fitVsDatCorrelation=0.9292698437228 cont.fitVsDatCorrelation=0.225512669539334 fstatistic=11270.9281951037,69,1083 cont.fstatistic=1606.97178810802,69,1083 residuals=-1.41879950744999,-0.094874206381654,-0.00316821187970081,0.0808501452891805,0.89092345763921 cont.residuals=-0.753556893805883,-0.298104170807645,-0.106924544341203,0.1889799348744,1.94697655256176 predictedValues: Include Exclude Both Lung 59.7273288543794 74.5339548294196 64.2495308062875 cerebhem 55.3694572333071 78.060214938006 67.918625313548 cortex 54.6670391032138 69.0646837015236 61.849937884681 heart 58.4534516855484 83.1478342171778 65.8331925507551 kidney 60.7356488942052 78.9209205640862 64.9174612294802 liver 62.3866487952391 88.4486401055332 65.4266641758172 stomach 61.3384075339795 78.9142438659256 69.5018625989431 testicle 59.203068220032 85.2313049853652 62.509712341559 diffExp=-14.8066259750402,-22.6907577046990,-14.3976445983098,-24.6943825316295,-18.1852716698810,-26.0619913102941,-17.5758363319462,-26.0282367653332 diffExpScore=0.99395553986055 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,-1,0,-1,0,-1,0,-1 diffExp1.4Score=0.8 diffExp1.3=0,-1,0,-1,0,-1,0,-1 diffExp1.3Score=0.8 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 69.7853489491962 66.623313283839 70.5692611540111 cerebhem 74.7468768914432 65.7345756211443 71.2834187571697 cortex 74.544883093388 62.0342937829891 67.4620327502651 heart 72.6390490364231 63.934290347907 70.3580768821679 kidney 76.4597186121263 69.8572709784262 70.3198276106558 liver 80.819302867537 62.7199088265098 71.3245219523099 stomach 72.7252073831273 66.8701045286148 67.148087606417 testicle 67.964962852094 61.3323883547312 70.1650645220755 cont.diffExp=3.16203566535724,9.01230127029883,12.5105893103990,8.70475868851615,6.60244763370018,18.0993940410273,5.85510285451251,6.63257449736285 cont.diffExpScore=0.986029461845616 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=0,0,1,0,0,1,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.628159116566098 cont.tran.correlation=0.117116456484472 tran.covariance=0.00233692179834585 cont.tran.covariance=0.000311480314265225 tran.mean=69.2626779704339 cont.tran.mean=69.2994684630935 weightedLogRatios: wLogRatio Lung -0.930269101351112 cerebhem -1.43760731320003 cortex -0.962752164588173 heart -1.49569061711818 kidney -1.10986404072233 liver -1.50375725130893 stomach -1.06889216570294 testicle -1.55348004757521 cont.weightedLogRatios: wLogRatio Lung 0.195783218047337 cerebhem 0.546032922829525 cortex 0.775189561611368 heart 0.538883846519796 kidney 0.387574676376656 liver 1.08144835436935 stomach 0.356285061159185 testicle 0.427952070166488 varWeightedLogRatios=0.0698207439004481 cont.varWeightedLogRatios=0.0766468522561805 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.22275092668731 0.0738242125305915 57.2000808669306 0 *** df.mm.trans1 -0.396728033907292 0.0629611991953191 -6.3011511689375 4.29034745133992e-10 *** df.mm.trans2 0.114984693213401 0.0548422291423672 2.09664514027881 0.0362565113826980 * df.mm.exp2 -0.0850717138803754 0.0687572445383886 -1.23727636922504 0.216252696700560 df.mm.exp3 -0.126676687494749 