fitVsDatCorrelation=0.849644335741086 cont.fitVsDatCorrelation=0.231329019824333 fstatistic=10536.7076828092,53,715 cont.fstatistic=3086.45480628139,53,715 residuals=-0.583575933648848,-0.0900814304034807,-0.00904805416825253,0.0886276836696961,0.905156742235034 cont.residuals=-0.568227775035354,-0.192352665162999,-0.0266516850520416,0.137357283397707,1.44911848923394 predictedValues: Include Exclude Both Lung 75.0724664332056 60.7676901358459 59.5584364007386 cerebhem 74.3408719607874 61.14038365757 58.6625002269759 cortex 68.4335321596489 62.3007629983894 55.3506466417286 heart 77.6533135444876 67.0447569558562 59.300694475369 kidney 76.4460869038463 75.4363248485961 62.8125682391405 liver 76.753170905619 75.93277056843 61.5706033698396 stomach 84.605010919419 64.949826145692 58.7940444724719 testicle 84.7320619563614 61.9890214208344 63.6513640509261 diffExp=14.3047762973597,13.2004883032173,6.13276916125952,10.6085565886314,1.00976205525021,0.82040033718907,19.6551847737269,22.7430405355270 diffExpScore=0.988823691027708 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=0,0,0,0,0,0,1,1 diffExp1.3Score=0.666666666666667 diffExp1.2=1,1,0,0,0,0,1,1 diffExp1.2Score=0.8 cont.predictedValues: Include Exclude Both Lung 70.7058768846805 64.2082070722722 74.1555970254566 cerebhem 70.0713967265273 70.3915673686141 78.9445937926752 cortex 70.1705469181158 79.2150084457992 75.8281127495144 heart 69.0740839282394 69.0262483903058 65.1283743304684 kidney 68.1724091635042 70.6174267080901 77.1254355735376 liver 70.2724208255256 71.791341121965 67.5509348750039 stomach 67.1660078189457 72.865197752852 70.7937967352485 testicle 72.719232060502 76.5326560445403 72.2424906917705 cont.diffExp=6.49766981240826,-0.320170642086808,-9.0444615276834,0.0478355379336364,-2.44501754458591,-1.51892029643946,-5.69918993390635,-3.81342398403831 cont.diffExpScore=1.69907697728524 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=0.0355274301071813 cont.tran.correlation=0.192814567431560 tran.covariance=0.000399938961655232 cont.tran.covariance=0.000264362758086017 tran.mean=71.7248782196618 cont.tran.mean=70.812476701905 weightedLogRatios: wLogRatio Lung 0.890558222235745 cerebhem 0.823184966669983 cortex 0.392355580318182 heart 0.628530312632356 kidney 0.0575744194408754 liver 0.0465878049381693 stomach 1.13836177229832 testicle 1.33866551944520 cont.weightedLogRatios: wLogRatio Lung 0.40586592653798 cerebhem -0.0193830737570516 cortex -0.522719559475064 heart 0.00293374433118810 kidney -0.149392954582215 liver -0.0911634707174347 stomach -0.345964427588656 testicle -0.22040164198563 varWeightedLogRatios=0.225836843706438 cont.varWeightedLogRatios=0.0752487874286617 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.70444497184761 0.0785159964428976 59.917025637814 6.04168934703032e-281 *** df.mm.trans1 -0.202772719962862 0.0690767342317817 -2.93547056353987 0.00343724885012327 ** df.mm.trans2 -0.695894345214787 0.0624569144196232 -11.1419904694515 1.06915757770319e-26 *** df.mm.exp2 0.0114786540078093 0.0832227593254506 0.137926861604299 0.890337053649364 df.mm.exp3 0.00559412509934557 0.0832227593254506 0.0672186928754573 0.946426395650106 