fitVsDatCorrelation=0.9381298762113 cont.fitVsDatCorrelation=0.246428538045398 fstatistic=6909.47732303727,52,692 cont.fstatistic=870.490032012895,52,692 residuals=-0.675634171372679,-0.0844726707337152,6.57051915814728e-05,0.0815195195585436,1.81337372027526 cont.residuals=-1.04276489952985,-0.444609860273437,-0.09591250445154,0.385675965300786,2.19759151280817 predictedValues: Include Exclude Both Lung 66.1832978756314 102.424157168562 85.095491131153 cerebhem 60.960201017611 71.3365792212722 81.0526607730767 cortex 60.5640087576131 97.111593211996 88.961876468011 heart 65.9705357494597 98.3570757214074 81.7925059668853 kidney 63.6282039269981 100.691373905624 83.5485414766508 liver 67.58144816018 91.7642013961462 73.0360360645803 stomach 68.9052840239062 91.216237960092 90.1691600838388 testicle 66.5358070925546 119.366255477862 97.060830282788 diffExp=-36.2408592929311,-10.3763782036612,-36.5475844543828,-32.3865399719477,-37.0631699786263,-24.1827532359661,-22.3109539361858,-52.8304483853073 diffExpScore=0.996046472724098 diffExp1.5=-1,0,-1,0,-1,0,0,-1 diffExp1.5Score=0.8 diffExp1.4=-1,0,-1,-1,-1,0,0,-1 diffExp1.4Score=0.833333333333333 diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.875 diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 85.0807422038168 70.2596201493861 89.3638065294058 cerebhem 73.2061154913083 70.5396231252121 77.5106041993863 cortex 73.1553694105928 87.964712376282 111.294817175813 heart 70.4274335899396 65.257103025506 103.847729845210 kidney 75.7176651360365 87.074013904153 59.6100143291324 liver 79.6662291443722 89.3385179391266 60.4462305215077 stomach 71.1697153744075 70.6407365523303 67.4667580057422 testicle 78.0056244832363 68.1831943409412 84.718039759345 cont.diffExp=14.8211220544307,2.66649236609615,-14.8093429656892,5.17033056443358,-11.3563487681165,-9.67228879475437,0.528978822077292,9.82243014229515 cont.diffExpScore=17.9822537020022 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=1,0,-1,0,0,0,0,0 cont.diffExp1.2Score=2 tran.correlation=0.365327944653003 cont.tran.correlation=0.129946105753427 tran.covariance=0.00272692949316046 cont.tran.covariance=0.00126474054593301 tran.mean=80.7872662916823 cont.tran.mean=75.9804010154155 weightedLogRatios: wLogRatio Lung -1.92616117221487 cerebhem -0.658431568333909 cortex -2.04907072482796 heart -1.7529128355392 kidney -2.01160882654771 liver -1.33559527840287 stomach -1.22662197723097 testicle -2.62419026534843 cont.weightedLogRatios: wLogRatio Lung 0.832202966340484 cerebhem 0.158611088082489 cortex -0.808331547071992 heart 0.321496430599144 kidney -0.614451491879301 liver -0.508209103564864 stomach 0.0317912755759505 testicle 0.577291577995357 varWeightedLogRatios=0.366962771479669 cont.varWeightedLogRatios=0.349224423177832 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.81467858994804 0.105648486022344 36.1072717042178 2.59994672412933e-161 *** df.mm.trans1 0.0307336041475972 0.0948877903791782 0.323894191494855 0.746116008916098 df.mm.trans2 0.642554234855995 0.087262210197335 7.3634879680783 5.10510291953593e-13 *** df.mm.exp2 -0.395245315900692 