fitVsDatCorrelation=0.90096867476841 cont.fitVsDatCorrelation=0.23168609004498 fstatistic=9771.91849326105,54,738 cont.fstatistic=1933.01804977495,54,738 residuals=-0.550307456046538,-0.0966626866067791,0.00138167403577279,0.0911111924391322,0.91992558966256 cont.residuals=-0.850964000939064,-0.248070524159936,-0.0749927646601389,0.197902619814902,1.71859306687427 predictedValues: Include Exclude Both Lung 64.0241280849134 62.0162268073815 75.194165956342 cerebhem 67.8755740541324 62.098368823104 68.8031879945355 cortex 56.9175447635131 56.6751770197806 69.9184465531786 heart 57.8052496507505 56.798588329601 68.6835891437706 kidney 64.4122852808312 58.9795008938568 74.6844290007461 liver 65.6886143610085 57.3225210731657 65.4018705178274 stomach 60.3168933351235 63.4710617644551 84.2172376712706 testicle 62.1591229749826 64.2616678957222 68.924614853373 diffExp=2.00790127753183,5.77720523102836,0.242367743732501,1.00666132114949,5.43278438697438,8.36609328784274,-3.15416842933168,-2.10254492073952 diffExpScore=1.5121271056283 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 70.5457630805546 58.7932642662097 62.6236344918205 cerebhem 69.4044081128002 71.5286231474646 69.2719772516549 cortex 68.2892301320391 73.406089297176 63.454486124401 heart 67.672780558652 62.2548284667219 65.4322084295473 kidney 68.4130115485007 58.4512984056841 69.3491350498426 liver 62.4660684562795 62.7620377505217 62.5267080111838 stomach 69.2908358145501 63.5960536088642 72.621882019765 testicle 64.5591671027896 72.953509352746 60.2098378563228 cont.diffExp=11.7524988143449,-2.12421503466440,-5.11685916513683,5.4179520919301,9.9617131428166,-0.295969294242141,5.69478220568594,-8.39434224995638 cont.diffExpScore=2.7246049079833 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=0.341838021571558 cont.tran.correlation=-0.179730012143936 tran.covariance=0.00116321946057368 cont.tran.covariance=-0.000700026676048964 tran.mean=61.3014078195201 cont.tran.mean=66.5241855688471 weightedLogRatios: wLogRatio Lung 0.132022756752075 cerebhem 0.371233144759217 cortex 0.0172377014109926 heart 0.071120957506474 kidney 0.363141381910325 liver 0.560841323195827 stomach -0.210263935618749 testicle -0.137930634582607 cont.weightedLogRatios: wLogRatio Lung 0.759032292548358 cerebhem -0.128277530273547 cortex -0.307796838888015 heart 0.348224926896909 kidney 0.652590665924364 liver -0.0195550891077305 stomach 0.359805453416037 testicle -0.516917651435864 varWeightedLogRatios=0.071413741288405 cont.varWeightedLogRatios=0.209574836211743 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.66248083798529 0.079507370107759 46.064670898074 2.80987233101363e-219 *** df.mm.trans1 0.229106935109192 0.0695271782317907 3.29521405780905 0.00103045033775124 ** df.mm.trans2 0.405169809843486 0.0624852594218395 6.4842462621172 1.63216264165537e-10 *** df.mm.exp2 0.148563436835653 0.0824591573659031 1.80166086558973 0.072006791526036 . df.mm.exp3 -0.134971995939104 0.0824591573659032 -1.63683452815532 0.102091371642313 df.mm.exp4 -0.0995016651350348 0.0824591573659031 -1.20667817030324 0.227942757240387 df.mm.exp5 -0.0373597416571874 0.0824591573659032 -0.453069651092937 0.650631759819547 df.mm.exp6 0.086485898696967 0.0824591573659032 1.04883316128487 0.294598260814461 df.mm.exp7 -0.149785761704041 0.0824591573659032 -1.81648426310474 0.0697016756592413 . df.mm.exp8 0.093065097035941 0.0824591573659032 1.12862051964678 0.259424696809540 df.mm.trans1:exp2 -0.0901472147371706 0.0771334788163216 -1.16871708783988 0.242895007286961 df.mm.trans2:exp2 -0.147239788681328 0.0618443680244274 -2.38081159828768 0.0175273435293818 * df.mm.trans1:exp3 0.0173156197266286 0.0771334788163216 0.224489028530172 0.82243894028693 df.mm.trans2:exp3 0.0449122422455905 0.0618443680244274 0.726213941225028 0.467937877996877 df.mm.trans1:exp4 -0.00267875254277113 0.0771334788163216 -0.0347287920093694 0.972305392837512 df.mm.trans2:exp4 0.0116170636998126 0.0618443680244273 0.187843518672939 0.851050954754186 df.mm.trans1:exp5 0.0434041081651209 0.0771334788163216 0.562714256263216 0.57380036832762 df.mm.trans2:exp5 -0.0128463907876007 0.0618443680244274 -0.207721271927729 0.835503914011059 df.mm.trans1:exp6 -0.0608202992605615 0.0771334788163216 -0.78850714623404 0.430653329210727 df.mm.trans2:exp6 -0.165188387770725 0.0618443680244273 -2.67103364538351 0.00772841377126711 ** df.mm.trans1:exp7 0.090137967373185 0.0771334788163216 1.16859720002816 0.242943298671911 df.mm.trans2:exp7 0.172973769984474 0.0618443680244273 2.79692032613467 0.00529352908542875 ** df.mm.trans1:exp8 -0.122627513869458 0.0771334788163216 -1.58980919506395 0.112306086635431 df.mm.trans2:exp8 -0.0574978617597103 0.0618443680244274 -0.929718640458897 0.352820779628709 df.mm.trans1:probe2 0.0292257704300271 0.0472344162964408 0.618738892561044 0.536279311253083 df.mm.trans1:probe3 -0.067468289626053 0.0472344162964408 -1.42837140619301 0.153608046586544 df.mm.trans1:probe4 0.0230881736073096 0.0472344162964408 0.488799807801357 0.625128654511852 df.mm.trans1:probe5 -0.0299083507951038 0.0472344162964408 -0.633189803964138 0.5268058626112 df.mm.trans1:probe6 0.135346834862569 0.0472344162964408 2.86542833541414 0.00428287134153255 ** df.mm.trans1:probe7 0.225356680957005 0.0472344162964408 4.77102711596304 2.20963347279406e-06 *** df.mm.trans1:probe8 -0.0825964193279686 0.0472344162964408 -1.74864909538836 0.0807674505532029 . df.mm.trans1:probe9 -0.080825924510256 0.0472344162964408 -1.71116594313343 0.08747068331213 . df.mm.trans1:probe10 0.194232126385104 0.0472344162964408 4.11208905739647 4.36158772225639e-05 *** df.mm.trans1:probe11 0.411664723433119 0.0472344162964408 8.71535536396876 1.89683371839429e-17 *** df.mm.trans1:probe12 0.394482662883079 0.0472344162964408 8.35159389728299 3.32202081822296e-16 *** df.mm.trans1:probe13 0.362115230934138 0.0472344162964408 7.66634287722581 5.58633781709917e-14 *** df.mm.trans1:probe14 0.147988831449141 0.0472344162964408 3.13307209134057 0.00179827323247660 ** df.mm.trans1:probe15 0.279366260463908 0.0472344162964408 5.91446412104723 5.0916157136802e-09 *** df.mm.trans1:probe16 0.876121897652067 0.0472344162964408 18.5483798964207 2.46598259401457e-63 *** df.mm.trans1:probe17 0.982985537480418 0.0472344162964408 20.8107904056917 4.85177775289506e-76 *** df.mm.trans1:probe18 0.651357269405 0.0472344162964408 13.789887130543 1.17371649777886e-38 *** df.mm.trans1:probe19 1.16686677225055 0.0472344162964408 24.7037406988868 1.15809813435922e-98 *** df.mm.trans1:probe20 0.906084208127178 0.0472344162964408 19.1827120809674 7.51843659090559e-67 *** df.mm.trans1:probe21 0.701666498648946 0.0472344162964408 14.8549840066896 7.27076612248322e-44 *** df.mm.trans2:probe2 0.100634951807708 0.0472344162964408 2.13054293242723 0.0334568594176374 * df.mm.trans2:probe3 0.279301820371958 0.0472344162964408 5.9130998596251 5.13205815604849e-09 *** df.mm.trans2:probe4 0.153780097531055 0.0472344162964408 3.25567900672127 0.00118285769241005 ** df.mm.trans2:probe5 0.094840192759227 0.0472344162964408 2.00786206743860 0.0450219614502686 * df.mm.trans2:probe6 0.088388045601402 0.0472344162964408 1.87126363638503 0.0617039884765772 . df.mm.trans3:probe2 0.281780351244121 0.0472344162964408 5.96557284577587 3.78134031316056e-09 *** df.mm.trans3:probe3 -0.0327280418715792 0.0472344162964408 -0.69288549404696 0.488599391549887 df.mm.trans3:probe4 -0.248204831647180 0.0472344162964408 -5.25474539770871 1.94197944325027e-07 *** df.mm.trans3:probe5 -0.222406509084808 0.0472344162964408 -4.70856901647722 2.98003800911187e-06 *** df.mm.trans3:probe6 0.3614554192026 0.0472344162964408 7.65237400064656 6.17846506276805e-14 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.23297252928795 0.178259621140307 23.7461097595187 4.84269643360323e-93 *** df.mm.trans1 -0.0165347932589146 0.155883516631927 -0.106071466798870 0.91555447424005 df.mm.trans2 -0.173494229812346 0.140095171759478 -1.23840263467616 0.215960578031321 df.mm.exp2 0.0788614592083924 0.184877680291432 0.426560194200182 0.669824092473872 df.mm.exp3 0.176289905728670 0.184877680291432 0.953548884055527 0.340624249949641 df.mm.exp4 -0.0282406447637421 0.184877680291432 -0.152753132337148 0.878634712001774 df.mm.exp5 -0.138542900298568 0.184877680291432 -0.749376020297178 0.453869394095728 df.mm.exp6 -0.0547660540920756 0.184877680291432 -0.296228587494959 0.767138857035718 df.mm.exp7 -0.0875483666207288 0.184877680291432 -0.473547517919533 0.635962654811796 df.mm.exp8 0.166422582986029 0.184877680291432 0.900176715348705 0.368319853183319 df.mm.trans1:exp2 -0.0951726971771224 0.172937234528017 -0.550330861002081 0.582258987864814 df.mm.trans2:exp2 0.117208939077066 0.138658260218574 0.845308017656528 0.398212851669553 df.mm.trans1:exp3 -0.208799457268522 0.172937234528017 -1.20737132080538 0.227675985155208 df.mm.trans2:exp3 0.0456896918556624 0.138658260218574 0.329512946315924 0.741861508668773 df.mm.trans1:exp4 -0.0133369368004926 0.172937234528017 -0.077120099884169 0.938548905862837 df.mm.trans2:exp4 0.0854494477838687 0.138658260218574 0.616259338961634 0.537913402219774 df.mm.trans1:exp5 0.107844313213565 0.172937234528017 0.623603780342016 0.53308051332972 df.mm.trans2:exp5 0.132709506878066 0.138658260218574 0.957097735604567 0.338831346111053 df.mm.trans1:exp6 -0.0668720622310602 0.172937234528017 -0.38668400367086 0.699101626885723 df.mm.trans2:exp6 0.120089155320271 0.138658260218574 0.866080067144708 0.386727724092528 