fitVsDatCorrelation=0.82112411379947 cont.fitVsDatCorrelation=0.270860187949940 fstatistic=11667.2080105620,54,738 cont.fstatistic=4092.70373604831,54,738 residuals=-0.77030249430962,-0.082290841584285,-0.00506381522556492,0.0723183834946897,0.98922580334384 cont.residuals=-0.518676329829419,-0.134451878701597,-0.0453948224749897,0.0602701038952895,1.12792703809303 predictedValues: Include Exclude Both Lung 46.4973128252066 45.3009369389872 73.0214517428458 cerebhem 48.9116014820937 59.9971895031383 69.091893944434 cortex 47.6123895707312 44.3715065484107 69.1829988850188 heart 48.2105181510882 46.0375530573936 67.4600320272322 kidney 47.0696104003789 45.5690832910478 69.9935853272956 liver 51.2946443112115 50.824444637912 61.6664991395598 stomach 48.0209747390414 45.9451908957472 73.994606679747 testicle 48.5435433626017 51.0095802601313 70.4530364029255 diffExp=1.1963758862194,-11.0855880210446,3.24088302232059,2.17296509369457,1.50052710933108,0.470199673299526,2.07578384329413,-2.46603689752958 diffExpScore=6.21541500316683 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,-1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 51.8844355712592 52.1714353816945 52.1574996485227 cerebhem 50.9551168166066 46.4331551636411 49.8579842697745 cortex 53.8392528843438 50.2001280421239 50.9115268705276 heart 55.0364952721528 50.9644491492058 52.6574634101043 kidney 51.3367366426764 49.8956351211669 55.5258204960237 liver 50.109225066911 50.9439300935435 44.1216797350182 stomach 52.4632497521009 44.9540292900823 49.5435207188769 testicle 49.3634676227859 49.1942327968592 45.603198568159 cont.diffExp=-0.286999810435319,4.52196165296543,3.63912484221991,4.07204612294698,1.44110152150949,-0.834705026632534,7.5092204620185,0.169234825926750 cont.diffExpScore=1.05856580361756 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=0.522342632843012 cont.tran.correlation=0.129664570120362 tran.covariance=0.00166007663761425 cont.tran.covariance=0.000217541284201889 tran.mean=48.4510049984451 cont.tran.mean=50.6090609166971 weightedLogRatios: wLogRatio Lung 0.0997410166539691 cerebhem -0.815530045952137 cortex 0.269845840826778 heart 0.177677258804010 kidney 0.124260557972757 liver 0.0362184952426273 stomach 0.170106345155789 testicle -0.193612466892008 cont.weightedLogRatios: wLogRatio Lung -0.0217990991376558 cerebhem 0.360990223382692 cortex 0.27651236228407 heart 0.305133123312947 kidney 0.111733221514881 liver -0.0648010647927212 stomach 0.599797755721101 testicle 0.0133848959882060 varWeightedLogRatios=0.122920457514776 cont.varWeightedLogRatios=0.0520783603098782 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.78337886760811 0.0691171134640671 54.7386700339421 4.80937879663816e-262 *** df.mm.trans1 0.0444525115704 0.0608860227747017 0.730093863001841 0.465564494352666 df.mm.trans2 0.0399934756905103 0.0549393176307904 0.727957270224563 0.466870637663954 df.mm.exp2 0.386905942133577 0.0731628180631617 5.28828648726405 1.62835628556122e-07 *** df.mm.exp3 0.0569663999065955 0.0731628180631617 0.778625009460628 0.436450216043008 df.mm.exp4 0.131530366010862 0.0731628180631617 1.79777610394000 0.0726211316789223 . df.mm.exp5 