fitVsDatCorrelation=0.881853514193433 cont.fitVsDatCorrelation=0.201331583525225 fstatistic=8152.35910611965,59,853 cont.fstatistic=1878.01683289435,59,853 residuals=-0.940853766282,-0.0864217931837961,-0.0107981964685081,0.0696937738423144,1.65122885165936 cont.residuals=-0.559484972425795,-0.203044765834521,-0.0901275158261781,0.0769355170383065,2.49921023032723 predictedValues: Include Exclude Both Lung 45.5242852336186 41.9377483409125 59.5757873340371 cerebhem 49.8351189591702 43.9626718729313 67.3752170084549 cortex 46.5681979187093 43.5246174164233 72.8839193754485 heart 47.5435260152325 44.0148609280634 61.6114927090564 kidney 46.2206776698729 49.2907733753566 101.509571940685 liver 48.6687926289619 48.7480487002263 65.272440912185 stomach 51.7738015072993 44.469174585801 62.0363254789845 testicle 48.0903813935957 45.5381067939293 62.1019346795003 diffExp=3.58653689270609,5.87244708623897,3.04358050228596,3.52866508716913,-3.0700957054837,-0.079256071264453,7.30462692149829,2.55227459966638 diffExpScore=1.22320876249250 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 51.4776346455475 51.0430873436202 57.7138123257076 cerebhem 55.1967594040625 49.2304842366031 53.9450744239089 cortex 53.663090456201 50.6234776794486 49.1605433626904 heart 54.1525433482532 51.5753723395767 48.9962941316751 kidney 53.9918860193594 49.5159492814494 55.5742574380919 liver 54.8085446610784 51.1961351865695 52.4276771193447 stomach 55.0798654846446 48.7757921023627 49.7069707175191 testicle 51.7560360018827 53.2949097106529 51.1308239321858 cont.diffExp=0.434547301927324,5.96627516745939,3.03961277675241,2.57717100867652,4.47593673791001,3.61240947450889,6.30407338228194,-1.53887370877015 cont.diffExpScore=1.08031136016814 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=0.0301068535429038 cont.tran.correlation=-0.681378998502151 tran.covariance=0.000124806867030885 cont.tran.covariance=-0.000527332489534953 tran.mean=46.6069239587565 cont.tran.mean=52.211347993832 weightedLogRatios: wLogRatio Lung 0.309956801588377 cerebhem 0.482211556470161 cortex 0.257327827845351 heart 0.294829942713965 kidney -0.248594209266772 liver -0.00632287794462297 stomach 0.588708412928155 testicle 0.209722716256734 cont.weightedLogRatios: wLogRatio Lung 0.0333743910606730 cerebhem 0.4522695355145 cortex 0.230532622261261 heart 0.193454252800959 kidney 0.341444995663905 liver 0.270666085534303 stomach 0.479880931720781 testicle -0.116061947664762 varWeightedLogRatios=0.0698073170384666 cont.varWeightedLogRatios=0.0407735495699711 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.2644131602259 0.0805786923879104 40.5121138540054 6.07018108995226e-201 *** df.mm.trans1 0.497652479272252 0.0695857877250468 7.15163966007855 1.84493742455685e-12 *** df.mm.trans2 0.468031666912874 0.0614787289329283 7.61290408953451 7.10256090528261e-14 *** df.mm.exp2 0.0146005700485830 0.0790813026256615 0.184627333690949 0.853565230403061 df.mm.exp3 -0.141806496906640 0.0790813026256615 -1.79317350876089 0.0732993531322046 . df.mm.exp4 0.0581415081648632 0.0790813026256615 0.735211816629796 0.462412590969398 df.mm.exp5 -0.356171944022767 0.0790813026256615 -4.50387047503174 7.60102494462738e-06 *** df.mm.exp6 0.125950254495152 0.0790813026256615 1.59266792924934 0.111605178824868 df.mm.exp7 0.146777371195163 0.0790813026256615 1.85603127821436 0.0637937871594445 . df.mm.exp8 0.0956715081408262 