df.mm.exp4 0.13643946830962 0.0832227593254506 1.63944898505540 0.101559663902429 df.mm.exp5 0.181165335627048 0.0832227593254506 2.17687249371994 0.0298165261621387 * df.mm.exp6 0.211704366072867 0.0832227593254506 2.543827767654 0.0111738785605611 * df.mm.exp7 0.199013849988664 0.0832227593254506 2.3913392394309 0.0170445812976171 * df.mm.exp8 0.0744764590918145 0.0832227593254506 0.894904947822832 0.371139053973027 df.mm.trans1:exp2 -0.0212716260017709 0.0782938856826559 -0.271689491667203 0.785939209203426 df.mm.trans2:exp2 -0.00536429798145736 0.0643404592706336 -0.0833736352252888 0.933577787492854 df.mm.trans1:exp3 -0.0981850505016952 0.0782938856826559 -1.25405770381180 0.210230870825474 df.mm.trans2:exp3 0.0193213123010067 0.0643404592706336 0.30029801652077 0.764037135274812 df.mm.trans1:exp4 -0.102639112961692 0.0782938856826559 -1.31094672421437 0.190296642730044 df.mm.trans2:exp4 -0.0381372931575749 0.0643404592706336 -0.592742010080454 0.553541239173373 df.mm.trans1:exp5 -0.163033455756455 0.0782938856826559 -2.08232679135724 0.037667601819489 * df.mm.trans2:exp5 0.0350653498113508 0.0643404592706336 0.544996883902496 0.585925610185916 df.mm.trans1:exp6 -0.189563531884296 0.0782938856826559 -2.42117925597209 0.0157181797318874 * df.mm.trans2:exp6 0.0110857494589211 0.0643404592706336 0.172298264336152 0.863251805695593 df.mm.trans1:exp7 -0.0794742205610633 0.0782938856826559 -1.01507569675609 0.310413107530766 df.mm.trans2:exp7 -0.132457019099547 0.0643404592706336 -2.05868936282218 0.0398857574214943 * df.mm.trans1:exp8 0.046563740277443 0.0782938856826559 0.594730225373884 0.552211966434604 df.mm.trans2:exp8 -0.0545773990668756 0.0643404592706336 -0.848259395185666 0.396577405737022 df.mm.trans1:probe2 -0.0643913197309828 0.0457137805036879 -1.40857568596384 0.159395365017658 df.mm.trans1:probe3 -0.61398615487952 0.0457137805036879 -13.4310955714981 7.75257702269287e-37 *** df.mm.trans1:probe4 -0.376400339277776 0.0457137805036879 -8.2338484179275 8.60344099187902e-16 *** df.mm.trans1:probe5 -0.0450505051832223 0.0457137805036879 -0.98549069201546 0.324716461218776 df.mm.trans1:probe6 -0.365775201029131 0.0457137805036879 -8.00142095007046 4.96546864234706e-15 *** df.mm.trans1:probe7 -0.335043172207063 0.0457137805036879 -7.32915039000185 6.27694697792584e-13 *** df.mm.trans1:probe8 -0.200816119208205 0.0457137805036879 -4.39290115574678 1.28782291184869e-05 *** df.mm.trans1:probe9 -0.3662586528142 0.0457137805036879 -8.01199657474516 4.58883051717965e-15 *** df.mm.trans1:probe10 -0.232862446815100 0.0457137805036879 -5.09392231947026 4.49192076045303e-07 *** df.mm.trans1:probe11 0.530589722800573 0.0457137805036879 11.6067784583637 1.17541822501416e-28 *** df.mm.trans1:probe12 -0.163245828538730 0.0457137805036879 -3.57104196459009 0.00037937573296664 *** df.mm.trans1:probe13 0.160513966538508 0.0457137805036879 3.51128182289711 0.000473947440311676 *** df.mm.trans1:probe14 0.0967983622727054 0.0457137805036879 2.11748757609090 0.0345630900155732 * df.mm.trans1:probe15 -0.190899720811313 0.0457137805036879 -4.1759775434874 3.33351646601198e-05 *** df.mm.trans1:probe16 -0.439826436643812 0.0457137805036879 -9.62130963131193 1.09145444777506e-20 *** df.mm.trans1:probe17 -0.474916944082679 0.0457137805036879 -10.3889229648019 1.21352752222989e-23 *** df.mm.trans1:probe18 -0.386260662777960 0.0457137805036879 -8.44954537826507 1.63167743447067e-16 *** df.mm.trans1:probe19 -0.506350571017527 0.0457137805036879 -11.0765411532017 1.99744287577319e-26 *** df.mm.trans1:probe20 -0.357182615479715 0.0457137805036879 -7.81345606388645 1.98944906304814e-14 *** df.mm.trans1:probe21 -0.432313390049365 0.0457137805036879 -9.45695992075057 4.45125039665187e-20 *** df.mm.trans2:probe2 0.214693776339745 0.0457137805036879 4.69647826047607 3.17400966652180e-06 *** df.mm.trans2:probe3 0.266845285570493 0.0457137805036879 5.83730513272612 8.0460955211899e-09 *** df.mm.trans2:probe4 0.186775448927106 0.0457137805036879 4.08575809896182 4.89070170289226e-05 *** df.mm.trans2:probe5 0.1491357674191 0.0457137805036879 3.26238096643678 0.00115729076429327 ** df.mm.trans2:probe6 0.266133419097877 0.0457137805036879 5.8217328815412 8.79566623176628e-09 *** df.mm.trans3:probe2 0.214816560986452 0.0457137805036879 4.69916420430645 3.13379134931334e-06 *** df.mm.trans3:probe3 0.396704701874941 0.0457137805036879 8.67801125839804 2.70252177970301e-17 *** df.mm.trans3:probe4 0.323182124216959 0.0457137805036879 7.06968709776446 3.69997088225649e-12 *** df.mm.trans3:probe5 0.471196854397529 0.0457137805036879 10.3075451035933 2.54123869647129e-23 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.94805279667666 0.144847644317395 27.2565896068392 9.99736637427133e-113 *** df.mm.trans1 0.296675037274813 0.127433933006112 2.32806937898231 0.0201864745584755 * df.mm.trans2 0.186089280054492 0.115221576938083 1.61505583415588 0.106739763451240 df.mm.exp2 0.0203477037331990 0.153530760456591 0.132531771956878 0.894600981922517 df.mm.exp3 0.180131156343455 0.153530760456591 1.17325776155707 0.241083149893271 df.mm.exp4 0.178811956546538 0.153530760456591 1.16466534793915 0.244542918261067 df.mm.exp5 0.0193895626647114 0.153530760456591 0.126291061198733 0.899537035832436 df.mm.exp6 0.198767198454731 0.153530760456591 1.29464087759098 0.195862075772899 df.mm.exp7 0.121512847306424 0.153530760456591 0.791456037507094 0.428940391098777 df.mm.exp8 0.229800845172642 0.153530760456591 1.49677396561593 0.13489336424998 df.mm.trans1:exp2 -0.0293617219474253 0.144437890612977 -0.203282683116027 0.838971927190434 df.mm.trans2:exp2 0.0715947320182857 0.118696372482879 0.603175400567632 0.546583289961989 df.mm.trans1:exp3 -0.187731186623236 0.144437890612977 -1.29973641837698 0.194110195782971 df.mm.trans2:exp3 0.0299035865599417 0.118696372482879 0.251933449476352 0.801164901760414 df.mm.trans1:exp4 -0.202161041512926 0.144437890612977 -1.39963994665790 0.162054874001327 df.mm.trans2:exp4 -0.106456151375953 0.118696372482879 -0.896877883873903 0.370085957145081 df.mm.trans1:exp5 -0.0558783310915316 0.144437890612977 -0.386867537696587 0.698969387199071 df.mm.trans2:exp5 0.0757563500579709 0.118696372482879 0.638236438682217 0.523524139883762 df.mm.trans1:exp6 -0.204916477202925 0.144437890612977 -1.41871690546908 0.156417305718622 df.mm.trans2:exp6 -0.087134365402385 