0.060484487254218 0.0731628180631617 0.826710737167089 0.408668236919971 df.mm.exp6 0.382253980089971 0.0731628180631617 5.22470279589244 2.27197230310157e-07 *** df.mm.exp7 0.0331258851682711 0.0731628180631617 0.452769399063789 0.650847872372988 df.mm.exp8 0.197559342242186 0.0731628180631617 2.70026971995027 0.00708709271573409 ** df.mm.trans1:exp2 -0.336285846887548 0.0690102588057907 -4.87298341879758 1.34612048844231e-06 *** df.mm.trans2:exp2 -0.105935937877890 0.0564629390229196 -1.87620304063322 0.0610218562695632 . df.mm.trans1:exp3 -0.0332679096563598 0.0690102588057907 -0.482071944549326 0.629897771806968 df.mm.trans2:exp3 -0.0776965961346114 0.0564629390229196 -1.37606361764258 0.169219353313283 df.mm.trans1:exp4 -0.0953476720765894 0.0690102588057907 -1.38164489927386 0.167498868849143 df.mm.trans2:exp4 -0.115400647013214 0.0564629390229196 -2.0438299707773 0.041324860782857 * df.mm.trans1:exp5 -0.0482514311198213 0.0690102588057906 -0.699192148454492 0.484652253772162 df.mm.trans2:exp5 -0.0545827139367501 0.0564629390229196 -0.966699836765383 0.334010785576475 df.mm.trans1:exp6 -0.284062154973077 0.0690102588057906 -4.11623083130999 4.28571704768171e-05 *** df.mm.trans2:exp6 -0.267204262837342 0.0564629390229196 -4.73238317843988 2.65991494657669e-06 *** df.mm.trans1:exp7 -0.00088251819381345 0.0690102588057906 -0.0127882174199207 0.989800213037639 df.mm.trans2:exp7 -0.0190044166036689 0.0564629390229196 -0.336582135690007 0.73652760935675 df.mm.trans1:exp8 -0.154492668008755 0.0690102588057906 -2.23869132911862 0.0254733368177635 * df.mm.trans2:exp8 -0.0788735941762128 0.0564629390229196 -1.39690911491866 0.162860797219964 df.mm.trans1:probe2 -0.0241816498936747 0.0402933102124661 -0.600140563437584 0.548596790628769 df.mm.trans1:probe3 -0.112731355687682 0.0402933102124661 -2.79776854006908 0.00527979667586322 ** df.mm.trans1:probe4 -0.0651818471832504 0.0402933102124661 -1.61768409791966 0.106157993186366 df.mm.trans1:probe5 0.0659205513280668 0.0402933102124661 1.63601726888331 0.102262342622891 df.mm.trans1:probe6 -0.0389998576151113 0.0402933102124661 -0.967899073306847 0.333411859014209 df.mm.trans1:probe7 -0.108973629442715 0.0402933102124661 -2.7045092316342 0.0069981806126566 ** df.mm.trans1:probe8 0.186943277884341 0.0402933102124661 4.63956118022053 4.1306472627655e-06 *** df.mm.trans1:probe9 0.0267190479628402 0.0402933102124661 0.663113748211577 0.507464767235732 df.mm.trans1:probe10 0.0391991074933226 0.0402933102124661 0.97284405988553 0.330949558325728 df.mm.trans1:probe11 0.050989048575357 0.0402933102124661 1.26544700116452 0.206110510315684 df.mm.trans1:probe12 0.0478553744329319 0.0402933102124661 1.18767542752361 0.235343412205439 df.mm.trans1:probe13 -0.0688354798554574 0.0402933102124661 -1.70836001044563 0.0879900590970212 . df.mm.trans1:probe14 -0.0243975555106799 0.0402933102124661 -0.60549891240089 0.545033701887157 df.mm.trans1:probe15 0.158324508293782 0.0402933102124661 3.92930011108393 9.31953780587156e-05 *** df.mm.trans1:probe16 0.0615252498133398 0.0402933102124661 1.52693460747995 0.127205717179346 df.mm.trans1:probe17 -0.033468227899762 0.0402933102124661 -0.830615000934014 0.406459783480813 