fitVsDatCorrelation=0.825534208654447 cont.fitVsDatCorrelation=0.266552251339682 fstatistic=14305.6478671725,52,692 cont.fstatistic=4895.98935076981,52,692 residuals=-0.376537746869810,-0.0750026225014013,-0.0039440820804487,0.0714576327098345,0.660439187654802 cont.residuals=-0.508654713074019,-0.150314256112127,-0.0303459672077912,0.135722422648247,1.03993143583001 predictedValues: Include Exclude Both Lung 54.5218930025494 54.1316255214308 60.0959075758343 cerebhem 53.2768987238546 54.2147788906935 53.8316440570653 cortex 51.0327494874943 46.4170244891071 56.2136209972796 heart 52.9891528628117 44.9572457440679 57.0820838744959 kidney 52.7859671535801 51.4487612914927 59.7535982375609 liver 56.889004466583 50.7482053234636 53.4015892442316 stomach 52.8136666481597 47.9349605792451 51.2085411138255 testicle 51.7521679265425 52.5302436000738 55.439665102559 diffExp=0.39026748111862,-0.937880166838916,4.6157249983872,8.03190711874382,1.33720586208744,6.14079914311932,4.87870606891464,-0.778075673531298 diffExpScore=1.09854312146653 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 51.1731257433897 54.7221638596956 50.997839518285 cerebhem 53.820184675704 54.4019745598988 53.150863139464 cortex 52.8075919784345 55.8645279357917 55.9354212200825 heart 50.9516082210908 52.000271625272 57.312380037569 kidney 51.7633816344821 53.3236856040469 50.7997364301679 liver 55.8034114565857 60.5638154986276 57.4290695427598 stomach 53.2250298083459 58.5853433617153 51.4851153272678 testicle 56.172280036436 51.360136481924 53.7008409264029 cont.diffExp=-3.54903811630591,-0.581789884194748,-3.05693595735723,-1.04866340418128,-1.56030396956483,-4.76040404204184,-5.36031355336938,4.81214355451208 cont.diffExpScore=1.5354935475951 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=0.313626458746718 cont.tran.correlation=0.291317299358523 tran.covariance=0.00075431309084916 cont.tran.covariance=0.00059087694741174 tran.mean=51.7777716069469 cont.tran.mean=54.15865828009 weightedLogRatios: wLogRatio Lung 0.0286991051008187 cerebhem -0.0695276788251447 cortex 0.368309296757973 heart 0.639074606766724 kidney 0.101440681396985 liver 0.45507444987695 stomach 0.379781532564808 testicle -0.0590034695655264 cont.weightedLogRatios: wLogRatio Lung -0.266121235006757 cerebhem -0.0429109911389319 cortex -0.224805530172237 heart -0.0802897645902543 kidney -0.117648122413146 liver -0.332588637607812 stomach -0.385982769057327 testicle 0.356779359385504 varWeightedLogRatios=0.06997886343627 cont.varWeightedLogRatios=0.0544128632430125 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.78125900264003 0.0672179548290147 56.2537050146585 2.39418529160324e-260 *** df.mm.trans1 0.0466872721502356 0.0603715533243293 0.773332299393082 0.439589825749279 df.mm.trans2 0.216243772822353 0.0555198424905388 3.89489168416145 0.000107769518039268 *** df.mm.exp2 0.0885157160375023 0.0760484346833396 1.16393869783218 0.244849999493565 df.mm.exp3 -0.153104393697657 0.0760484346833397 -2.01324845587122 0.0444752361866873 * df.mm.exp4 -0.162770298841944 0.0760484346833397 -2.14035041641170 0.0326753933691306 * df.mm.exp5 -0.077476814888041 0.0760484346833397 -1.01878250631520 0.308662275431535 df.mm.exp6 0.0960586547559224 0.0760484346833397 1.26312468042115 0.206969793940418 df.mm.exp7 0.00662959049445583 0.0760484346833397 0.0871758968091977 0.930556917039271 df.mm.exp8 -0.00151909041517651 0.0760484346833397 -0.0199753015496229 0.984068833190996 df.mm.trans1:exp2 -0.111615226109882 0.0728108328682667 -1.53294807534783 0.12574567754128 df.mm.trans2:exp2 -0.08698076181285 0.0633736955694497 -1.37250575386644 0.170350718135112 df.mm.trans1:exp3 0.0869696396719995 0.0728108328682667 1.19446016816412 0.232707261755553 df.mm.trans2:exp3 -0.000647897583636726 0.0633736955694498 -0.0102234464601597 0.991845958566739 df.mm.trans1:exp4 0.134255201006375 0.0728108328682667 1.84389047230481 0.065626439680836 . df.mm.trans2:exp4 -0.0229363479023172 0.0633736955694498 -0.361922209147198 0.717520651201908 df.mm.trans1:exp5 0.0451198691942038 0.0728108328682667 0.619686211745951 