fitVsDatCorrelation=0.912936336232076 cont.fitVsDatCorrelation=0.257474257349882 fstatistic=4016.18632672538,42,462 cont.fstatistic=707.337537264917,42,462 residuals=-0.675980829736588,-0.128865497933397,0.0026498756277306,0.124317363775147,0.793372951485111 cont.residuals=-0.933192414028134,-0.358227742972814,-0.122868034321479,0.187900440522318,1.88979075787902 predictedValues: Include Exclude Both Lung 71.5651291030753 80.2937118131445 61.9802833094327 cerebhem 142.894272994033 69.0533036479096 132.106623419737 cortex 170.845027126546 66.3930913196327 178.389737415108 heart 61.41947142507 69.3482330619943 59.2516915955643 kidney 70.5276079625204 85.7435079893618 61.1015098336778 liver 60.3262030413755 75.4008120064748 49.2152931395609 stomach 70.2893252346756 68.4579906252443 61.5472445134932 testicle 66.6774453295017 73.5041457552241 62.1558599093501 diffExp=-8.72858271006918,73.8409693461233,104.451935806913,-7.92876163692422,-15.2159000268414,-15.0746089650993,1.83133460943138,-6.82670042572241 diffExpScore=1.83666564777509 diffExp1.5=0,1,1,0,0,0,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=0,1,1,0,0,0,0,0 diffExp1.4Score=0.666666666666667 diffExp1.3=0,1,1,0,0,0,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=0,1,1,0,-1,-1,0,0 diffExp1.2Score=4 cont.predictedValues: Include Exclude Both Lung 72.2255724339608 95.4173306767095 84.4823183861559 cerebhem 69.2084548228675 88.957862299249 73.900925287388 cortex 75.4190922790219 101.646689194838 136.876309006798 heart 83.982751270845 79.8249613770274 77.4211421016227 kidney 79.034821940268 68.7454249158683 105.752575413840 liver 81.7897330381782 85.8268465356046 91.7986452459763 stomach 91.8493590482522 75.363476531026 95.2918406867598 testicle 80.5749420478543 79.3541372415358 76.6965708193665 cont.diffExp=-23.1917582427486,-19.7494074763815,-26.2275969158159,4.15778989381754,10.2893970243997,-4.0371134974264,16.4858825172261,1.22080480631853 cont.diffExpScore=2.50546337004848 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=-1,0,-1,0,0,0,0,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=-1,-1,-1,0,0,0,1,0 cont.diffExp1.2Score=1.33333333333333 tran.correlation=-0.508195153395035 cont.tran.correlation=-0.603883572067834 tran.covariance=-0.0177872309552046 cont.tran.covariance=-0.00682810087476297 tran.mean=81.4212049022365 cont.tran.mean=81.8263409783191 weightedLogRatios: wLogRatio Lung -0.498098076973565 cerebhem 3.34414415157005 cortex 4.41219027535276 heart -0.507319352203311 kidney -0.850518566126775 liver -0.939335023381585 stomach 0.111919248750118 testicle -0.414132976827263 cont.weightedLogRatios: wLogRatio Lung -1.23054906302368 cerebhem -1.09519685550208 cortex -1.33471899178643 heart 0.223676038038927 kidney 0.599777745504806 liver -0.213353271624736 stomach 0.874640230799138 testicle 0.0668937291804632 varWeightedLogRatios=4.31897883817503 cont.varWeightedLogRatios=0.737720063634664 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.84802603417219 0.132391713970945 29.0654597538989 2.24655226454086e-106 *** df.mm.trans1 -0.088837275356731 0.116660359276726 -0.761503529626573 0.446744901758103 df.mm.trans2 0.610768440583148 0.110300466094781 5.53731513752911 5.16104783406594e-08 *** df.mm.exp2 -0.21610853710849 0.151970871200969 -1.42203920659706 0.155689612501492 df.mm.exp3 -0.377103734148452 0.151970871200969 -2.48142115109522 0.0134406183546080 * df.mm.exp4 -0.254409670308616 0.151970871200969 -1.67406864419550 0.0947939126855384 . df.mm.exp5 0.0653451158244377 0.151970871200969 0.429984478657256 