0.118696372482879 -0.734094594297337 0.463131743542197 df.mm.trans1:exp7 -0.172874257483922 0.144437890612977 -1.19687608805601 0.231751543266592 df.mm.trans2:exp7 0.00496724209541269 0.118696372482879 0.0418483058202066 0.966631309268601 df.mm.trans1:exp8 -0.201723649284102 0.144437890612977 -1.39661170921294 0.162963729384068 df.mm.trans2:exp8 -0.0542143575690311 0.118696372482879 -0.456748226040783 0.647990746185246 df.mm.trans1:probe2 0.0781619490206022 0.0843335590043403 0.92681904977566 0.35433322638774 df.mm.trans1:probe3 0.0320161672739154 0.0843335590043403 0.379637331234505 0.704327374247133 df.mm.trans1:probe4 0.0249148013358048 0.0843335590043403 0.295431636349209 0.767749939514471 df.mm.trans1:probe5 -0.00203522031043570 0.0843335590043403 -0.0241329825808841 0.98075326809701 df.mm.trans1:probe6 0.0923782086951528 0.0843335590043403 1.09539084779285 0.27371427650429 df.mm.trans1:probe7 0.0904571524176523 0.0843335590043403 1.07261158530019 0.283807308859973 df.mm.trans1:probe8 0.108762190807276 0.0843335590043403 1.2896667956546 0.197583370463797 df.mm.trans1:probe9 -0.0029091161457453 0.0843335590043403 -0.0344953560609909 0.972491773041017 df.mm.trans1:probe10 0.0300218283407185 0.0843335590043403 0.355989106770336 0.72195373219699 df.mm.trans1:probe11 0.00272641374197943 0.0843335590043403 0.0323289301930103 0.974218760573102 df.mm.trans1:probe12 0.00690930035571349 0.0843335590043403 0.0819282434808413 0.934726703561573 df.mm.trans1:probe13 0.0282005872711666 0.0843335590043403 0.334393420651383 0.738180765709426 df.mm.trans1:probe14 -0.01476257266979 0.0843335590043403 -0.175049800388837 0.861090060401013 df.mm.trans1:probe15 0.0103112305902280 0.0843335590043403 0.122267229225999 0.902721740750567 df.mm.trans1:probe16 0.0236684948704092 0.0843335590043403 0.280653338360723 0.779057488449489 df.mm.trans1:probe17 -0.0421738447409626 0.0843335590043403 -0.500083777310906 0.617169959533773 df.mm.trans1:probe18 -0.006931738839912 0.0843335590043403 -0.0821943117514494 0.934515200345249 df.mm.trans1:probe19 -0.00976216473647844 0.0843335590043403 -0.115756584350674 0.907877962089803 df.mm.trans1:probe20 -0.0823853038571709 0.0843335590043403 -0.976898222129234 0.328949943259625 df.mm.trans1:probe21 -0.00874600993762658 0.0843335590043403 -0.103707350204163 0.917430675037025 df.mm.trans2:probe2 0.0971382260859216 0.0843335590043403 1.15183359071710 0.249774453630521 df.mm.trans2:probe3 0.0970584057915144 0.0843335590043403 1.1508871075454 0.250163416698588 df.mm.trans2:probe4 0.0832986235133748 0.0843335590043403 0.987728070495493 0.323619969676684 df.mm.trans2:probe5 0.0472556667978549 0.0843335590043403 0.560342375630357 0.575421452806002 df.mm.trans2:probe6 -0.0168723422661908 0.0843335590043403 -0.200066764232285 0.841485254511547 df.mm.trans3:probe2 -0.100337379556336 0.0843335590043403 -1.18976811533795 0.234532462166500 df.mm.trans3:probe3 -0.164392426098938 0.0843335590043403 -1.94931208927726 0.0516488067790948 . df.mm.trans3:probe4 -0.152786765240534 0.0843335590043403 -1.81169592561214 0.0704526303218827 . df.mm.trans3:probe5 -0.205741258086991 0.0843335590043403 -2.43961313285026 0.0149450249464058 *