fitVsDatCorrelation=0.895872499887992 cont.fitVsDatCorrelation=0.248481733519268 fstatistic=11317.9297309148,53,715 cont.fstatistic=2370.67924099051,53,715 residuals=-0.761022890338255,-0.0807023260357123,-0.00427055600676823,0.071407847858491,0.680674501988595 cont.residuals=-0.59377951257255,-0.197777005717103,-0.0808366990561075,0.0965049257927202,1.24434789051645 predictedValues: Include Exclude Both Lung 56.5382741615817 58.2220390231223 79.4672871725789 cerebhem 61.2664379200703 60.9509258162891 60.9989628840313 cortex 52.9209901162747 61.5844468605448 84.7458848123119 heart 53.2635151661408 53.7990474486245 75.5004191124039 kidney 56.271476236997 51.0376070785215 67.2554398183831 liver 54.6887784339281 50.2593985572089 64.9431428345114 stomach 55.4375278721693 53.8285967595842 68.5714068188851 testicle 58.7197101170026 59.9327153589525 78.7404675067455 diffExp=-1.68376486154059,0.315512103781202,-8.6634567442701,-0.5355322824837,5.23386915847547,4.4293798767192,1.60893111258510,-1.21300524194994 diffExpScore=15.7045100032187 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 57.4887578652781 57.6980665903745 63.1316168051512 cerebhem 59.6193935189834 58.76906545586 58.5104316208081 cortex 57.9912642218703 59.4902244758582 60.9006708698895 heart 61.6787591849847 59.5831155350251 66.6603058904584 kidney 60.1884086795527 59.813971623302 55.1387688597049 liver 57.0164805538603 73.3375821437017 55.2476059248911 stomach 58.9766507843531 55.2366512742322 54.7658890130658 testicle 59.1974487299332 63.3956717629084 73.2947534138226 cont.diffExp=-0.209308725096385,0.850328063123442,-1.49896025398792,2.09564364995956,0.374437056250677,-16.3211015898414,3.73999951012090,-4.19822303297517 cont.diffExpScore=1.81157086389629 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,-1,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.385474087940051 cont.tran.correlation=-0.407034827393647 tran.covariance=0.00147758576846484 cont.tran.covariance=-0.000868208983193255 tran.mean=56.1700929329383 cont.tran.mean=59.9675945250049 weightedLogRatios: wLogRatio Lung -0.118839832297191 cerebhem 0.0212341993165746 cortex -0.613199703131333 heart -0.0398191871830097 kidney 0.388681812866630 liver 0.334417155822912 stomach 0.117823227880663 testicle -0.0834853852124827 cont.weightedLogRatios: wLogRatio Lung -0.0147310995475382 cerebhem 0.0586218790190193 cortex -0.103942753145205 heart 0.141887178688849 kidney 0.0255509082584597 liver -1.04952624471114 stomach 0.264967467726796 testicle -0.281957440589274 varWeightedLogRatios=0.096561135425 cont.varWeightedLogRatios=0.167352031755282 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.33753484859117 0.0746255362698246 44.7237636795479 2.34520249040052e-209 *** df.mm.trans1 0.53978985769826 0.0662689204569491 8.14544516458404 1.68378966292395e-15 *** df.mm.trans2 0.693423052322645 0.0602802007954168 11.5033301676621 3.24232911392573e-28 *** df.mm.exp2 0.39060803792027 0.0812666684846996 4.80649748788227 1.87291738872819e-06 *** df.mm.exp3 -0.0742841200684558 0.0812666684846996 -0.914078569400707 0.360983699480456 df.mm.exp4 -0.0874672006339612 0.0812666684846996 -1.07629858913718 0.282156764382333 df.mm.exp5 0.0304162848078053 0.0812666684846996 0.374277491312836 0.708308845189125 df.mm.exp6 0.0215076117046033 0.0812666684846996 0.264654773053144 0.79135169325482 df.mm.exp7 0.0493496562936661 0.0812666684846996 0.607255806271391 0.543873956006172 df.mm.exp8 0.0760044138805636 0.0812666684846996 0.935247073588026 0.349976737826483 df.mm.trans1:exp2 -0.310293677893032 0.0771665143723553 -4.02109231467625 6.4085898703341e-05 *** df.mm.trans2:exp2 -0.344802952771955 0.0649800202404593 -5.30629186473024 1.49393203711403e-07 *** df.mm.trans1:exp3 0.00816634122344557 0.0771665143723553 0.105827524929273 0.91574888020928 df.mm.trans2:exp3 0.130429512267404 0.0649800202404593 2.00722486365421 0.0451016226729789 * df.mm.trans1:exp4 0.0278009513602214 0.0771665143723553 0.360272218932581 0.718749970982573 df.mm.trans2:exp4 0.00845900179861005 0.0649800202404593 0.130178503597066 0.89646181611102 df.mm.trans1:exp5 -0.0351463444183386 0.0771665143723553 -0.455461085733966 0.648915836256922 df.mm.trans2:exp5 -0.162117490558579 0.0649800202404593 -2.49488211851368 0.0128244241529550 * df.mm.trans1:exp6 -0.0547668983793811 0.0771665143723553 -0.709723625912552 0.478106921307365 df.mm.trans2:exp6 -0.168574006720554 0.0649800202404593 -2.59424367823747 0.00967409372481982 ** df.mm.trans1:exp7 -0.0690107210534796 0.0771665143723553 -0.894309165248528 0.371457431646468 df.mm.trans2:exp7 -0.127808752490573 0.0649800202404593 -1.96689308525321 0.0495818471591653 * df.mm.trans1:exp8 -0.0381467938829507 0.0771665143723553 -0.494343876916341 0.621215186014276 df.mm.trans2:exp8 -0.0470458520244717 0.0649800202404593 -0.724004884122503 0.469299551775173 df.mm.trans1:probe2 -0.0253052126739736 0.0422658406057856 -0.598715471200392 0.549552265022899 df.mm.trans1:probe3 0.782079783376134 0.0422658406057856 18.5038265456639 9.29667451717826e-63 *** df.mm.trans1:probe4 0.0297410357831154 0.0422658406057856 0.703666018629812 0.481869868128942 df.mm.trans1:probe5 0.127925861003048 0.0422658406057856 3.02669624381105 0.00256121010910651 ** df.mm.trans1:probe6 0.173008157943039 0.0422658406057856 4.09333294838945 4.73712469064691e-05 *** df.mm.trans1:probe7 0.0462869142892948 0.0422658406057856 1.09513767207457 0.273825085205887 df.mm.trans1:probe8 0.89822826128534 0.0422658406057856 21.2518726331066 4.6127128091629e-78 *** df.mm.trans1:probe9 0.2223182517576 0.0422658406057856 5.259998347866 1.90540477055346e-07 *** df.mm.trans1:probe10 1.17046056272755 0.0422658406057856 27.6928258364588 2.91435387203173e-115 *** df.mm.trans1:probe11 0.0627098560258843 0.0422658406057856 1.48370067002288 0.138329013813150 df.mm.trans1:probe12 0.00390824784770183 0.0422658406057856 0.0924682389297339 0.926351926607328 df.mm.trans1:probe13 0.113194792840266 0.0422658406057856 2.67816258278253 0.00757272811261245 ** df.mm.trans1:probe14 0.0611949623415592 0.0422658406057856 1.44785863629984 0.148094920705890 df.mm.trans1:probe15 0.00996999095455942 0.0422658406057856 0.235887676943415 0.813587382238173 df.mm.trans1:probe16 0.0644521889401498 0.0422658406057856 1.52492386325157 0.127720269977449 