0.119527617421587 -3.30672797155003 0.00099269886011557 *** df.mm.exp3 -0.186423035989166 0.119527617421587 -1.55966495451534 0.119296269225574 df.mm.exp4 -0.00414960126231456 0.119527617421587 -0.0347166734502743 0.972315676851338 df.mm.exp5 -0.0380875100352281 0.119527617421587 -0.318650290676248 0.750087864351122 df.mm.exp6 0.0638261275856577 0.119527617421587 0.533986445664151 0.593522401167148 df.mm.exp7 -0.133498344137930 0.119527617421587 -1.11688283442534 0.264432062117541 df.mm.exp8 0.0268222237800826 0.119527617421587 0.224401894379586 0.822510836854577 df.mm.trans1:exp2 0.313038391647344 0.114438981044956 2.73541750187701 0.00638972438806583 ** df.mm.trans2:exp2 0.0335319496888496 0.0996063478513225 0.336644706007102 0.736486815190802 df.mm.trans1:exp3 0.0976957045769584 0.114438981044956 0.853692541517647 0.393570776823454 df.mm.trans2:exp3 0.133161204089451 0.0996063478513225 1.33687467678480 0.181702871119225 df.mm.trans1:exp4 0.000929682412293354 0.114438981044956 0.00812382637283479 0.993520537028343 df.mm.trans2:exp4 -0.0363685068278403 0.0996063478513225 -0.365122380374048 0.715131620004737 df.mm.trans1:exp5 -0.00128379310474549 0.114438981044956 -0.0112181451898909 0.991052636168698 df.mm.trans2:exp5 0.0210250501437542 0.0996063478513225 0.21108142801438 0.832885876037979 df.mm.trans1:exp6 -0.0429207508606399 0.114438981044956 -0.375053591605984 0.707735529585185 df.mm.trans2:exp6 -0.173726463651799 0.0996063478513225 -1.74413044348450 0.0815803611652113 . df.mm.trans1:exp7 0.173803077260913 0.114438981044956 1.51874016767622 0.129284632898349 df.mm.trans2:exp7 0.017608678545315 0.0996063478513225 0.176782694327862 0.859730809235 df.mm.trans1:exp8 -0.0215101016714888 0.114438981044956 -0.187961317682816 0.850962076850691 df.mm.trans2:exp8 0.126251725163946 0.0996063478513225 1.26750681946893 0.205400474577924 df.mm.trans1:probe2 0.00232853836336326 0.057219490522478 0.040694846145974 0.967550908363313 df.mm.trans1:probe3 0.31910104222188 0.057219490522478 5.57678929518823 3.51515948714775e-08 *** df.mm.trans1:probe4 1.25213591354513 0.057219490522478 21.8830315004858 4.44691631957401e-81 *** df.mm.trans1:probe5 -0.0322773069003032 0.057219490522478 -0.564096370058091 0.572871320265229 df.mm.trans1:probe6 0.458645329946662 0.057219490522478 8.01554375543572 4.67010849775365e-15 *** df.mm.trans1:probe7 -0.117910402632504 0.057219490522478 -2.06066851619701 0.0397078811457173 * df.mm.trans1:probe8 -0.0579281742359379 0.057219490522478 -1.01238535518210 0.311707681012788 df.mm.trans1:probe9 0.08717530599618 0.057219490522478 1.52352468014258 0.128084332720863 df.mm.trans1:probe10 0.0307568661557736 0.057219490522478 0.537524292420798 0.591078316257683 df.mm.trans1:probe11 0.665555481979502 0.057219490522478 11.6316219508813 1.09157539543153e-28 *** df.mm.trans1:probe12 0.77237379856664 0.057219490522478 13.4984389325036 4.98804121571834e-37 *** df.mm.trans1:probe13 1.72723090978192 0.057219490522478 30.1860588762739 2.26506011295135e-128 *** df.mm.trans1:probe14 1.22430127628107 0.057219490522478 21.3965777238109 2.40093716120328e-78 *** df.mm.trans1:probe15 