4 0.208240735143077 0.0881596484246388 2.36208672407634 0.0184232311106341 * df.mm.trans2:probe5 0.0176138387936707 0.0881596484246388 0.199794793972295 0.841694447947224 df.mm.trans2:probe6 0.250027591164241 0.0881596484246388 2.83607745303080 0.00468840350301825 ** df.mm.trans3:probe2 0.780848666595595 0.0881596484246388 8.85721166711645 5.71791510191612e-18 *** df.mm.trans3:probe3 -0.273624899887191 0.0881596484246388 -3.10374309308972 0.00198162565742214 ** df.mm.trans3:probe4 -0.210696713546075 0.0881596484246388 -2.38994502939953 0.0170932598570938 * df.mm.trans3:probe5 -0.0828556328835109 0.0881596484246388 -0.939836244405376 0.347599775794477 df.mm.trans3:probe6 0.000540362436175944 0.0881596484246388 0.00612936242183247 0.995111113356806 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.14696486483427 0.397207330572122 12.9578798493492 7.64720976998778e-35 *** df.mm.trans1 0.170517971440443 0.34781303623634 0.490257562757295 0.624092966608855 df.mm.trans2 -0.233724194998894 0.3119081745839 -0.749336548523273 0.453885949037991 df.mm.exp2 -0.247554794831215 0.411332319626295 -0.601836478728742 0.547462243766165 df.mm.exp3 0.493183254452211 0.411332319626295 1.19898979710682 0.230905119901284 df.mm.exp4 0.139967996805475 0.411332319626295 0.340279599066367 0.733739884783864 df.mm.exp5 0.0597169462707841 0.411332319626296 0.145179319546391 0.884607735843983 df.mm.exp6 0.316205654917653 0.411332319626295 0.768735253298191 0.44228899296613 df.mm.exp7 -0.290113165982774 0.411332319626295 -0.70530116925008 0.480838653912312 df.mm.exp8 0.433460855678876 0.411332319626296 1.05379722184895 0.292310229084074 df.mm.trans1:exp2 0.092054018682083 0.385863918899981 0.238566018156116 0.811506343840634 df.mm.trans2:exp2 0.263979344006820 0.307812922429718 0.857596691922814 0.391385202082791 df.mm.trans1:exp3 -0.563655195324939 0.385863918899981 -1.46076159940480 0.144493612107581 df.mm.trans2:exp3 -0.161075531838658 0.307812922429718 -0.52329034975923 0.60092454999704 df.mm.trans1:exp4 -0.3755216850973 0.385863918899981 -0.973197199074316 0.330764534591514 df.mm.trans2:exp4 0.336252182041471 0.307812922429718 1.09239137651294 0.275006811845895 df.mm.trans1:exp5 -0.181342846009143 0.385863918899981 -0.469965801742008 0.638514093616522 df.mm.trans2:exp5 0.330666795585479 0.307812922429718 1.07424598348687 0.283052940661908 df.mm.trans1:exp6 -0.350672738188339 0.385863918899981 -0.908798985891284 0.36374395902543 df.mm.trans2:exp6 -0.0769228664827958 0.307812922429718 -0.24990135526347 0.802731078137362 df.mm.trans1:exp7 0.122948347054973 0.385863918899981 0.318631364667300 0.750093495549046 df.mm.trans2:exp7 0.317808116989864 0.307812922429718 1.03247165350061 0.302179224310319 df.mm.trans1:exp8 -0.413640449646476 0.385863918899981 -1.07198530203518 0.284066468379176 df.mm.trans2:exp8 0.0831938473488636 0.307812922429718 0.270274057021953 0.787022701693269 df.mm.trans1:probe2 -0.0823912953503673 0.236292427863906 -0.348683604020612 0.727423299327747 df.mm.trans1:probe3 -0.28657763030763 0.236292427863906 -1.21280919959351 0.225579123389171 df.mm.trans1:probe4 0.111043935693729 0.236292427863906 0.469942844540434 0.638530488252587 df.mm.trans1:probe5 0.0717323233499825 0.236292427863906 0.303574363336337 0.76153516236211 df.mm.trans1:probe6 -0.125271075744543 0.236292427863906 -0.53015273014459 0.596160689678792 df.mm.trans1:probe7 0.0434465027486313 0.236292427863906 0.183867520179930 0.854166366277389 df.mm.trans1:probe8 0.0355045272101528 0.236292427863906 0.150256728626962 0.88060188621193 df.mm.trans1:probe9 -0.0971361659517664 0.236292427863906 -0.411084548201065 0.681126273420301 df.mm.trans1:probe10 -0.0448444069143619 0.236292427863906 -0.189783512403497 0.849529385152332 df.mm.trans1:probe11 0.00145489743663729 0.236292427863906 0.00615719026542503 0.99508891765856 df.mm.trans1:probe12 -0.167240727583662 0.236292427863906 -0.707770151991437 0.479304707365583 df.mm.trans1:probe13 0.095876131828412 0.236292427863906 0.405752028091363 0.6850388647292 df.mm.trans1:probe14 -0.0284989143214227 0.236292427863906 -0.120608665199533 0.90403284085532 df.mm.trans1:probe15 0.202192001404113 0.236292427863906 0.855685487816678 0.392441099941853 df.mm.trans1:probe16 -0.0172275107792232 0.236292427863906 -0.0729075871578311 0.941898814087821 df.mm.trans1:probe17 -0.107801099891229 0.236292427863906 -0.456219020075063 0.648362657195256 df.mm.trans1:probe18 -0.318669935739595 0.236292427863906 -1.34862525481830 0.177858562844056 df.mm.trans1:probe19 0.274944703587797 0.236292427863906 1.16357813948297 0.244959709661011 df.mm.trans1:probe20 -0.296548059411101 0.236292427863906 -1.25500449630108 0.209862390177546 df.mm.trans1:probe21 -0.0283997714011574 0.236292427863906 -0.120189087978369 0.904365085079666 df.mm.trans1:probe22 -0.125129934365283 0.236292427863906 -0.529555413588423 0.596574660765674 df.mm.trans2:probe2 -0.345382518658228 0.236292427863906 -1.46167408655665 0.144243334844092 df.mm.trans2:probe3 0.0210715473017598 0.236292427863906 0.0891757196464038 0.92896572439047 df.mm.trans2:probe4 -0.331589469623285 0.236292427863906 -1.40330129332060 0.160934898609836 df.mm.trans2:probe5 -0.360952002683463 0.236292427863906 -1.52756483119872 0.127036129324356 df.mm.trans2:probe6 -0.314366498975761 0.236292427863906 -1.33041292020082 0.183780792555692 df.mm.trans3:probe2 0.0798747426089097 0.236292427863906 0.338033441574836 0.735431216343654 df.mm.trans3:probe3 0.140360710327629 0.236292427863906 0.594012730735793 0.552680130271615 df.mm.trans3:probe4 0.0148514937166180 0.236292427863906 0.0628521779173211 0.94990072035244 df.mm.trans3:probe5 0.233841846445889 0.236292427863906 0.98962903111128 0.322670021785418 df.mm.trans3:probe6 0.222689025542565 0.236292427863906 0.942429800039229 0.346271714643941