fitVsDatCorrelation=0.947837709103202 cont.fitVsDatCorrelation=0.245774981459619 fstatistic=6286.51024242672,49,623 cont.fstatistic=668.456440191814,49,623 residuals=-0.931690239394777,-0.104695669954369,-0.0023655476554883,0.106917938765793,0.902708097826607 cont.residuals=-1.07432980208454,-0.466042566741556,-0.137861915378261,0.318196261533829,2.11391397290439 predictedValues: Include Exclude Both Lung 96.7527706139119 135.241383644301 54.3159274725867 cerebhem 87.9797428014617 92.1289172788843 58.637162219701 cortex 71.5797683887597 88.9148841929156 54.0243774843368 heart 74.7101463973267 97.4699439090095 52.284096155799 kidney 99.2203209115658 142.001681705160 53.1513267681914 liver 87.7546833044741 115.773685969987 52.360567121993 stomach 80.6250593266174 102.908768305229 56.4600505786099 testicle 83.889345633378 118.418743124809 55.4762074632152 diffExp=-38.488613030389,-4.14917447742262,-17.3351158041559,-22.7597975116827,-42.7813607935947,-28.0190026655129,-22.283708978612,-34.5293974914310 diffExpScore=0.995268426220177 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,-1,0,0,-1 diffExp1.4Score=0.666666666666667 diffExp1.3=-1,0,0,-1,-1,-1,0,-1 diffExp1.3Score=0.833333333333333 diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 92.4332965371946 97.1057084917207 98.2705151571818 cerebhem 80.070106827982 127.921148730365 92.5382110390665 cortex 86.7736679885257 88.0026546936612 98.8360126206373 heart 106.447937649139 80.051091374267 111.247117618564 kidney 98.4502700604225 92.1164599319418 87.634777092344 liver 80.3888132235159 105.569240575603 87.1430861899464 stomach 107.17303234785 106.719208320964 84.7163074840806 testicle 93.9406297228265 104.600267103015 98.1554050546127 cont.diffExp=-4.67241195452601,-47.8510419023831,-1.22898670513555,26.3968462748722,6.33381012848068,-25.1804273520872,0.453824026886224,-10.6596373801886 cont.diffExpScore=2.13867287744606 cont.diffExp1.5=0,-1,0,0,0,0,0,0 cont.diffExp1.5Score=0.5 cont.diffExp1.4=0,-1,0,0,0,0,0,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=0,-1,0,1,0,-1,0,0 cont.diffExp1.3Score=1.5 cont.diffExp1.2=0,-1,0,1,0,-1,0,0 cont.diffExp1.2Score=1.5 tran.correlation=0.852853992344787 cont.tran.correlation=-0.527110936519311 tran.covariance=0.0166458685230511 cont.tran.covariance=-0.00870674500035677 tran.mean=98.460615344237 cont.tran.mean=96.7352208486871 weightedLogRatios: wLogRatio Lung -1.58730603766446 cerebhem -0.207377060655940 cortex -0.949714317655392 heart -1.18247079583862 kidney -1.71238900416154 liver -1.27825101076477 stomach -1.10103601321039 testicle -1.58639279051428 cont.weightedLogRatios: wLogRatio Lung -0.224430083601701 cerebhem -2.16319159490439 cortex -0.0628697625689507 heart 1.28962977665024 kidney 0.302984567507879 liver -1.23251438317890 stomach 0.0198269518566137 testicle -0.494035906566735 varWeightedLogRatios=0.232287483862708 cont.varWeightedLogRatios=1.06143904581014 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.14426380947023 0.103396216558484 49.7529211483332 3.5386631967525e-219 *** df.mm.trans1 -0.905324288662848 0.0889234495542536 -10.1809398218464 1.26833108559515e-22 *** df.mm.trans2 -0.47215772131369 0.0811106655174423 -5.82115457075316 9.35897121003528e-09 *** df.mm.exp2 -0.555475869266657 0.106877383250226 -5.19731913688563 2.74427918843328e-07 *** df.mm.exp3 -0.715346024813447 0.106877383250225 -6.6931468853298 4.87601822195309e-11 *** df.mm.exp4 -0.547934931223287 0.106877383250226 -5.12676222564731 3.9394176609862e-07 *** df.mm.exp5 0.095636004944369 0.106877383250226 0.89481985838353 0.37122896504288 df.mm.exp6 -0.216373907437083 0.106877383250225 -2.02450603539291 0.0433442295811122 * df.mm.exp7 -0.494283626192201 0.106877383250225 -4.62477290480592 4.56238628286223e-06 *** df.mm.exp8 -0.296631274208451 0.106877383250226 -2.77543541194273 0.00567826054380781 ** df.mm.trans1:exp2 0.460423493471728 0.0969457879485997 4.74928826939694 2.53565820749786e-06 *** df.mm.trans2:exp2 0.171603531400995 0.0797624719201565 2.15143196129595 