fitVsDatCorrelation=0.90652846889864 cont.fitVsDatCorrelation=0.315220879743727 fstatistic=10384.0452894417,52,692 cont.fstatistic=2043.98571766744,52,692 residuals=-0.687184771198875,-0.0852891475367113,-0.00530128680495596,0.0795759129495786,0.748728826464484 cont.residuals=-0.653192186338308,-0.219605019972248,-0.0740926060134728,0.143019899942498,1.48833398696399 predictedValues: Include Exclude Both Lung 61.650245833515 46.9122031120003 77.8054006114945 cerebhem 61.0218221163237 47.2921589516490 97.8139627039382 cortex 59.2208877299265 53.6902934367789 121.940815810864 heart 59.8529472917417 56.0250758796967 143.867123549033 kidney 62.7771530971969 48.3888503658258 89.8724261623058 liver 60.8605170264336 48.4190332723411 76.2097723921553 stomach 61.529970845963 45.7193990577112 78.6217489480523 testicle 60.1698784984205 48.5643544355628 90.3930557525835 diffExp=14.7380427215146,13.7296631646747,5.53059429314756,3.82787141204509,14.3883027313711,12.4414837540925,15.8105717882519,11.6055240628576 diffExpScore=0.989255636275373 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=1,0,0,0,0,0,1,0 diffExp1.3Score=0.666666666666667 diffExp1.2=1,1,0,0,1,1,1,1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 59.5746028752232 60.2970097035566 54.4388029072142 cerebhem 62.7388533956959 65.1834943833953 59.4464725327184 cortex 58.7259200035284 54.4120125847924 69.7741133856118 heart 59.8288954766467 59.7924901027486 56.6218381834221 kidney 59.0953797062398 57.412356532819 71.967077283463 liver 64.6214894325066 52.5026060861401 53.7792310086728 stomach 56.291151046031 59.287298980127 50.1437126203778 testicle 58.6170040862453 62.8840977802126 52.9288124072791 cont.diffExp=-0.722406828333398,-2.44464098769939,4.31390741873604,0.0364053738981767,1.68302317342086,12.1188833463665,-2.99614793409604,-4.26709369396726 cont.diffExpScore=3.27708536849370 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=-0.702280606592563 cont.tran.correlation=-0.171096092948541 tran.covariance=-0.000920719291408508 cont.tran.covariance=-0.00058378154070154 tran.mean=55.1309244344429 cont.tran.mean=59.4540413859943 weightedLogRatios: wLogRatio Lung 1.08866607471007 cerebhem 1.01541590067816 cortex 0.395330510414399 heart 0.268254349688798 kidney 1.04374203687811 liver 0.913449080271552 stomach 1.17940071087151 testicle 0.854992012121617 cont.weightedLogRatios: wLogRatio Lung -0.0493366489792075 cerebhem -0.158944608964525 cortex 0.307834993791706 heart 0.00249020861832187 kidney 0.117442339331346 liver 0.844174991760456 stomach -0.210359222846076 testicle -0.288533587915665 varWeightedLogRatios=0.111337172792835 cont.varWeightedLogRatios=0.133788914944687 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.52645844015582 0.0811824830375168 43.4386619897563 7.39099436071595e-200 *** df.mm.trans1 0.823214494258806 0.0729137418144912 11.2902516558984 2.99757232389165e-27 *** df.mm.trans2 0.295816837322380 0.0670540881926419 4.41161523921582 1.18962133366226e-05 *** df.mm.exp2 -0.231035493596585 0.091847494831024 -2.51542509702232 0.0121142886921927 * df.mm.exp3 -0.354573475665340 0.091847494831024 -3.86045886518370 0.000123773948020666 *** df.mm.exp4 -0.466744278624915 0.091847494831024 -5.08173118367143 4.81748110417757e-07 *** df.mm.exp5 -0.0950747594083138 0.091847494831024 -1.03513720851317 0.300966433493342 df.mm.exp6 0.0394437168487908 0.091847494831024 0.42944793346141 0.66773095298059 df.mm.exp7 -0.0381454912161611 0.091847494831024 -0.415313354886152 0.678041265546377 df.mm.exp8 -0.139649971339565 0.091847494831024 -1.52045487573162 0.128853458015907 df.mm.trans1:exp2 0.220789816238646 0.0879372813307325 2.51076463699457 0.0122738900396634 * df.mm.trans2:exp2 0.239102166766957 0.0765395790258534 3.12390229747924 0.00185919568731484 ** df.mm.trans1:exp3 0.314370571430507 0.0879372813307326 3.5749407608834 0.000374665520182651 *** df.mm.trans2:exp3 0.4895278695814 0.0765395790258534 6.3957481320357 2.9406369981345e-10 *** df.mm.trans1:exp4 0.437157736887495 0.0879372813307325 4.97124462198624 8.3962584453982e-07 *** df.mm.trans2:exp4 0.644265816807641 0.0765395790258534 8.41742043799355 2.20820130123296e-16 *** df.mm.trans1:exp5 0.113188745335351 0.0879372813307326 1.28715311211007 0.198471283926893 df.mm.trans2:exp5 0.126066346357224 0.0765395790258534 1.64707394477101 0.0999968399688227 . df.mm.trans1:exp6 -0.0523362942449302 0.0879372813307326 -0.595154790470417 0.551934573354656 df.mm.trans2:exp6 -0.007828566893254 0.0765395790258534 -0.102281290188566 0.918563036190095 df.mm.trans1:exp7 0.0361926609806417 0.0879372813307325 0.411573571902012 0.680779446430696 df.mm.trans2:exp7 0.0123903501935239 0.0765395790258534 0.161881608851529 0.87144633944306 df.mm.trans1:exp8 0.115344624095269 0.0879372813307326 1.31166920730079 0.19006676416771 df.mm.trans2:exp8 0.174261949420346 0.0765395790258534 2.2767560475017 0.0231052854336841 * df.mm.trans1:probe2 -0.548034057878752 0.0439686406653663 -12.4642028860909 2.58938088320151e-32 *** df.mm.trans1:probe3 -0.391707062395378 0.0439686406653663 -8.9087826338903 4.51591588400708e-18 *** df.mm.trans1:probe4 -0.0305735923177458 0.0439686406653663 -0.695349955219979 0.487069444922402 df.mm.trans1:probe5 -0.102462695843851 0.0439686406653663 -2.33035850763882 0.0200737603955377 * df.mm.trans1:probe6 -0.232426715011171 0.0439686406653663 -5.28619287505633 1.67613840869559e-07 *** df.mm.trans1:probe7 0.479425715573078 0.0439686406653663 10.9038102683651 1.17589856126156e-25 *** df.mm.trans1:probe8 -0.421720286012863 0.0439686406653663 -9.5913878535037 1.53738137134768e-20 *** df.mm.trans1:probe9 -0.521528549794073 0.0439686406653663 -11.8613753325533 1.13208837062274e-29 *** df.mm.trans1:probe10 -0.437215765978385 0.0439686406653663 -9.94380902757306 7.23647623857536e-22 *** df.mm.trans1:probe11 -0.452051307381441 0.0439686406653663 -10.2812209006388 3.59874793798874e-23 *** df.mm.trans1:probe12 -0.133550946546875 0.0439686406653663 -3.03741358672641 0.00247591185394878 ** df.mm.trans1:probe13 -0.205927208356482 0.0439686406653663 -4.68350181493533 3.39521885709272e-06 *** df.mm.trans1:probe14 -0.148555364488054 0.0439686406653663 -3.37866630034505 0.000769212858692706 *** df.mm.trans1:probe15 -0.123272198983755 0.0439686406653663 -2.80363907362857 0.00519451323405601 ** df.mm.trans1:probe16 -0.0137460852418831 0.0439686406653663 -0.312633846165518 0.754653049789265 df.mm.trans1:probe17 -0.424087332909122 0.0439686406653663 -9.64522274265286 9.69008881326291e-21 *** df.mm.trans1:probe18 -0.292349267255772 0.0439686406653663 -6.64904038041033 5.98885965679114e-11 *** df.mm.trans1:probe19 -0.326276444808664 0.0439686406653663 -7.42066254201189 3.42678983246682e-13 *** df.mm.trans1:probe20 -0.463237311771686 0.0439686406653663 -10.5356295933109 3.57137064643519e-24 *** df.mm.trans1:probe21 -0.478549585290202 0.0439686406653663 -10.8838840147986 1.41745113814443e-25 *** df.mm.trans1:probe22 -0.437046868433632 0.0439686406653663 -9.93996770925625 7.4848570992188e-22 *** df.mm.trans2:probe2 -0.00965580694215439 0.0439686406653663 -0.219606674121272 0.826242255229136 df.mm.trans2:probe3 0.0264107360590287 0.0439686406653663 0.60067210765131 0.548255076738015 df.mm.trans2:probe4 0.0805705264317207 0.0439686406653663 1.83245434046783 0.067313310016329 . df.mm.trans2:probe5 0.02978731203536 0.0439686406653663 0.677467203547714 0.498335998528738 df.mm.trans2:probe6 0.106910258151873 0.0439686406653663 2.43151156219586 0.0152887419555273 * df.mm.trans3:probe2 -1.03813050913352 0.0439686406653663 -23.6107028423839 7.35100894300194e-91 *** df.mm.trans3:probe3 0.164293730048434 0.0439686406653663 3.73661153863797 0.000201901013364226 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.10100867912299 0.1825053529842 22.4706213383123 2.15458605578595e-84 *** df.mm.trans1 -0.031830373115701 0.163916496383868 -0.194186514584591 0.846086831786293 df.mm.trans2 -0.0340496291985089 0.150743480326616 -0.225877955880636 0.821363039233043 df.mm.exp2 0.0416762431566512 0.206481236316756 0.201840341040564 0.84010086232425 df.mm.exp3 -0.365231607649302 0.206481236316756 -1.76883679197374 0.0773615747689194 . df.mm.exp4 -0.0434606095690645 0.206481236316756 -0.210482125854734 0.8333533611051 df.mm.exp5 -0.336231140779755 0.206481236316756 -1.62838593364462 0.103898227984892 df.mm.exp6 -0.0449122154289192 0.206481236316756 -0.217512332985167 0.827873214570286 df.mm.exp7 0.00860463244723583 0.206481236316756 0.0416727088655928 0.96677163075172 df.mm.exp8 0.0539356114426785 0.206481236316756 0.261213136867980 0.794005868450891 df.mm.trans1:exp2 0.010075326948835 0.197690732892704 0.0509650948297275 0.95936804189686 df.mm.trans2:exp2 0.0362475279172054 0.172067696930630 0.210658528961533 0.833215751892775 df.mm.trans1:exp3 0.350883447373533 0.197690732892704 1.77491095429330 0.0763521172047972 . df.mm.trans2:exp3 0.262534044526479 0.17206769693063 1.52576020490540 0.127526486893083 df.mm.trans1:exp4 0.0477199987727274 0.197690732892704 0.241387130668523 0.809326593198615 df.mm.trans2:exp4 0.0350581668922411 0.17206769693063 0.203746359820083 0.838611612954769 df.mm.trans1:exp5 0.328154527577322 0.197690732892705 1.65993884880494 0.0973798036954431 . df.mm.trans2:exp5 0.28720817936343 0.172067696930630 1.66915803771826 0.0955383634035914 . df.mm.trans1:exp6 0.126229867655387 0.197690732892704 0.638521926689896 0.523345245925368 