1.09581463944284 0.057219490522478 19.1510729899344 6.41236389400846e-66 *** df.mm.trans1:probe16 1.18681891794945 0.057219490522478 20.7415149473101 1.09628865509035e-74 *** df.mm.trans1:probe17 0.121590769001998 0.057219490522478 2.12498866892623 0.0339412995273622 * df.mm.trans1:probe18 -0.00591536140852781 0.057219490522478 -0.103380183124910 0.917691191608088 df.mm.trans1:probe19 0.0216117309440876 0.057219490522478 0.37769876569589 0.70577020110355 df.mm.trans1:probe20 -0.0125124543006658 0.057219490522478 -0.218674689103538 0.826967942305186 df.mm.trans1:probe21 -0.0574581594055828 0.057219490522478 -1.00417111164265 0.315647140780300 df.mm.trans1:probe22 -0.00604018470158353 0.057219490522478 -0.105561665202362 0.915960732511101 df.mm.trans2:probe2 0.127103221227519 0.057219490522478 2.22132738454894 0.0266516439459524 * df.mm.trans2:probe3 0.71372226875117 0.057219490522478 12.4734118083556 2.35623618793541e-32 *** df.mm.trans2:probe4 0.0535291749698155 0.057219490522478 0.935505969749715 0.349853948622408 df.mm.trans2:probe5 0.465629683270005 0.057219490522478 8.13760624252829 1.8717675641843e-15 *** df.mm.trans2:probe6 0.187023580235853 0.057219490522478 3.26852928133610 0.00113454671676668 ** df.mm.trans3:probe2 0.268390080480338 0.057219490522478 4.6905360049458 3.28403032818792e-06 *** df.mm.trans3:probe3 -0.400967214356296 0.057219490522478 -7.00752856579141 5.76658455187235e-12 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.27669742459578 0.295683616108981 14.4637619117168 1.27445582212974e-41 *** df.mm.trans1 0.206533349881941 0.265567127748262 0.77770675773441 0.437007705853739 df.mm.trans2 0.00176411603117539 0.244225041287889 0.00722332166215451 0.994238755106926 df.mm.exp2 -0.00404341367210533 0.334527824058273 -0.0120869278466983 0.990359745416356 df.mm.exp3 -0.145743627911728 0.334527824058273 -0.435669673582490 0.663212274381634 df.mm.exp4 -0.413090125121187 0.334527824058273 -1.2348453414423 0.217307187582857 df.mm.exp5 0.502864193451509 0.334527824058273 1.50320588389656 0.133242170467197 df.mm.exp6 0.565442077157287 0.334527824058273 1.6902691988299 0.0914268231549815 . df.mm.exp7 0.107957159751786 0.334527824058273 0.322715038893090 0.747008542498394 df.mm.exp8 -0.0634317159249385 0.334527824058273 -0.189615665314252 0.849665914090692 df.mm.trans1:exp2 -0.146278337780236 0.320286007052133 -0.456711609497276 0.648021664936867 df.mm.trans2:exp2 0.0080207552889141 0.278773186715227 0.0287716167520353 0.97705503349139 df.mm.trans1:exp3 -0.00527155846692433 0.320286007052133 -0.0164589096958778 0.986873027071684 df.mm.trans2:exp3 0.370482125779104 0.278773186715227 1.32897331391329 0.184294788317301 df.mm.trans1:exp4 0.224072280161129 0.320286007052133 0.699600592056638 0.484411870035241 df.mm.trans2:exp4 0.339227783561285 0.278773186715227 1.21685943888073 0.224072547321609 df.mm.trans1:exp5 -0.619453416977686 0.320286007052134 -1.93406331634356 0.0535127441701829 . df.mm.trans2:exp5 -0.288302942402422 0.278773186715227 -1.03418462083633 0.301411137373425 df.mm.trans1:exp6 -0.631197019713761 0.320286007052133 -1.97072930385941 