0.0318258694811582 * df.mm.trans1:exp3 0.413999526112244 0.0969457879485996 4.27042303613794 2.25558874236158e-05 *** df.mm.trans2:exp3 0.29596437074522 0.0797624719201565 3.71057169644247 0.000225283538113203 *** df.mm.trans1:exp4 0.289391874414162 0.0969457879485997 2.98508971392957 0.00294610836602444 ** df.mm.trans2:exp4 0.220417785287065 0.0797624719201565 2.76342721057725 0.00588878726846658 ** df.mm.trans1:exp5 -0.0704521320693233 0.0969457879485996 -0.726716792550872 0.467672424044759 df.mm.trans2:exp5 -0.0468583132350457 0.0797624719201565 -0.587473182651004 0.557098810342541 df.mm.trans1:exp6 0.118760171013873 0.0969457879485996 1.22501630578152 0.22103202975589 df.mm.trans2:exp6 0.0609500010638378 0.0797624719201565 0.764143833516717 0.44507084228549 df.mm.trans1:exp7 0.311934168805717 0.0969457879485996 3.21761445655693 0.00135965259527439 ** df.mm.trans2:exp7 0.221065268492970 0.0797624719201565 2.77154485275056 0.00574571217503108 ** df.mm.trans1:exp8 0.153970922406564 0.0969457879485996 1.58821673086199 0.112744687031189 df.mm.trans2:exp8 0.16379707873279 0.0797624719201565 2.05356071332335 0.0404351150190806 * df.mm.trans1:probe2 0.0257874470324515 0.0616079730449021 0.418573209893738 0.67567232698492 df.mm.trans1:probe3 0.283788079382579 0.0616079730449021 4.60635312860794 4.97091367130466e-06 *** df.mm.trans1:probe4 0.0153758121986178 0.0616079730449021 0.249575037753820 0.80299827038652 df.mm.trans1:probe5 0.0370481989894371 0.0616079730449021 0.601353967000912 0.547822995290796 df.mm.trans1:probe6 -0.0592354465344922 0.0616079730449021 -0.961489943701268 0.336678899421946 df.mm.trans1:probe7 0.283314769150619 0.0616079730449021 4.59867051532646 5.15148649118361e-06 *** df.mm.trans1:probe8 0.0516743834323334 0.0616079730449021 0.838761297903296 0.401924977677023 df.mm.trans1:probe9 -0.00568864897621253 0.0616079730449021 -0.092336246350232 0.926460574240075 df.mm.trans1:probe10 0.51370933368029 0.0616079730449021 8.33835798015754 4.8349940391453e-16 *** df.mm.trans1:probe11 0.808378731765867 0.0616079730449021 13.1213330322796 6.6702258493861e-35 *** df.mm.trans1:probe12 1.75490269480813 0.0616079730449021 28.4849932252940 6.43324697737009e-115 *** df.mm.trans1:probe13 1.71149324584427 0.0616079730449021 27.7803855776407 4.02621480748546e-111 *** df.mm.trans1:probe14 1.38392934053860 0.0616079730449021 22.4634778931931 2.59449422202152e-82 *** df.mm.trans1:probe15 0.526349904320586 0.0616079730449021 8.5435354923455 1.00057215775222e-16 *** df.mm.trans2:probe2 0.226174775822195 0.0616079730449021 3.67119326677654 0.000262133114298679 *** df.mm.trans2:probe3 0.82326266625051 0.0616079730449021 13.3629240755979 5.41805897487854e-36 *** df.mm.trans2:probe4 0.321502533432122 0.0616079730449021 5.21852152476758 2.45974822261105e-07 *** df.mm.trans2:probe5 1.09091873025484 0.0616079730449021 17.7074277295204 3.9035490915828e-57 *** df.mm.trans2:probe6 0.592557862811925 0.0616079730449021 9.61820091662564 1.63850968824537e-20 *** df.mm.trans3:probe2 0.182783986364363 0.0616079730449021 2.96688849398021 0.00312361792130926 ** df.mm.trans3:probe3 0.201394938685243 0.0616079730449021 3.26897524348772 0.00113879683566511 ** df.mm.trans3:probe4 0.0528863705609149 0.0616079730449021 0.85843386735625 0.390983076791224 df.mm.trans3:probe5 0.724716773235064 0.0616079730449021 11.7633601207244 5.45108599633665e-29 *** df.mm.trans3:probe6 0.101233188657881 0.0616079730449021 1.64318323837887 0.100849583598208 df.mm.trans3:probe7 0.333234698885825 0.0616079730449021 5.40895410798455 9.04767609266714e-08 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.64413095305571 0.3144272693273 14.7701278040914 1.51177502181736e-42 *** df.mm.trans1 -0.093160929413686 0.270415672382876 -0.34451009659596 0.730578965285158 df.mm.trans2 -0.0100017736938868 0.246657043370092 -0.0405493131565671 0.967668184191119 df.mm.exp2 0.192131588648194 0.325013476186604 0.591149606787022 0.554634595215849 df.mm.exp3 -0.167355240735686 0.325013476186604 -0.514917851097352 0.606793061411163 