df.mm.trans1:probe17 0.0652741446422692 0.0422658406057856 1.54437114479947 0.122940907964696 df.mm.trans1:probe18 0.0737065026439098 0.0422658406057856 1.74387878219132 0.0816099935375376 . df.mm.trans1:probe19 0.0983233804251368 0.0422658406057856 2.32630840924711 0.0202807298053061 * df.mm.trans1:probe20 -0.0860254055125799 0.0422658406057856 -2.03534117101658 0.0421848361146067 * df.mm.trans1:probe21 0.0823517877317831 0.0422658406057856 1.9484242251297 0.0517550797932495 . df.mm.trans1:probe22 0.123617089622872 0.0422658406057856 2.92475171086388 0.00355641027644871 ** df.mm.trans2:probe2 0.038247397818342 0.0422658406057856 0.904924574317031 0.365810159066451 df.mm.trans2:probe3 0.0339118063717780 0.0422658406057856 0.802345484810634 0.422619735053804 df.mm.trans2:probe4 0.0152180851536192 0.0422658406057856 0.360056370238997 0.718911307553337 df.mm.trans2:probe5 0.117591773664419 0.0422658406057856 2.78219413074496 0.00554161506085223 ** df.mm.trans2:probe6 0.128091532158184 0.0422658406057856 3.03061598497228 0.00252861824939202 ** df.mm.trans3:probe2 -0.220582918290103 0.0422658406057856 -5.2189407599268 2.36060937315458e-07 *** df.mm.trans3:probe3 -0.39755411472652 0.0422658406057856 -9.4060382812332 6.85514889424632e-20 *** df.mm.trans3:probe4 -0.163472181942195 0.0422658406057856 -3.86771396473344 0.00011988205914947 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.04255981286601 0.162690070497494 24.8482270645293 9.8739024557566e-99 *** df.mm.trans1 0.0131112650731479 0.144471931189235 0.0907530270082302 0.927714252452515 df.mm.trans2 0.0443612997527096 0.131416008610648 0.337563895157864 0.735790930097039 df.mm.exp2 0.130800102377352 0.177168308406762 0.738281601001955 0.460585594371739 df.mm.exp3 0.0752688207773156 0.177168308406762 0.424843593384125 0.671078512203485 df.mm.exp4 0.048110812425852 0.177168308406762 0.271554279986656 0.786043143780984 df.mm.exp5 0.217274606455510 0.177168308406762 1.22637399662172 0.220461685547859 df.mm.exp6 0.364997169603721 0.177168308406762 2.06017189465805 0.039743441936081 * df.mm.exp7 0.124109409080460 0.177168308406762 0.700516984084514 0.483832387012721 df.mm.exp8 -0.0258063693770637 0.177168308406762 -0.145660189506436 0.884230703610016 df.mm.trans1:exp2 -0.0944085999227905 0.168229620727842 -0.561188924485081 0.574844585339825 df.mm.trans2:exp2 -0.112408148587043 0.14166201815455 -0.793495321126994 0.427752538064891 df.mm.trans1:exp3 -0.0665658517402721 0.168229620727842 -0.395684490354769 0.692455833146819 df.mm.trans2:exp3 -0.0446804812338153 0.141662018154550 -0.315401981532339 0.752548495080235 df.mm.trans1:exp4 0.0222393863507044 0.168229620727842 0.132196614689412 0.894865970463874 df.mm.trans2:exp4 -0.0159622398696167 0.14166201815455 -0.112678331690872 0.910317207778469 df.mm.trans1:exp5 -0.171384232832756 0.168229620727842 -1.01875182320013 0.308665386379157 df.mm.trans2:exp5 -0.181258998589525 0.141662018154550 -1.27951726899567 0.20113000291523 df.mm.trans1:exp6 -0.373246224337271 0.168229620727842 -2.21867125850031 0.0268223884899071 * df.mm.trans2:exp6 -0.125147640261493 0.141662018154550 -0.883424095546625 