0.0687572445383886 -1.84237585937614 0.0656934114021713 . df.mm.exp4 0.0634566858585276 0.0687572445383886 0.922909088119411 0.356260146339383 df.mm.exp5 0.0635904766219005 0.0687572445383886 0.924854930543307 0.355247397776688 df.mm.exp6 0.196573396784185 0.0687572445383886 2.85894814581225 0.00433198508716174 ** df.mm.exp7 0.00514433167009323 0.0687572445383886 0.074818758439644 0.940372733787474 df.mm.exp8 0.152750166846161 0.0687572445383886 2.22158650876240 0.0265173755575341 * df.mm.trans1:exp2 0.00931015671720197 0.0625120053552344 0.148933899405331 0.881633518824566 df.mm.trans2:exp2 0.131297438049897 0.0414921259066793 3.16439409118734 0.00159736288393081 ** df.mm.trans1:exp3 0.0381479540822522 0.0625120053552343 0.61025004501888 0.54182421129768 df.mm.trans2:exp3 0.0504654068379839 0.0414921259066793 1.21626467034942 0.224149133089663 df.mm.trans1:exp4 -0.085015631448125 0.0625120053552343 -1.35998886877825 0.174116478904672 df.mm.trans2:exp4 0.0459086816680465 0.0414921259066793 1.10644322663294 0.26878043089501 df.mm.trans1:exp5 -0.0468493399116616 0.0625120053552344 -0.749445480838966 0.453751502626612 df.mm.trans2:exp5 -0.00639892208993213 0.0414921259066793 -0.154220155031923 0.877464884176047 df.mm.trans1:exp6 -0.153011791325657 0.0625120053552344 -2.44771848953083 0.0145343115312286 * df.mm.trans2:exp6 -0.0254061420985841 0.0414921259066793 -0.61231237357482 0.540459639348719 df.mm.trans1:exp7 0.0214721799719469 0.0625120053552344 0.343488900250886 0.731297358953022 df.mm.trans2:exp7 0.0519626194315974 0.0414921259066793 1.25234892876946 0.210713117936771 df.mm.trans1:exp8 -0.161566483667192 0.0625120053552344 -2.58456728030184 0.00988003779304677 ** df.mm.trans2:exp8 -0.0186361621673929 0.0414921259066793 -0.449149368950335 0.653413772720357 df.mm.trans1:probe2 0.0616661804653999 0.0474812765539431 1.29874731559379 0.194307115885859 df.mm.trans1:probe3 0.0626230046407974 0.0474812765539431 1.31889892576186 0.187481711838977 df.mm.trans1:probe4 0.0793592235243036 0.0474812765539431 1.67137931588980 0.094935672158855 . df.mm.trans1:probe5 0.246574199755187 0.0474812765539431 5.19308278232697 2.46957487184089e-07 *** df.mm.trans1:probe6 0.237729938443005 0.0474812765539431 5.00681438446419 6.45599587920057e-07 *** df.mm.trans1:probe7 0.215665384246583 0.0474812765539431 4.54211427954275 6.19386098277009e-06 *** df.mm.trans1:probe8 0.0809996656108 0.0474812765539431 1.70592855730778 0.0883081018869473 . df.mm.trans1:probe9 0.393176415002757 0.0474812765539431 8.28066226391516 3.57862555055217e-16 *** df.mm.trans1:probe10 0.244862406692032 0.0474812765539431 5.15703082274644 2.98163073078263e-07 *** df.mm.trans1:probe11 0.276417487254004 0.0474812765539431 5.82161027073415 7.67288027966581e-09 *** df.mm.trans1:probe12 0.426676558243532 0.0474812765539431 8.98620654730687 1.11318362665442e-18 *** df.mm.trans1:probe13 0.560323568249617 0.0474812765539431 11.8009373150075 2.54064728114133e-30 *** df.mm.trans1:probe14 0.272984982367159 0.0474812765539431 5.74931851415206 1.16448214467667e-08 *** df.mm.trans1:probe15 0.413063720824553 0.0474812765539431 8.69950748597238 1.21966403236222e-17 *** df.mm.trans1:probe16 0.671226120615005 0.0474812765539431 14.1366485766748 9.14411480543604e-42 *** df.mm.trans1:probe17 1.05590643134234 0.0474812765539431 22.2383749548673 1.51245199829732e-90 *** df.mm.trans1:probe18 1.20712898766890 0.0474812765539431 25.4232631318893 3.43821077295770e-112 *** df.mm.trans1:probe19 1.13504848302744 0.0474812765539431 23.9051804291303 9.20191232529076e-102 *** df.mm.trans1:probe20 1.24091383151944 0.0474812765539431 26.1348034758427 3.91107435287646e-117 *** df.mm.trans1:probe21 1.16658223778823 0.0474812765539431 24.5693107358411 2.65505841297825e-106 *** df.mm.trans1:probe22 1.02927646313823 0.0474812765539431 21.6775229698990 7.7269777520525e-87 *** df.mm.trans2:probe2 -0.153208276761620 0.0474812765539431 -3.22670930271981 0.00128974509460112 ** df.mm.trans2:probe3 -0.0102815319113625 0.0474812765539431 -0.216538658131520 0.828608675666916 df.mm.trans2:probe4 -0.231558012565151 0.0474812765539431 -4.87682786502337 1.23929417640886e-06 *** df.mm.trans2:probe5 -0.33883613325078 0.0474812765539431 -7.13620521271856 1.75398253650522e-12 *** df.mm.trans2:probe6 0.0453824050606665 0.0474812765539431 0.955795807408587 0.339388703815517 df.mm.trans3:probe2 0.0715528422200512 0.0474812765539431 1.50696963968019 0.132110061601357 df.mm.trans3:probe3 0.152425177931287 0.0474812765539431 3.21021651046213 0.00136533793693562 ** df.mm.trans3:probe4 0.209723663698639 0.0474812765539431 4.41697609920773 1.10130958724717e-05 *** df.mm.trans3:probe5 0.819034866783658 0.0474812765539431 17.2496387255545 4.25986563593718e-59 *** df.mm.trans3:probe6 0.133009805027415 0.0474812765539431 2.80131063612629 0.00518017246229558 ** df.mm.trans3:probe7 -0.0580301775609982 0.0474812765539431 -1.22216970083082 0.221909388911592 df.mm.trans3:probe8 0.686948203249036 0.0474812765539431 14.4677703108635 1.67047280457667e-43 *** df.mm.trans3:probe9 1.66631287602028 0.0474812765539431 35.0941043913845 8.11242332603014e-181 *** df.mm.trans3:probe10 0.466186172356528 0.0474812765539431 9.81831589609638 7.42810279915711e-22 *** df.mm.trans3:probe11 -0.109956688262539 0.0474812765539431 -2.31579048085655 0.0207557808509568 * df.mm.trans3:probe12 -0.0249090758740592 0.0474812765539431 -0.524608386334352 0.599962816107328 df.mm.trans3:probe13 -0.143036034951355 0.0474812765539431 -3.01247239612131 0.00265147433650057 ** df.mm.trans3:probe14 0.0194214738220288 0.0474812765539431 0.409034365366404 0.682595315842101 df.mm.trans3:probe15 0.0512849539705173 0.0474812765539431 1.08010899648523 0.280334120338081 df.mm.trans3:probe16 0.125397684093821 0.0474812765539431 2.64099226463209 0.00838535030471766 ** df.mm.trans3:probe17 0.13358351714734 0.0474812765539431 2.81339354883555 0.00499082237907188 ** df.mm.trans3:probe18 0.2706974683079 0.0474812765539431 5.70114133305499 1.53375386982099e-08 *** df.mm.trans3:probe19 0.024653611257612 0.0474812765539431 0.519228063078785 0.603707800504116 df.mm.trans3:probe20 -0.0589636366497018 0.0474812765539431 -1.24182922046575 0.214568443976699 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.2319495774226 0.194700061135466 21.7357383081567 