df.mm.trans1:probe18 0.0143208337357929 0.0402933102124661 0.355414674552161 0.722380513738434 df.mm.trans1:probe19 -0.029901302379442 0.0402933102124661 -0.74209098785314 0.458268316753214 df.mm.trans1:probe20 -0.00950979616219703 0.0402933102124661 -0.236014269169051 0.813487081113352 df.mm.trans1:probe21 0.0147870107620201 0.0402933102124661 0.3669842632449 0.713735956632161 df.mm.trans1:probe22 0.161801553421024 0.0402933102124661 4.01559347117044 6.53626085387984e-05 *** df.mm.trans2:probe2 -0.0456207629793589 0.0402933102124661 -1.13221680568812 0.257911028520121 df.mm.trans2:probe3 -0.0389729801702408 0.0402933102124661 -0.967232028461718 0.333744910936428 df.mm.trans2:probe4 -0.00210405281111255 0.0402933102124661 -0.0522184154148148 0.958368790284845 df.mm.trans2:probe5 -0.0122343284570361 0.0402933102124661 -0.303631754068469 0.761494036631361 df.mm.trans2:probe6 -0.0115587840626364 0.0402933102124661 -0.286866082773719 0.774295380695564 df.mm.trans3:probe2 0.357153731906599 0.0402933102124661 8.8638468773931 5.73391357432236e-18 *** df.mm.trans3:probe3 0.904797456395853 0.0402933102124661 22.4552773555925 1.63578314733929e-85 *** df.mm.trans3:probe4 0.585213944255025 0.0402933102124661 14.5238487771096 3.18861474214005e-42 *** df.mm.trans3:probe5 0.26747688045335 0.0402933102124661 6.63824538224702 6.15069892145802e-11 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.89513037540591 0.116571991011544 33.4139473951354 8.22921534112792e-150 *** df.mm.trans1 0.0991771981790399 0.102689544512172 0.965796456203818 0.334462413628421 df.mm.trans2 0.0228801568171358 0.0926599118518798 0.246926166449527 0.805034037046408 df.mm.exp2 -0.089505714110261 0.123395421802041 -0.725356847143421 0.468463074532874 df.mm.exp3 0.0226450244952441 0.123395421802041 0.183515921130143 0.854443635038796 df.mm.exp4 0.0260308435603776 0.123395421802041 0.210954694916786 0.832980858761276 df.mm.exp5 -0.117794012995216 0.123395421802041 -0.954606024072665 0.340089539939207 df.mm.exp6 0.108693474128282 0.123395421802041 0.880854998839869 0.378683073508035 df.mm.exp7 -0.0863841761636115 0.123395421802041 -0.700059815040745 0.484110566548933 df.mm.exp8 0.0257231652995608 0.123395421802041 0.20846126155983 0.834926345910402 df.mm.trans1:exp2 0.0714320443917371 0.116391771386622 0.613720742804567 0.539588994747509 df.mm.trans2:exp2 -0.0270156610887692 0.095229631134509 -0.283689653807546 0.776727790571261 df.mm.trans1:exp3 0.0143389316129244 0.116391771386622 0.123195406703575 0.901985904856471 df.mm.trans2:exp3 -0.0611625773626998 0.095229631134509 -0.642264142305765 0.520901148524755 df.mm.trans1:exp4 0.0329468194815577 0.116391771386622 0.28306828815344 0.777203870407443 df.mm.trans2:exp4 -0.0494376596137209 0.095229631134509 -0.519141563657762 0.603817668738298 df.mm.trans1:exp5 0.107181770235977 0.116391771386622 0.920870684921082 0.357418861099395 df.mm.trans2:exp5 0.0731924092032344 0.095229631134509 0.768588603476289 0.44238345055419 df.mm.trans1:exp6 -0.143507202456885 0.116391771386622 -1.23296690777386 0.21798070558543 df.mm.trans2:exp6 -0.132502986330697 0.095229631134509 -1.39140501493218 0.164521913434731 df.mm.trans1:exp7 