0.0790813026256615 1.20978670007114 0.226695914670188 df.mm.trans1:exp2 0.075873440462854 0.0730964925112782 1.03799016691734 0.299568776619479 df.mm.trans2:exp2 0.0325540008735733 0.0539853374914371 0.603015603611571 0.546658599380395 df.mm.trans1:exp3 0.164478432002330 0.0730964925112781 2.25015491648868 0.0246932213545628 * df.mm.trans2:exp3 0.178946856143941 0.0539853374914371 3.31473071132185 0.000956007569161853 *** df.mm.trans1:exp4 -0.0147418045079160 0.0730964925112781 -0.201675949165981 0.840218150165567 df.mm.trans2:exp4 -0.00980051931094118 0.0539853374914371 -0.181540391638668 0.855986560424896 df.mm.trans1:exp5 0.371353285627634 0.0730964925112782 5.08031607084754 4.63032823852987e-07 *** df.mm.trans2:exp5 0.517722518521214 0.0539853374914371 9.59005801535152 9.37056783490179e-21 *** df.mm.trans1:exp6 -0.0591581632181383 0.0730964925112781 -0.809316031258418 0.418559074658373 df.mm.trans2:exp6 0.0245285790803159 0.0539853374914371 0.454356316364726 0.649687980822838 df.mm.trans1:exp7 -0.0181390372438614 0.0730964925112781 -0.248151951217943 0.804076567001347 df.mm.trans2:exp7 -0.0881674643969132 0.0539853374914371 -1.63317427460554 0.102801380641390 df.mm.trans1:exp8 -0.0408352470189378 0.0730964925112781 -0.558648515353009 0.576548238263435 df.mm.trans2:exp8 -0.0133083571521493 0.0539853374914371 -0.246517994895562 0.805340587699056 df.mm.trans1:probe2 -0.000249945013593362 0.0500457472786681 -0.00499433073107293 0.996016284934556 df.mm.trans1:probe3 0.0863650250069178 0.0500457472786681 1.72572155883724 0.0847596649869843 . df.mm.trans1:probe4 0.0867407994634866 0.0500457472786681 1.73323017799076 0.0834161434236223 . df.mm.trans1:probe5 -0.104737190514202 0.0500457472786681 -2.09282898566780 0.0366593513467565 * df.mm.trans1:probe6 0.215962101100072 0.0500457472786681 4.31529376307517 1.78061809227506e-05 *** df.mm.trans1:probe7 0.0240005423757046 0.0500457472786681 0.47957206517595 0.631654646443451 df.mm.trans1:probe8 1.04645705731411 0.0500457472786681 20.9100096255364 1.04899961160956e-78 *** df.mm.trans1:probe9 0.339561301847363 0.0500457472786681 6.78501811465807 2.17002508463047e-11 *** df.mm.trans1:probe10 0.247145072364516 0.0500457472786681 4.93838309553749 9.47749523470152e-07 *** df.mm.trans1:probe11 -0.0674044503536195 0.0500457472786681 -1.34685670649083 0.178384104822488 df.mm.trans1:probe12 -0.0511587371321814 0.0500457472786681 -1.02223944918468 0.306957404327753 df.mm.trans1:probe13 0.00275308144028395 0.0500457472786681 0.0550112964634948 0.956142345875954 df.mm.trans1:probe14 -0.0499692789941691 0.0500457472786681 -0.998472032317287 0.318333673221238 df.mm.trans1:probe15 0.0259327189903092 0.0500457472786681 0.518180273059145 0.604466872416937 df.mm.trans1:probe16 -0.0785963901767277 0.0500457472786681 -1.57049088984688 0.116671795298334 df.mm.trans1:probe17 -0.0435099424268411 0.0500457472786681 -0.869403391752071 0.384871093848801 df.mm.trans1:probe18 -0.015732620811949 0.0500457472786681 -0.314364789566345 0.753320866868013 df.mm.trans1:probe19 0.0762111464453605 0.0500457472786681 1.52282962268495 0.128171957212869 df.mm.trans1:probe20 -0.0124352019556465 0.0500457472786681 -0.24847669645942 0.80382540666305 df.mm.trans1:probe21 0.0333047854371316 0.0500457472786681 0.665486824518409 0.505919070244598 df.mm.trans1:probe22 0.0371292605596252 0.0500457472786681 0.74190640720938 0.458348267531252 df.mm.trans2:probe2 