3.19728896960901e-87 *** df.mm.trans1 0.0399847562690051 0.166050526138848 0.240798732763850 0.809756688387835 df.mm.trans2 -0.0445629156708178 0.144637985300546 -0.308099670900556 0.758065737852565 df.mm.exp2 0.0451847409039435 0.181336708597915 0.249175918396828 0.803271954386737 df.mm.exp3 0.0396397997884259 0.181336708597915 0.218597768178978 0.827004576692474 df.mm.exp4 0.00187690078498712 0.181336708597915 0.0103503631421305 0.991743658752813 df.mm.exp5 0.142280423026699 0.181336708597915 0.78462008121137 0.432847940811192 df.mm.exp6 0.0757705639356628 0.181336708597915 0.417844597056583 0.676143571111439 df.mm.exp7 0.094655614448068 0.181336708597915 0.521988157720186 0.601785316729742 df.mm.exp8 -0.103434146630354 0.181336708597915 -0.570398279698032 0.568525912678492 df.mm.trans1:exp2 0.0234986024827035 0.164865846138504 0.142531658515629 0.886686586300318 df.mm.trans2:exp2 -0.0586142542805804 0.109429131361521 -0.535636658641992 0.592319665475336 df.mm.trans1:exp3 0.0263375145268846 0.164865846138504 0.159751186457130 0.87310686608609 df.mm.trans2:exp3 -0.111007007729064 0.109429131361520 -1.01441916195360 0.310609356141664 df.mm.trans1:exp4 0.0382016546685932 0.164865846138503 0.231713575390867 0.816804259586646 df.mm.trans2:exp4 -0.0430756237706986 0.109429131361520 -0.393639456283262 0.693924730022005 df.mm.trans1:exp5 -0.0509404622571791 0.164865846138504 -0.308981292671037 0.757395186399094 df.mm.trans2:exp5 -0.094880814162091 0.109429131361520 -0.867052611873833 0.386105244723851 df.mm.trans1:exp6 0.0710211826407003 0.164865846138504 0.43078165856763 0.666712857037575 df.mm.trans2:exp6 -0.136146207153801 0.109429131361521 -1.2441495738828 0.213713719546938 df.mm.trans1:exp7 -0.0533916457585859 0.164865846138504 -0.323849038531192 0.746114856542453 df.mm.trans2:exp7 -0.0909581808220933 0.109429131361520 -0.83120627652243 0.406040326124878 df.mm.trans1:exp8 0.0770023795485257 0.164865846138504 0.46706083371468 0.640550304330263 df.mm.trans2:exp8 0.0206876425924487 0.109429131361520 0.189050596811402 0.850088582795182 df.mm.trans1:probe2 -0.123692114991177 0.125224599504016 -0.987762112884298 0.323489791827538 df.mm.trans1:probe3 0.0208862848937695 0.125224599504016 0.166790590478987 0.867565957611596 df.mm.trans1:probe4 -0.0363143957929762 0.125224599504016 -0.289994106084657 0.771876259522056 df.mm.trans1:probe5 -0.245538530993612 0.125224599504016 -1.96078511703076 0.050160094456272 . df.mm.trans1:probe6 -0.0033719815974838 0.125224599504016 -0.0269274696093211 0.978522544706861 df.mm.trans1:probe7 -0.0186249617846503 0.125224599504016 -0.148732452396887 0.88179244159992 df.mm.trans1:probe8 0.0170123227204743 0.125224599504016 0.135854478975025 0.891961569235797 df.mm.trans1:probe9 -0.00238856230868757 0.125224599504016 -0.0190742259759511 0.984785405777897 df.mm.trans1:probe10 -0.178306910930555 0.125224599504016 -1.42389683526068 0.154764380337203 df.mm.trans1:probe11 -0.125260971331837 0.125224599504016 -1.00029045273824 0.317393405772294 df.mm.trans1:probe12 -0.0395294318433275 0.125224599504016 -0.315668263263719 0.752315137691157 df.mm.trans1:probe13 0.0521358256754602 