0.0974782433805163 0.116391771386622 0.8375011585374 0.402582057663691 df.mm.trans2:exp7 -0.0625105577590157 0.095229631134509 -0.656419194470274 0.511759058809806 df.mm.trans1:exp8 -0.0755313890338592 0.116391771386622 -0.64894097008769 0.516578419182919 df.mm.trans2:exp8 -0.084481898449888 0.095229631134509 -0.88713877648607 0.375293198038081 df.mm.trans1:probe2 -0.0605696644664524 0.067958153350181 -0.891278845591102 0.3730700765276 df.mm.trans1:probe3 -0.0768862544351725 0.067958153350181 -1.13137645219678 0.258264180612972 df.mm.trans1:probe4 -0.0216425477680102 0.067958153350181 -0.318468744382863 0.750219503166104 df.mm.trans1:probe5 -0.0767527527579355 0.067958153350181 -1.12941198331916 0.259091043669279 df.mm.trans1:probe6 -0.0398484573931273 0.067958153350181 -0.586367572229229 0.557807829612597 df.mm.trans1:probe7 -0.107624247676507 0.067958153350181 -1.58368411104302 0.113694028395885 df.mm.trans1:probe8 -0.0893581794831571 0.067958153350181 -1.31490005360658 0.188951928103838 df.mm.trans1:probe9 -0.0712643361924435 0.067958153350181 -1.04865027490117 0.294682399813112 df.mm.trans1:probe10 -0.152828489185315 0.067958153350181 -2.24886171344013 0.0248154690899455 * df.mm.trans1:probe11 -0.0529932844900269 0.067958153350181 -0.779792885438753 0.435762798298744 df.mm.trans1:probe12 -0.0530651338137736 0.067958153350181 -0.780850143769132 0.435141030592 df.mm.trans1:probe13 -0.00314569592385276 0.067958153350181 -0.046288719878001 0.963092652358418 df.mm.trans1:probe14 -0.0756635494537115 0.067958153350181 -1.11338442443875 0.265905860703367 df.mm.trans1:probe15 -0.0769672419958812 0.067958153350181 -1.13256817911572 0.257763466077463 df.mm.trans1:probe16 -0.0312295053958359 0.067958153350181 -0.459540229630927 0.645981604723321 df.mm.trans1:probe17 -0.00773341843439101 0.067958153350181 -0.113796771294557 0.909429847712365 df.mm.trans1:probe18 -0.0513304543494187 0.067958153350181 -0.755324443336746 0.450295323727966 df.mm.trans1:probe19 -0.0539541741184131 0.067958153350181 -0.793932316559473 0.427490037617434 df.mm.trans1:probe20 0.00314148045830079 0.067958153350181 0.0462266895645778 0.963142075647325 df.mm.trans1:probe21 -0.0744983340435535 0.067958153350181 -1.09623835214697 0.273332069827761 df.mm.trans1:probe22 -0.0485812272658492 0.067958153350181 -0.714869737785772 0.474915607644971 df.mm.trans2:probe2 0.170281300366072 0.067958153350181 2.50567874451548 0.0124358969195418 * df.mm.trans2:probe3 0.0418277199648126 0.067958153350181 0.61549229787453 0.538419409239025 df.mm.trans2:probe4 0.0456876673135106 0.067958153350181 0.672291183047411 0.50160876157981 df.mm.trans2:probe5 0.00273558296446783 0.067958153350181 0.0402539331869667 0.967901567011176 df.mm.trans2:probe6 0.141238307157438 0.067958153350181 2.07831290573262 0.0380255736509494 * df.mm.trans3:probe2 -0.0927654718356685 0.067958153350181 -1.36503814866272 0.172657043097615 df.mm.trans3:probe3 -0.00232079824975216 0.067958153350181 -0.0341504019067931 0.97276645118005 df.mm.trans3:probe4 -0.117905590271997 0.067958153350181 -1.73497342790412 0.0831629911118315 . df.mm.trans3:probe5 -0.101606890237421 0.067958153350181 -1.49513907056673 0.135305615677159