df.mm.exp4 -0.175996316035982 0.325013476186604 -0.541504672670664 0.5883533680083 df.mm.exp5 0.124863880973578 0.325013476186604 0.384180626719269 0.700975665729525 df.mm.exp6 0.0641272427503261 0.325013476186604 0.197306411730164 0.843652116226736 df.mm.exp7 0.390774306883975 0.325013476186604 1.20233262776961 0.229691459539864 df.mm.exp8 0.091693706054691 0.325013476186604 0.282122781893652 0.777943059552394 df.mm.trans1:exp2 -0.335716268252719 0.294811554929768 -1.13874867738035 0.255245678690739 df.mm.trans2:exp2 0.0834822965144338 0.242557194793144 0.344175717342166 0.730830280419668 df.mm.trans1:exp3 0.104171186319732 0.294811554929768 0.353348383324215 0.723946808969242 df.mm.trans2:exp3 0.0689220583410302 0.242557194793144 0.284147655977832 0.77639164024773 df.mm.trans1:exp4 0.317165067424712 0.294811554929768 1.07582305415495 0.282422842499734 df.mm.trans2:exp4 -0.0171387743482014 0.242557194793144 -0.0706586929438132 0.943692071957729 df.mm.trans1:exp5 -0.061799598640122 0.294811554929768 -0.209624072078329 0.834029619749107 df.mm.trans2:exp5 -0.177610399026783 0.242557194793144 -0.732241313964123 0.464296664937720 df.mm.trans1:exp6 -0.203739481127674 0.294811554929768 -0.691083771042184 0.489770316346429 df.mm.trans2:exp6 0.0194396402715972 0.242557194793144 0.0801445625563717 0.936148018291152 df.mm.trans1:exp7 -0.242816919684636 0.294811554929768 -0.823634337339598 0.410462538735584 df.mm.trans2:exp7 -0.296373306417271 0.242557194793144 -1.22186978073366 0.222218992323150 df.mm.trans1:exp8 -0.0755179878910448 0.294811554929768 -0.256156811455491 0.79791431448924 df.mm.trans2:exp8 -0.0173477642522912 0.242557194793144 -0.0715203037662339 0.94300661883335 df.mm.trans1:probe2 -0.0450669264292723 0.187349473492017 -0.240550056476101 0.809982998196365 df.mm.trans1:probe3 -0.143918127032347 0.187349473492017 -0.768180045291025 0.442671297865453 df.mm.trans1:probe4 0.072959771168001 0.187349473492017 0.389431418237266 0.697090115186481 df.mm.trans1:probe5 -0.0215719016132364 0.187349473492017 -0.115142579325987 0.908369211496307 df.mm.trans1:probe6 0.0560368755570138 0.187349473492017 0.299103458966495 0.764960855516843 df.mm.trans1:probe7 0.113663083422175 0.187349473492017 0.606690167330621 0.544277564044442 df.mm.trans1:probe8 -0.076319182491377 0.187349473492017 -0.407362673984932 0.683881569174094 df.mm.trans1:probe9 -0.175954493325852 0.187349473492017 -0.939177944011409 0.348003364546171 df.mm.trans1:probe10 0.104609235987385 0.187349473492017 0.558364184524077 0.576796334655907 df.mm.trans1:probe11 -0.159698952067033 0.187349473492017 -0.852412067620985 0.39431299145666 df.mm.trans1:probe12 -0.266316054140293 0.187349473492017 -1.42149347514255 0.155673724812934 df.mm.trans1:probe13 0.0301346586228870 0.187349473492017 0.160847308835224 0.872265805773605 df.mm.trans1:probe14 0.135794261997006 0.187349473492017 0.724817953666643 0.468835850449574 df.mm.trans1:probe15 -0.162972920627548 0.187349473492017 -0.869887262504064 0.384697044433391 df.mm.trans2:probe2 -0.101364230385347 0.187349473492017 -0.541043582861558 0.588670941959578 df.mm.trans2:probe3 -0.244973881139218 0.187349473492017 -1.30757709948759 0.191499111652979 df.mm.trans2:probe4 -0.26877719680318 0.187349473492017 -1.43463011554518 0.151894302557534 df.mm.trans2:probe5 -0.132746488513471 0.187349473492017 -0.708550101792134 0.478868740037368 df.mm.trans2:probe6 -0.0104154107759168 0.187349473492017 -0.0555934883711354 0.955683475696115 df.mm.trans3:probe2 0.000817280244117858 0.187349473492017 0.00436233008230303 0.99652077167546 df.mm.trans3:probe3 0.214413475720722 0.187349473492017 1.14445731671543 0.252873531477736 df.mm.trans3:probe4 0.137533757619928 0.187349473492017 0.734102717538667 0.463162323598261 df.mm.trans3:probe5 -0.0858890479872917 0.187349473492017 -0.458442964297691 0.646794105807799 df.mm.trans3:probe6 -0.101147220188894 0.187349473492017 -0.539885265240441 0.589469078896858 df.mm.trans3:probe7 0.161562247319427 0.187349473492017 0.862357626675217 0.388822569730056