0.535668375840809 df.mm.trans2:exp5 0.0266446105987882 0.0633736955694497 0.420436434381343 0.67429718024781 df.mm.trans1:exp6 -0.0535589028910991 0.0728108328682667 -0.735589757474698 0.462229637898533 df.mm.trans2:exp6 -0.160600990856155 0.0633736955694498 -2.53419008333128 0.0114901623843274 * df.mm.trans1:exp7 -0.0384619227172485 0.0728108328682667 -0.528244509808532 0.597498996217492 df.mm.trans2:exp7 -0.128203076657581 0.0633736955694497 -2.02296986952712 0.0434602823033753 * df.mm.trans1:exp8 -0.050616913492017 0.0728108328682667 -0.695183827708657 0.487173470854082 df.mm.trans2:exp8 -0.0285104275905957 0.0633736955694497 -0.449877939646929 0.652939342616025 df.mm.trans1:probe2 0.0350159814963114 0.0364054164341334 0.961834389661884 0.336468763834256 df.mm.trans1:probe3 -0.0268114690658137 0.0364054164341334 -0.736469231558509 0.461694771793992 df.mm.trans1:probe4 0.0941758549417324 0.0364054164341334 2.58686382868660 0.00988830219691513 ** df.mm.trans1:probe5 0.0401241654515605 0.0364054164341334 1.10214823456711 0.27078040455085 df.mm.trans1:probe6 0.465884129811606 0.0364054164341334 12.7971103051248 8.31866892249896e-34 *** df.mm.trans1:probe7 0.345665380681514 0.0364054164341334 9.49488879784992 3.49945285278313e-20 *** df.mm.trans1:probe8 0.160530010044186 0.0364054164341334 4.40950896234427 1.20092933836983e-05 *** df.mm.trans1:probe9 0.176040847543251 0.0364054164341334 4.83556747281694 1.63714553871408e-06 *** df.mm.trans1:probe10 0.105406845084008 0.0364054164341334 2.89536160847701 0.00390658138687384 ** df.mm.trans1:probe11 0.433564635056832 0.0364054164341334 11.9093441999561 7.02783280894894e-30 *** df.mm.trans1:probe12 0.40662115650786 0.0364054164341334 11.1692488738191 9.54600494190093e-27 *** df.mm.trans1:probe13 0.369974350314934 0.0364054164341334 10.1626182736932 1.0420297130986e-22 *** df.mm.trans1:probe14 0.589238627293289 0.0364054164341334 16.1854659281091 3.30019828078038e-50 *** df.mm.trans1:probe15 0.440473041651584 0.0364054164341334 12.0991073525697 1.05302462580778e-30 *** df.mm.trans1:probe16 0.500111734316371 0.0364054164341334 13.7372892086319 3.78412389840494e-38 *** df.mm.trans1:probe17 0.00781334086791704 0.0364054164341334 0.214620285474645 0.830126606279246 df.mm.trans1:probe18 0.0567580452134027 0.0364054164341334 1.55905496414503 0.119440565127622 df.mm.trans1:probe19 0.0282823856556764 0.0364054164341334 0.77687301577353 0.437499164097535 df.mm.trans1:probe20 0.0211124932884801 0.0364054164341334 0.579927256887118 0.562152438900085 df.mm.trans1:probe21 0.0196293782443312 0.0364054164341334 0.539188400161436 0.589930290684888 df.mm.trans1:probe22 -0.0032096172668366 0.0364054164341334 -0.0881631795819067 0.929772488619455 df.mm.trans2:probe2 0.0308563511929482 0.0364054164341334 0.847575833908538 0.39696728582957 df.mm.trans2:probe3 -0.0149228982361780 0.0364054164341334 -0.409908736057924 0.681999760250743 df.mm.trans2:probe4 -0.0294964277294446 0.0364054164341334 -0.810220857734482 0.41809178457073 df.mm.trans2:probe5 -0.06076226273086 0.0364054164341334 -1.66904457310066 0.0955608557785598 . df.mm.trans2:probe6 0.019567571153518 0.0364054164341334 0.537490655790758 0.591101531924029 df.mm.trans3:probe2 0.0518574749746022 0.0364054164341334 1.42444394417038 0.154768794728490 df.mm.trans3:probe3 0.103397619115226 0.0364054164341334 2.84017130534130 0.00464136994230803 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.01487431444899 0.114797203836938 34.9736246202639 4.10140820488523e-155 *** df.mm.trans1 -0.114190288561864 0.103104676876215 -1.10751802945814 0.268454833329699 df.mm.trans2 -0.00219583094035066 0.0948187532868813 -0.0231581924907514 0.981530763215323 df.mm.exp2 0.00321462883555534 0.129878210070965 0.0247511020809332 0.98026062945611 df.mm.exp3 -0.0403133028083202 0.129878210070965 -0.310393119725727 0.756355485963837 df.mm.exp4 -0.172091444622769 0.129878210070965 -1.32502168399717 0.185601302219869 df.mm.exp5 -0.0105276236134343 0.129878210070965 -0.0810576586148054 0.935419535607528 df.mm.exp6 0.0692819486008851 0.129878210070965 0.53343781503479 0.593901831873634 