0.667407417274514 df.mm.exp6 -0.00310273062663568 0.151970871200969 -0.0204166140663402 0.983719844938547 df.mm.exp7 -0.170447777874895 0.151970871200969 -1.12158189610884 0.262622856458313 df.mm.exp8 -0.161919463361201 0.151970871200969 -1.06546380948935 0.287222594696725 df.mm.trans1:exp2 0.907605612590568 0.140697687137803 6.45075005178754 2.81226052995417e-10 *** df.mm.trans2:exp2 0.0652959508964487 0.128438828390298 0.508381707578554 0.611428337283197 df.mm.trans1:exp3 1.24725267399070 0.140697687137803 8.86477026995556 1.67371284307827e-17 *** df.mm.trans2:exp3 0.187005429618285 0.128438828390298 1.45598828611248 0.146075059724627 df.mm.trans1:exp4 0.101528647743301 0.140697687137803 0.721608505503448 0.470900242480123 df.mm.trans2:exp4 0.107859028978470 0.128438828390298 0.839769642328947 0.401471903838720 df.mm.trans1:exp5 -0.0799488119891044 0.140697687137803 -0.568231174339068 0.570154074201026 df.mm.trans2:exp5 0.000323949568421374 0.128438828390298 0.00252220899615310 0.997988659203626 df.mm.trans1:exp6 -0.167738647026281 0.140697687137803 -1.19219192894048 0.233797992110195 df.mm.trans2:exp6 -0.0597705343090815 0.128438828390298 -0.465361877386888 0.641891608255596 df.mm.trans1:exp7 0.152459787650353 0.140697687137803 1.08359839277976 0.279108082209015 df.mm.trans2:exp7 0.0109767502605468 0.128438828390298 0.0854628650706064 0.931930439805886 df.mm.trans1:exp8 0.0911782764828865 0.140697687137803 0.648043889972295 0.517278489846445 df.mm.trans2:exp8 0.0735699636139988 0.128438828390298 0.5728015782769 0.567057708023845 df.mm.trans1:probe2 0.105280221768673 0.0703488435689013 1.49654516588548 0.135194462638815 df.mm.trans1:probe3 0.0721205880446481 0.0703488435689013 1.02518512580824 0.305812315061842 df.mm.trans1:probe4 0.23538394045066 0.0703488435689013 3.34595323120158 0.000886887403030631 *** df.mm.trans1:probe5 0.414631076345386 0.0703488435689013 5.89392881688647 7.28062541324436e-09 *** df.mm.trans1:probe6 0.131185827506303 0.0703488435689013 1.86479010671748 0.0628446471352117 . df.mm.trans1:probe7 1.29686667385028 0.0703488435689013 18.4347973336633 2.76911571198952e-57 *** df.mm.trans1:probe8 1.14746290195001 0.0703488435689013 16.3110414292192 1.44351149059761e-47 *** df.mm.trans1:probe9 0.997307823118686 0.0703488435689013 14.1766057908528 3.91484884219298e-38 *** df.mm.trans1:probe10 1.03931015721033 0.0703488435689013 14.7736637090900 9.96535698812633e-41 *** df.mm.trans1:probe11 1.21666832012087 0.0703488435689013 17.2947877804023 4.88360591551856e-52 *** df.mm.trans1:probe12 1.01507006188181 0.0703488435689013 14.4290937901151 3.16559963779385e-39 *** df.mm.trans2:probe2 -0.108099217949116 0.0703488435689013 -1.53661684350563 0.125071712206416 df.mm.trans2:probe3 -0.150721854785929 0.0703488435689014 -2.14249228757127 0.0326758483170377 * df.mm.trans2:probe4 -0.101766569276644 0.0703488435689013 -1.44659903580322 0.148687355409433 df.mm.trans2:probe5 -0.217246735893496 0.0703488435689013 -3.08813514014228 0.00213546628251259 ** df.mm.trans2:probe6 -0.080094112018355 0.0703488435689013 -1.13852777039482 0.255490198050637 df.mm.trans3:probe2 -0.455311138437215 0.0703488435689013 -6.4721908042635 2.46953739057030e-10 *** df.mm.trans3:probe3 -0.274111489766686 0.0703488435689013 -3.89646049402667 0.000112039222373471 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.24736386352985 0.313471011884255 13.5494629567156 1.87374789246365e-35 *** df.mm.trans1 -0.0716419057417741 0.276223033695913 -0.25936253317905 0.795471063228037 df.mm.trans2 0.266175303829367 0.261164371099721 