0.377304111938101 df.mm.trans1:exp7 -0.0985572062231636 0.168229620727842 -0.585849304045019 0.558161661635173 df.mm.trans2:exp7 -0.167706368757123 0.141662018154550 -1.18384850746767 0.236866457611297 df.mm.trans1:exp8 0.0550954012590064 0.168229620727842 0.327501191648875 0.74338470725052 df.mm.trans2:exp8 0.119978294779116 0.14166201815455 0.84693340065382 0.397315628333756 df.mm.trans1:probe2 0.0635413245206537 0.0921431581131776 0.689593517541554 0.490673584241733 df.mm.trans1:probe3 -0.103369941241550 0.0921431581131776 -1.12184065923357 0.262306797794518 df.mm.trans1:probe4 0.0320796602174504 0.0921431581131776 0.348150214018577 0.727829851268331 df.mm.trans1:probe5 0.0564027879275067 0.0921431581131776 0.61212128043439 0.54065212152112 df.mm.trans1:probe6 0.0467812174971217 0.0921431581131776 0.507701477299718 0.611819284117029 df.mm.trans1:probe7 -0.00937079320324897 0.0921431581131775 -0.101698198706615 0.919024744338138 df.mm.trans1:probe8 0.101872561820532 0.0921431581131776 1.10559008293816 0.269275853693304 df.mm.trans1:probe9 0.0117430295071700 0.0921431581131776 0.127443314811788 0.898625368548242 df.mm.trans1:probe10 -0.02686826707936 0.0921431581131775 -0.291592643768062 0.770682676421103 df.mm.trans1:probe11 -0.0368185761704514 0.0921431581131776 -0.399580141644678 0.689585116226016 df.mm.trans1:probe12 -0.0391460810621073 0.0921431581131776 -0.424839802148141 0.671081274855334 df.mm.trans1:probe13 -0.0058895984304651 0.0921431581131776 -0.0639179137232416 0.949053453392296 df.mm.trans1:probe14 -0.0271699011500931 0.0921431581131776 -0.294866181130029 0.768181701893687 df.mm.trans1:probe15 0.0476288585843973 0.0921431581131775 0.51690065285038 0.605385359890478 df.mm.trans1:probe16 0.0691081677064067 0.0921431581131775 0.750008672608361 0.453496195786415 df.mm.trans1:probe17 -0.0212902520790659 0.0921431581131776 -0.231056244598384 0.81733720807638 df.mm.trans1:probe18 -0.0697795744934042 0.0921431581131776 -0.757295234093186 0.449122493418209 df.mm.trans1:probe19 -0.0403002469960516 0.0921431581131776 -0.437365593075849 0.661978304021686 df.mm.trans1:probe20 -0.083602016669139 0.0921431581131776 -0.907305744463982 0.364550815840339 df.mm.trans1:probe21 -0.0197719783500142 0.0921431581131776 -0.214578909111501 0.830156822464464 df.mm.trans1:probe22 -0.0519036614429545 0.0921431581131776 -0.563293710632344 0.57341150098974 df.mm.trans2:probe2 -0.0265745863610949 0.0921431581131776 -0.288405421577301 0.773120004295931 df.mm.trans2:probe3 -0.0148118016170598 0.0921431581131776 -0.160747709546343 0.872337516882718 df.mm.trans2:probe4 -0.0148634658059612 0.0921431581131776 -0.161308404338657 0.87189607217078 df.mm.trans2:probe5 -0.181760792690739 0.0921431581131776 -1.97259130696916 0.0489270216743431 * df.mm.trans2:probe6 -0.078963829843687 0.0921431581131776 -0.856968997597167 0.391749149616665 df.mm.trans3:probe2 0.180624586323942 0.0921431581131776 1.96026042543587 0.0503533179442315 . df.mm.trans3:probe3 -0.00702639024277951 0.0921431581131776 -0.0762551489080626 0.93923743942392 df.mm.trans3:probe4 0.00715910426288558 0.0921431581131776 0.077695451398488 0.938092049257447