df.mm.trans2:exp6 -0.0935074886505678 0.17206769693063 -0.543434301257986 0.587005826550332 df.mm.trans1:exp7 -0.0652966416800409 0.197690732892704 -0.330296927552392 0.741275584821234 df.mm.trans2:exp7 -0.0254920440248662 0.172067696930630 -0.148151247907639 0.882266563784818 df.mm.trans1:exp8 -0.0701401418303393 0.197690732892704 -0.354797318033150 0.722849537627377 df.mm.trans2:exp8 -0.0119248093657635 0.172067696930630 -0.0693030102597994 0.944768450632608 df.mm.trans1:probe2 0.263118764553181 0.0988453664463522 2.66192310284961 0.00794994942471686 ** df.mm.trans1:probe3 -0.00939729490919232 0.0988453664463522 -0.0950706669117631 0.924286219342942 df.mm.trans1:probe4 -0.0744537409746888 0.0988453664463522 -0.75323450811524 0.451565186929064 df.mm.trans1:probe5 0.045830012364048 0.0988453664463522 0.463653623955373 0.64304172921982 df.mm.trans1:probe6 -0.0899308702033551 0.0988453664463522 -0.909813716479716 0.363237536375085 df.mm.trans1:probe7 0.0299288064397782 0.0988453664463522 0.302784111342457 0.762145386062509 df.mm.trans1:probe8 0.0616588712030658 0.0988453664463522 0.623791214700295 0.532970273657311 df.mm.trans1:probe9 0.119662365167717 0.0988453664463522 1.21060166469880 0.226461419614250 df.mm.trans1:probe10 0.0179133004356481 0.0988453664463522 0.181225494726355 0.856243649891073 df.mm.trans1:probe11 -0.0446802968306404 0.0988453664463522 -0.452022167927218 0.651394662283736 df.mm.trans1:probe12 -0.0669469616286506 0.0988453664463522 -0.677289831941548 0.498448438349554 df.mm.trans1:probe13 -0.125306615084523 0.0988453664463522 -1.26770348059292 0.205330250608429 df.mm.trans1:probe14 0.0464755015071875 0.0988453664463522 0.470183916333719 0.638371767835947 df.mm.trans1:probe15 0.165533056142763 0.0988453664463522 1.67466682651842 0.0944514429202854 . df.mm.trans1:probe16 0.103537261272259 0.0988453664463522 1.04746701838021 0.295249947693075 df.mm.trans1:probe17 -0.167616183136641 0.0988453664463522 -1.69574143091082 0.0903846626990251 . df.mm.trans1:probe18 0.178436466452214 0.0988453664463522 1.80520820416058 0.071476631409997 . df.mm.trans1:probe19 -0.072125636327004 0.0988453664463522 -0.729681510828834 0.465831801560092 df.mm.trans1:probe20 0.0909652341723233 0.0988453664463522 0.920278182404172 0.357748098427667 df.mm.trans1:probe21 -0.0199954786369372 0.0988453664463522 -0.202290500362398 0.839749082732239 df.mm.trans1:probe22 -0.00133028718410512 0.0988453664463522 -0.0134582654901393 0.989266060935466 df.mm.trans2:probe2 0.0464269166597835 0.0988453664463522 0.469692392561278 0.638722770850693 df.mm.trans2:probe3 0.133691855051233 0.0988453664463522 1.35253537780947 0.176645895090623 df.mm.trans2:probe4 -0.0508151821542443 0.0988453664463522 -0.514087650045022 0.607354834326971 df.mm.trans2:probe5 0.142184958110676 0.0988453664463522 1.43845850566850 0.150756103786778 df.mm.trans2:probe6 0.0194226126417115 0.0988453664463522 0.196494922726125 0.844280497942468 df.mm.trans3:probe2 -0.0700843499508388 0.0988453664463522 -0.709030200104288 0.47854453509013 df.mm.trans3:probe3 -0.044070115491668 0.0988453664463522 -0.445849077969545 0.655845725962422