1.01918689256328 0.30864765204469 df.mm.exp2 0.0210483516474057 0.359829639963377 0.0584953247585385 0.953379378331897 df.mm.exp3 -0.376026373585495 0.359829639963377 -1.04501222751902 0.296563773002527 df.mm.exp4 0.0596756587767013 0.359829639963376 0.165844200001910 0.86835210983065 df.mm.exp5 -0.462315579025391 0.359829639963376 -1.28481794627159 0.199499698313658 df.mm.exp6 -0.0646260975192827 0.359829639963377 -0.179601929195884 0.857543833009486 df.mm.exp7 -0.115983729607310 0.359829639963377 -0.322329560230545 0.747348848192704 df.mm.exp8 0.0217391602037850 0.359829639963377 0.0604151459173781 0.95185112094618 df.mm.trans1:exp2 -0.0637194889906304 0.333137513172014 -0.191270831026853 0.848397441103173 df.mm.trans2:exp2 -0.0911457765767211 0.304111551192478 -0.299711655868781 0.764531900025533 df.mm.trans1:exp3 0.419292657771224 0.333137513172015 1.25861736127784 0.208804205705773 df.mm.trans2:exp3 0.439269117259947 0.304111551192478 1.44443417403085 0.149294714146208 df.mm.trans1:exp4 0.0911416049425597 0.333137513172014 0.273585535518779 0.784525415640046 df.mm.trans2:exp4 -0.238099629254884 0.304111551192478 -0.782935170733408 0.434066318939363 df.mm.trans1:exp5 0.55240994640494 0.333137513172014 1.65820396852067 0.0979550568390497 . df.mm.trans2:exp5 0.134465541446122 0.304111551192478 0.442158612255462 0.658581340298675 df.mm.trans1:exp6 0.188983648245120 0.333137513172014 0.567284201787082 0.570796640930307 df.mm.trans2:exp6 -0.0413022734767337 0.304111551192478 -0.135812905872137 0.892028343279138 df.mm.trans1:exp7 0.356339390974956 0.333137513172014 1.06964654800362 0.285336956041284 df.mm.trans2:exp7 -0.119953734110070 0.304111551192478 -0.394439914037166 0.693438293063756 df.mm.trans1:exp8 0.0876543762403754 0.333137513172014 0.263117699972489 0.792577163024321 df.mm.trans2:exp8 -0.206078800674118 0.304111551192478 -0.67764213449323 0.498337831174745 df.mm.trans1:probe2 0.191788493611195 0.166568756586007 1.15140736799680 0.250160203245345 df.mm.trans1:probe3 0.116571789831724 0.166568756586007 0.699841868433065 0.484378295099103 df.mm.trans1:probe4 0.129808885720584 0.166568756586007 0.779311128816391 0.436195467048385 df.mm.trans1:probe5 0.0629294176101343 0.166568756586007 0.377798447319506 0.70575383407044 df.mm.trans1:probe6 0.197226422074901 0.166568756586007 1.18405411745427 0.237000462468369 df.mm.trans1:probe7 0.273595237354121 0.166568756586007 1.64253634932342 0.101159313094424 df.mm.trans1:probe8 0.110576185784970 0.166568756586007 0.663847098647666 0.507119316826195 df.mm.trans1:probe9 0.218148437105005 0.166568756586007 1.30965999612517 0.190961762651411 df.mm.trans1:probe10 0.131552003825888 0.166568756586007 0.789775985137771 0.430063759646816 df.mm.trans1:probe11 0.0154045444085991 0.166568756586007 0.0924815957345817 0.926355501204257 df.mm.trans1:probe12 0.113481796222879 0.166568756586007 0.681291008883068 0.49602871600116 df.mm.trans2:probe2 0.0366865157825996 0.166568756586007 0.220248481975410 0.8257749302315 df.mm.trans2:probe3 0.0767485593281026 0.166568756586007 0.460762035456954 0.645186154489501 df.mm.trans2:probe4 0.266631886475978 0.166568756586007 1.60073168546650 0.110119896744986 df.mm.trans2:probe5 0.08414511653789 0.166568756586007 0.505167465150897 0.613682112006967 df.mm.trans2:probe6 -0.0617225572574603 0.166568756586007 -0.370553028806396 0.711140304549034 df.mm.trans3:probe2 0.0379458487996332 0.166568756586007 0.227808921537095 0.819895574378194 df.mm.trans3:probe3 -0.0540108458539385 0.166568756586007 -0.324255562453275 0.745891311783691