df.mm.exp7 0.0980205259877314 0.129878210070965 0.754711093833009 0.450679110060926 df.mm.exp8 -0.0218430006639961 0.129878210070965 -0.168180641325909 0.866490310691103 df.mm.trans1:exp2 0.0472194413964177 0.124348919028814 0.379733428848512 0.70425981037941 df.mm.trans2:exp2 -0.00908299532744312 0.108231841725804 -0.0839216554260813 0.93314299275366 df.mm.trans1:exp3 0.071753764235254 0.124348919028814 0.577035689539264 0.564103013774584 df.mm.trans2:exp3 0.0609741019311396 0.108231841725804 0.563365650615204 0.573368418434296 df.mm.trans1:exp4 0.167753262066798 0.124348919028814 1.3490528375878 0.177761248642148 df.mm.trans2:exp4 0.121071570001452 0.108231841725804 1.11863170829316 0.263685450268444 df.mm.trans1:exp5 0.0219960982218977 0.124348919028814 0.176890144230371 0.859646439032177 df.mm.trans2:exp5 -0.0153605778604533 0.108231841725804 -0.141922909335387 0.887182206399068 df.mm.trans1:exp6 0.0173385497131695 0.124348919028814 0.139434663755716 0.889147252252014 df.mm.trans2:exp6 0.0321468454017377 0.108231841725804 0.297018371757720 0.766541614989273 df.mm.trans1:exp7 -0.0587062615834742 0.124348919028814 -0.472109142901923 0.636997723003989 df.mm.trans2:exp7 -0.0298047907216414 0.108231841725804 -0.275379132853981 0.783107179435619 df.mm.trans1:exp8 0.115051891676831 0.124348919028814 0.92523435326503 0.355166553674664 df.mm.trans2:exp8 -0.0415634993515215 0.108231841725804 -0.384022841049115 0.701079485684424 df.mm.trans1:probe2 0.0528530638891095 0.0621744595144072 0.85007677271183 0.395576418500719 df.mm.trans1:probe3 0.0385756416949744 0.0621744595144072 0.620441930597493 0.535171145832906 df.mm.trans1:probe4 0.0582890013169951 0.0621744595144072 0.937507165679314 0.348824817986119 df.mm.trans1:probe5 0.0399875038159937 0.0621744595144072 0.643150002883864 0.520339826837081 df.mm.trans1:probe6 0.0336004664668393 0.0621744595144072 0.540422333049045 0.589079697135092 df.mm.trans1:probe7 0.0458636739141989 0.0621744595144072 0.737660998943968 0.460970532590585 df.mm.trans1:probe8 0.0637557342705724 0.0621744595144072 1.02543286694433 0.305517299905208 df.mm.trans1:probe9 0.109174399984088 0.0621744595144072 1.75593645424114 0.079541624762952 . df.mm.trans1:probe10 0.00483490806796397 0.0621744595144072 0.0777635721440187 0.938038602858502 df.mm.trans1:probe11 0.0776722239432075 0.0621744595144072 1.24926255169471 0.211991454965528 df.mm.trans1:probe12 0.113112259242460 0.0621744595144072 1.81927209542126 0.0693019403855775 . df.mm.trans1:probe13 0.069540429673402 0.0621744595144072 1.11847260461167 0.263753312906962 df.mm.trans1:probe14 0.0207002269360081 0.0621744595144072 0.332937786635867 0.739282086047863 df.mm.trans1:probe15 -0.00608976026139936 0.0621744595144072 -0.097946332126751 0.922003271738169 df.mm.trans1:probe16 -0.0114464134767333 0.0621744595144072 -0.184101535680917 0.853987736287478 df.mm.trans1:probe17 -0.0168900024807488 0.0621744595144072 -0.271654994875106 0.785968331686497 df.mm.trans1:probe18 0.043494573255548 0.0621744595144072 0.699556917667606 0.484439135940282 df.mm.trans1:probe19 0.0416144706132206 0.0621744595144072 0.669317770322999 0.503516035772483 df.mm.trans1:probe20 -0.00494343707071696 0.0621744595144072 -0.0795091281745917 0.936650658041026 df.mm.trans1:probe21 0.084369770547286 0.0621744595144072 1.35698438243336 0.175228626146368 df.mm.trans1:probe22 0.00519327895966556 0.0621744595144072 0.0835275288313872 0.933456245256152 df.mm.trans2:probe2 -0.0742156775397729 0.0621744595144072 -1.19366823804195 0.233016815016074 df.mm.trans2:probe3 0.00318211945613363 0.0621744595144072 0.0511804924560102 0.959196465776193 df.mm.trans2:probe4 -0.0451534671885142 0.0621744595144072 -0.72623819396534 0.46793832353776 df.mm.trans2:probe5 -0.0189231173425214 0.0621744595144072 -0.304355156286264 0.760948830009253 df.mm.trans2:probe6 0.0414231418051318 0.0621744595144072 0.666240480876768 0.505479443395248 df.mm.trans3:probe2 -0.0203312151968683 0.0621744595144072 -0.327002684955501 0.743764741773403 df.mm.trans3:probe3 0.120216438677875 0.0621744595144072 1.93353411701179 0.0535779639738908 .