0.125224599504016 0.416338529984983 0.677244796316619 df.mm.trans1:probe14 -0.0590180191860063 0.125224599504016 -0.471297328318575 0.63752334538491 df.mm.trans1:probe15 -0.177531821902484 0.125224599504016 -1.41770724446829 0.156563896080580 df.mm.trans1:probe16 -0.131231229700727 0.125224599504016 -1.04796685491910 0.294887692533794 df.mm.trans1:probe17 -0.0323825496862655 0.125224599504016 -0.258595753666012 0.795996297235494 df.mm.trans1:probe18 -0.0444733085954886 0.125224599504016 -0.355148339636433 0.722547616320104 df.mm.trans1:probe19 0.107659269290524 0.125224599504016 0.859729395956833 0.390128490447059 df.mm.trans1:probe20 0.0155745598077241 0.125224599504016 0.124373005538937 0.901043025767891 df.mm.trans1:probe21 0.0121290340942389 0.125224599504016 0.0968582382557346 0.922856894593714 df.mm.trans1:probe22 -0.121162854501322 0.125224599504016 -0.967564320279067 0.333478021894177 df.mm.trans2:probe2 0.0598606722150859 0.125224599504016 0.478026461671104 0.632727843081533 df.mm.trans2:probe3 0.132732689875782 0.125224599504016 1.05995699248793 0.289400497947667 df.mm.trans2:probe4 -0.0211334070960814 0.125224599504016 -0.168764022243119 0.866013771605914 df.mm.trans2:probe5 0.124769108397393 0.125224599504016 0.996362606800683 0.31929655552394 df.mm.trans2:probe6 0.00713643666858453 0.125224599504016 0.0569890955678853 0.954564393760052 df.mm.trans3:probe2 -0.051388245313826 0.125224599504016 -0.410368613813598 0.68161673582828 df.mm.trans3:probe3 0.255318619029436 0.125224599504016 2.03888549087552 0.0417038847572178 * df.mm.trans3:probe4 -0.0937085395288519 0.125224599504016 -0.748323731119992 0.454427387277213 df.mm.trans3:probe5 0.0988812159867373 0.125224599504016 0.78963092218607 0.429916187891942 df.mm.trans3:probe6 0.0835712791164053 0.125224599504016 0.66737110318109 0.50467729138854 df.mm.trans3:probe7 0.0536572388870445 0.125224599504016 0.428488005548173 0.668381070026967 df.mm.trans3:probe8 0.0599217524863098 0.125224599504016 0.478514227425324 0.632380834920461 df.mm.trans3:probe9 0.0320319101787734 0.125224599504016 0.255795668787474 0.798157193096891 df.mm.trans3:probe10 -0.0441847831455540 0.125224599504016 -0.352844275969412 0.724273848442996 df.mm.trans3:probe11 -0.0336255429515354 0.125224599504016 -0.268521864591446 0.788348786820811 df.mm.trans3:probe12 0.164229797373685 0.125224599504016 1.31148191349112 0.189972962518181 df.mm.trans3:probe13 -0.0405166397685571 0.125224599504016 -0.323551761626977 0.746339880085436 df.mm.trans3:probe14 0.133662044350934 0.125224599504016 1.06737849336582 0.286038827180308 df.mm.trans3:probe15 0.0276511070448429 0.125224599504016 0.220812102049935 0.825280360857089 df.mm.trans3:probe16 -0.0280090938559862 0.125224599504016 -0.223670859934257 0.823055605783708 df.mm.trans3:probe17 0.164954791255787 0.125224599504016 1.31727146191030 0.188026270856206 df.mm.trans3:probe18 -0.094810167742981 0.125224599504016 -0.757120950025002 0.449142088481714 df.mm.trans3:probe19 -0.096442715820625 0.125224599504016 -0.770157909888401 0.441374187528275 df.mm.trans3:probe20 -0.00672928262934637 0.125224599504016 -0.0537377053390421 0.957154052403167