0.049153008852801 * df.mm.trans2:exp6 -0.325206590833065 0.278773186715227 -1.16656337958812 0.243788601168787 df.mm.trans1:exp7 -0.28649049157728 0.320286007052133 -0.894483321997416 0.371374348152925 df.mm.trans2:exp7 -0.102547417131928 0.278773186715227 -0.367852512432202 0.713095695449796 df.mm.trans1:exp8 -0.0233880651188900 0.320286007052133 -0.073022438083232 0.94180936317162 df.mm.trans2:exp8 0.0334325926538953 0.278773186715227 0.119927576420925 0.90457532836258 df.mm.trans1:probe2 0.0059754312459822 0.160143003526067 0.0373130958856381 0.970246124903138 df.mm.trans1:probe3 -0.073533283685985 0.160143003526067 -0.459172627382475 0.646254403555844 df.mm.trans1:probe4 0.127091731250928 0.160143003526067 0.793614010306988 0.427692210783033 df.mm.trans1:probe5 -0.0732951987817883 0.160143003526067 -0.457685925503813 0.647321768447109 df.mm.trans1:probe6 -0.155005002011599 0.160143003526067 -0.967916166168126 0.333424398090227 df.mm.trans1:probe7 -0.111774510147206 0.160143003526067 -0.697966865152569 0.485432372609134 df.mm.trans1:probe8 -0.276954457193279 0.160143003526067 -1.72941965053252 0.0841799569603786 . df.mm.trans1:probe9 -0.193701936314162 0.160143003526067 -1.20955603460149 0.226862352307320 df.mm.trans1:probe10 0.0243850303777655 0.160143003526067 0.152270344884573 0.879018097792972 df.mm.trans1:probe11 0.00270786241119194 0.160143003526067 0.0169090272542014 0.98651406498301 df.mm.trans1:probe12 -0.0353516488491182 0.160143003526067 -0.220750504678551 0.825351818644173 df.mm.trans1:probe13 0.0149718370178535 0.160143003526067 0.0934904222363767 0.925541019297048 df.mm.trans1:probe14 -0.0933141365151353 0.160143003526067 -0.582692558903745 0.560290099284139 df.mm.trans1:probe15 -0.109254359158590 0.160143003526067 -0.682229986655684 0.495321826140446 df.mm.trans1:probe16 0.0449834187892064 0.160143003526067 0.280895311057934 0.778874659340706 df.mm.trans1:probe17 0.0491804686995178 0.160143003526067 0.307103448896615 0.758857024943401 df.mm.trans1:probe18 0.0236186515734026 0.160143003526067 0.147484754584100 0.882792372130545 df.mm.trans1:probe19 -0.195159790722084 0.160143003526067 -1.21865948823869 0.223388743715293 df.mm.trans1:probe20 0.0925176160084104 0.160143003526067 0.577718751186974 0.563641943913067 df.mm.trans1:probe21 -0.0392599223853526 0.160143003526067 -0.245155401865322 0.806408789433179 df.mm.trans1:probe22 -0.0195793162242064 0.160143003526067 -0.122261452533700 0.902727455549498 df.mm.trans2:probe2 0.0327860417948331 0.160143003526067 0.204729779465492 0.837843453631021 df.mm.trans2:probe3 0.000311209023755620 0.160143003526067 0.00194331951383042 0.998450016400146 df.mm.trans2:probe4 -0.0703152636132516 0.160143003526067 -0.439077961977941 0.660742110982213 df.mm.trans2:probe5 -0.134243744683400 0.160143003526067 -0.83827417825062 0.402166183464716 df.mm.trans2:probe6 -0.0649169439661995 0.160143003526067 -0.405368592675564 0.68533188624312 df.mm.trans3:probe2 0.118430219120716 0.160143003526067 0.739527900146062 0.45983729368291 df.mm.trans3:probe3 -0.0952676360221211 0.160143003526067 -0.594891028171669 0.552110780290288