df.mm.trans1:exp7 0.0695994038040677 0.172937234528017 0.402454705570026 0.687465957766793 df.mm.trans2:exp7 0.166072489819424 0.138658260218574 1.19771075706298 0.231414168907438 df.mm.trans1:exp8 -0.255102081181393 0.172937234528017 -1.4751136843238 0.140608459327955 df.mm.trans2:exp8 0.0493725020533885 0.138658260218574 0.356073283881971 0.72188744765139 df.mm.trans1:probe2 -0.003486330726159 0.105901995530417 -0.0329203496940497 0.97374700609965 df.mm.trans1:probe3 -0.00109106767893708 0.105901995530417 -0.0103026168059667 0.991782630863797 df.mm.trans1:probe4 0.0126117479046703 0.105901995530417 0.119088859860512 0.905237364817323 df.mm.trans1:probe5 0.0227513881577135 0.105901995530417 0.214834366847969 0.829955808097526 df.mm.trans1:probe6 0.0472169816961635 0.105901995530417 0.445855448329131 0.655832435387435 df.mm.trans1:probe7 0.0622331193372642 0.105901995530417 0.58764822159928 0.556948192728844 df.mm.trans1:probe8 0.0910501601551214 0.105901995530417 0.859758682535594 0.390201271164506 df.mm.trans1:probe9 -0.0273516087904131 0.105901995530417 -0.258272836629951 0.796268357112265 df.mm.trans1:probe10 0.0753334707109502 0.105901995530417 0.71135081386935 0.477091643693937 df.mm.trans1:probe11 0.158669031753591 0.105901995530417 1.49826290769014 0.134492549974396 df.mm.trans1:probe12 0.0551706828127564 0.105901995530417 0.520959803792465 0.602551046017831 df.mm.trans1:probe13 0.0182016251520236 0.105901995530417 0.171872352932158 0.86358499988708 df.mm.trans1:probe14 0.0413743180809982 0.105901995530417 0.39068497126775 0.696142858400191 df.mm.trans1:probe15 -0.0593814648060579 0.105901995530417 -0.560720924177511 0.575157989592709 df.mm.trans1:probe16 0.0807776452298638 0.105901995530417 0.762758480850942 0.445851175311707 df.mm.trans1:probe17 0.134275105742610 0.105901995530417 1.26791856064737 0.205226899280135 df.mm.trans1:probe18 0.203864306372272 0.105901995530417 1.9250279973593 0.0546098985908076 . df.mm.trans1:probe19 0.0456489896169339 0.105901995530417 0.431049380970567 0.666558337749672 df.mm.trans1:probe20 0.0734523212969168 0.105901995530417 0.693587698031811 0.488159046753909 df.mm.trans1:probe21 0.0439244705735782 0.105901995530417 0.414765277590661 0.678434278522343 df.mm.trans2:probe2 0.140192776631924 0.105901995530417 1.32379730834872 0.185980265743074 df.mm.trans2:probe3 0.00946245556228632 0.105901995530417 0.0893510600522023 0.92882715829429 df.mm.trans2:probe4 -0.0158499097374926 0.105901995530417 -0.149665826957343 0.881069161625703 df.mm.trans2:probe5 0.00481224677856667 0.105901995530417 0.0454405675215489 0.963768438685853 df.mm.trans2:probe6 0.0359703776226650 0.105901995530417 0.33965722215625 0.734211318625612 df.mm.trans3:probe2 0.184277248336062 0.105901995530417 1.74007342744674 0.0822629757220635 . df.mm.trans3:probe3 0.110678788638094 0.105901995530417 1.04510578940229 0.296316268561352 df.mm.trans3:probe4 -0.00127555155843832 0.105901995530417 -0.0120446413880082 0.990393254137933 df.mm.trans3:probe5 0.09542671916663 0.105901995530417 0.901085184360118 0.367836988681482 df.mm.trans3:probe6 0.176088285990391 0.105901995530417 1.66274757249325 0.0967875799642663 .