0.0976387464487726 0.0500457472786681 1.9509898794216 0.0513853237139042 . df.mm.trans2:probe3 -0.0081472458404464 0.0500457472786681 -0.162795967359232 0.870717641521638 df.mm.trans2:probe4 -0.00921509801432657 0.0500457472786681 -0.184133488166626 0.853952499836915 df.mm.trans2:probe5 0.0217736400581815 0.0500457472786681 0.435074731463995 0.663618237656097 df.mm.trans2:probe6 -0.0421858943881487 0.0500457472786681 -0.842946637468442 0.399494587502046 df.mm.trans3:probe2 -0.45945485643567 0.0500457472786681 -9.1806972903672 3.16852580723062e-19 *** df.mm.trans3:probe3 0.861418200071929 0.0500457472786681 17.2126153951768 3.22356387832083e-57 *** df.mm.trans3:probe4 -0.302797465474675 0.0500457472786681 -6.05041351043512 2.16101729622467e-09 *** df.mm.trans3:probe5 -0.397250170773026 0.0500457472786681 -7.93774081463966 6.46764366653548e-15 *** df.mm.trans3:probe6 -0.326765719535977 0.0500457472786681 -6.52934039962393 1.13326692521860e-10 *** df.mm.trans3:probe7 -0.305762866803755 0.0500457472786681 -6.10966732300321 1.51649837282739e-09 *** df.mm.trans3:probe8 -0.113826812418991 0.0500457472786681 -2.27445524562102 0.0231861785971776 * df.mm.trans3:probe9 -0.267994083339083 0.0500457472786681 -5.35498214956848 1.10091655521974e-07 *** df.mm.trans3:probe10 -0.116046901964274 0.0500457472786681 -2.31881644844055 0.0206404971344504 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.91967822792348 0.167390808443265 23.4163289154075 4.90125501566665e-94 *** df.mm.trans1 0.0322382518778078 0.144554607654624 0.223017808984912 0.823575039023542 df.mm.trans2 -0.00349528119695716 0.127713342487688 -0.0273681757040704 0.978172482254843 df.mm.exp2 0.101129852479232 0.164280193522253 0.615593701900123 0.538326918438058 df.mm.exp3 0.193728519563299 0.164280193522253 1.17925670410814 0.238624781181228 df.mm.exp4 0.224783520009296 0.164280193522253 1.36829349412014 0.171580517031579 df.mm.exp5 0.0550874694975629 0.164280193522253 0.335326300246298 0.737461406239814 df.mm.exp6 0.161754475709941 0.164280193522253 0.984625548837268 0.325087230432877 df.mm.exp7 0.171552144889649 0.164280193522253 1.04426553932937 0.296658395537859 df.mm.exp8 0.169673329703307 0.164280193522253 1.03282888865311 0.301976717625450 df.mm.trans1:exp2 -0.031373041983759 0.151847599076524 -0.206608745706598 0.836364735423794 df.mm.trans2:exp2 -0.137286948462266 0.112146884231768 -1.22417086665148 0.221225776610977 df.mm.trans1:exp3 -0.152150517545556 0.151847599076524 -1.00199488481131 0.316630215498081 df.mm.trans2:exp3 -0.201983191032594 0.112146884231767 -1.8010593198039 0.0720466139762258 . df.mm.trans1:exp4 -0.174126013707960 0.151847599076524 -1.14671561991711 0.251820726931949 df.mm.trans2:exp4 -0.214409367611468 0.112146884231767 -1.91186201097090 0.0562286688884693 . df.mm.trans1:exp5 -0.00740112774017936 0.151847599076524 -0.048740498929124 0.961137510371441 df.mm.trans2:exp5 -0.0854627700290834 0.112146884231768 -0.762061029287825 0.446234235601316 df.mm.trans1:exp6 -0.099055804054836 0.151847599076524 -0.652336979031962 0.514359555945573 df.mm.trans2:exp6 -0.15876055706536 0.112146884231767 -1.41564839855255 0.157243478501854 df.mm.trans1:exp7 -0.103915347829789 0.151847599076524 -0.684339748944075 0.493946533238536 df.mm.trans2:exp7 -0.216988144501372 0.112146884231768 -1.93485664793803 0.0533382085701187 . df.mm.trans1:exp8 -0.164279701258078 0.151847599076524 -1.08187223411606 0.279615052006666 df.mm.trans2:exp8 -0.126502631621024 0.112146884231767 -1.12800843721693 0.259633527597748 df.mm.trans1:probe2 0.0437962109075709 0.103962944146516 0.421267512834655 0.673665877482739 df.mm.trans1:probe3 -0.00385464189756927 0.103962944146516 -0.0370770751945702 0.970432225828413 df.mm.trans1:probe4 0.0116397548260999 0.103962944146516 0.111960611751202 0.910880977686808 df.mm.trans1:probe5 -0.0316604393538003 0.103962944146516 -0.304535809501326 0.76079399677787 df.mm.trans1:probe6 0.0373548636678121 0.103962944146516 0.359309405620214 0.719452564739288 df.mm.trans1:probe7 0.0281100625220725 0.103962944146516 0.270385402730196 0.786929174600864 df.mm.trans1:probe8 -0.00913120485038367 0.103962944146516 -0.0878313414971683 0.93003134188895 df.mm.trans1:probe9 -0.0632634449269661 0.103962944146516 -0.608519174272406 0.543005195883794 df.mm.trans1:probe10 0.000546356418212273 0.103962944146516 0.00525529959446212 0.99580812566089 df.mm.trans1:probe11 -0.113486590476077 0.103962944146516 -1.09160616225084 0.275314474166436 df.mm.trans1:probe12 -0.115294040463602 0.103962944146516 -1.10899168362447 0.267746335432529 df.mm.trans1:probe13 0.0639840794276097 0.103962944146516 0.615450821952834 0.538421201967279 df.mm.trans1:probe14 -0.0291058951051866 0.103962944146516 -0.279964128989724 0.779572901929566 df.mm.trans1:probe15 -0.101171055183818 0.103962944146516 -0.973145345338014 0.330756944273851 df.mm.trans1:probe16 0.0404194395897733 0.103962944146516 0.388786984839616 0.697530754036834 df.mm.trans1:probe17 -0.0757402131009288 0.103962944146516 -0.728530859939744 0.466488640423704 df.mm.trans1:probe18 0.0136812895250206 0.103962944146516 0.131597749922697 0.89533349744895 df.mm.trans1:probe19 -0.00514274791214551 0.103962944146516 -0.0494671246025679 0.960558616021129 df.mm.trans1:probe20 -0.0464155602568132 0.103962944146516 -0.446462541416674 0.65537648196976 df.mm.trans1:probe21 0.0696263752345484 0.103962944146516 0.669723003769722 0.503215628319906 df.mm.trans1:probe22 -0.0595020445160613 0.103962944146516 -0.572338971395467 0.567243161199004 df.mm.trans2:probe2 0.106193594900096 0.103962944146516 1.02145620992069 0.30732794932861 df.mm.trans2:probe3 0.172390478749842 0.103962944146516 1.65819158128968 0.0976464590143363 . df.mm.trans2:probe4 -0.0287374372163541 0.103962944146516 -0.276420001879268 0.782292456103407 df.mm.trans2:probe5 0.0424311922075139 0.103962944146516 0.408137654775487 0.683275041666776 df.mm.trans2:probe6 -0.0284829616340728 0.103962944146516 -0.273972249130725 0.784172277044856 df.mm.trans3:probe2 0.0434832834465196 0.103962944146516 0.418257522461448 0.675864118875499 df.mm.trans3:probe3 0.168676061084978 0.103962944146516 1.62246329660751 0.105073694038039 df.mm.trans3:probe4 0.207762469250130 0.103962944146516 1.99842810297224 0.0459872664645243 * df.mm.trans3:probe5 0.091796238719582 0.103962944146516 0.88297074956066 0.377500844906343 df.mm.trans3:probe6 0.143918155050760 0.103962944146516 1.38432165645420 0.166622022588511 df.mm.trans3:probe7 0.0751328483931343 0.103962944146516 0.722688733085983 0.470069230841108 df.mm.trans3:probe8 0.0533964709547761 0.103962944146516 0.513610608021297 0.607657270030729 df.mm.trans3:probe9 0.193073250931748 0.103962944146516 1.85713527561944 0.0636364148724568 . df.mm.trans3:probe10 0.0935145020736293 0.103962944146516 0.899498401486578 0.368641099900997