fitVsDatCorrelation=0.91028816476315 cont.fitVsDatCorrelation=0.209510776482046 fstatistic=12245.3608641951,51,669 cont.fstatistic=2184.13249051168,51,669 residuals=-0.455924015085192,-0.0936592191588717,-0.00141080595694131,0.0775138217352617,0.68717643081873 cont.residuals=-0.650288600643209,-0.254276958895556,-0.0855569797536861,0.274789558969524,0.953824516587961 predictedValues: Include Exclude Both Lung 54.5204394784698 112.113169447977 101.068916046481 cerebhem 62.0058071475265 94.9734091780127 70.7923214932713 cortex 52.5266542295648 107.118983187068 107.985644583993 heart 53.6378190722464 111.722333394355 101.160639123424 kidney 53.3434656492475 98.9946575895308 73.0196678154907 liver 55.4933206878359 111.194649757332 85.2897157822348 stomach 56.9370617435102 124.706966294847 117.930838422660 testicle 55.4393166362628 109.038961743139 85.3915354570573 diffExp=-57.5927299695067,-32.9676020304861,-54.5923289575035,-58.084514322109,-45.6511919402834,-55.7013290694963,-67.7699045513368,-53.5996451068762 diffExpScore=0.997657856084647 diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.888888888888889 diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.888888888888889 diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.888888888888889 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 65.7315411142913 62.3184054990517 68.9432283926449 cerebhem 66.7154199511715 71.4929029125428 64.8365922171601 cortex 65.6588643018505 69.4428130013768 71.1556799296363 heart 62.151177129614 76.0608617858038 62.5103622075587 kidney 68.1191035690026 69.6318931352181 65.7802662390218 liver 68.1541343166096 68.2516458731516 62.7607224516218 stomach 63.042514456381 73.3180433785491 72.7449324190703 testicle 68.1411431861159 67.44784157177 80.4494576257297 cont.diffExp=3.41313561523961,-4.77748296137135,-3.78394869952626,-13.9096846561897,-1.51278956621557,-0.0975115565420026,-10.2755289221680,0.693301614345955 cont.diffExpScore=1.23080822231106 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,-1,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=-0.263937268124455 cont.tran.correlation=-0.594974477216231 tran.covariance=-0.00120885901839079 cont.tran.covariance=-0.00122571759654210 tran.mean=82.1104384523078 cont.tran.mean=67.8548940739063 weightedLogRatios: wLogRatio Lung -3.14257798409726 cerebhem -1.85061692216524 cortex -3.07682753487574 heart -3.19123136375329 kidney -2.65003903905151 liver -3.03290740019623 stomach -3.47631170497889 testicle -2.94477615769293 cont.weightedLogRatios: wLogRatio Lung 0.221762119632808 cerebhem -0.292902573276307 cortex -0.236029769233463 heart -0.85441947950238 kidney -0.0929611599915505 liver -0.00603700691738713 stomach -0.637104564563051 testicle 0.0431201616303227 varWeightedLogRatios=0.241000361653636 cont.varWeightedLogRatios=0.129603294598237 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.89905419829452 0.0764543913937848 50.9984335394433 1.08272028462010e-232 *** df.mm.trans1 -0.0778676080930154 0.0685910540977756 -1.13524437140179 0.256679497823411 df.mm.trans2 0.901325566557902 0.0631814404237921 14.2656697997422 1.66149565983783e-40 *** df.mm.exp2 0.318792602970003 0.0865735839064934 3.68233112902343 0.000249758080228171 *** df.mm.exp3 -0.149019197395951 0.0865735839064934 -1.72130100975032 0.0856585247573427 . df.mm.exp4 -0.0207205628534259 0.0865735839064933 -0.239340476834202 0.810914887029616 df.mm.exp5 0.178806698334282 0.0865735839064934 2.06537248737915 0.0392723876957588 * df.mm.exp6 0.179209201496344 0.0865735839064933 2.07002174808779 0.0388335049870334 * df.mm.exp7 -0.00446699322782212 0.0865735839064934 -0.0515976470680346 0.958864696277915 df.mm.exp8 0.157465465654216 0.0865735839064933 1.81886273559255 0.069379318647424 . df.mm.trans1:exp2 -0.190140226304156 0.0827239237881058 -2.29849138673830 0.0218413040373108 * df.mm.trans2:exp2 -0.484704456581564 0.0722073604843339 -6.71267379572372 4.08024631049327e-11 *** df.mm.trans1:exp3 0.111764270105583 0.0827239237881058 1.35105136443798 0.177135714401606 df.mm.trans2:exp3 0.103450603812271 0.0722073604843338 1.43268779136049 0.152414180491013 df.mm.trans1:exp4 0.00439929416907618 0.0827239237881057 0.0531804339980876 0.957604020801143 df.mm.trans2:exp4 0.0172283871632 0.0722073604843338 0.238595996968174 0.811491935561067 df.mm.trans1:exp5 -0.200630876552127 0.0827239237881058 -2.42530657837309 0.0155590798949248 * df.mm.trans2:exp5 -0.303249616040396 0.0722073604843338 -4.19970504400572 3.0345536797648e-05 *** df.mm.trans1:exp6 -0.161522203616135 0.0827239237881058 -1.95254523987363 0.0512901803314265 . df.mm.trans2:exp6 -0.187435737172916 0.0722073604843338 -2.5957982110921 0.0096442314137094 ** df.mm.trans1:exp7 0.0478378034053098 0.0827239237881058 0.578282571893526 0.563267966021891 df.mm.trans2:exp7 0.110924906144374 0.0722073604843338 1.53619943175239 0.124962262391127 df.mm.trans1:exp8 -0.140752104697134 0.0827239237881058 -1.70146794605229 0.0893197964475874 . df.mm.trans2:exp8 -0.185269002751834 0.0722073604843338 -2.56579109815307 0.0105107630925964 * df.mm.trans1:probe2 -0.0234535984612295 0.0413619618940529 -0.567033027139888 0.570881943794411 df.mm.trans1:probe3 0.0157129561645154 0.0413619618940529 0.379889044063324 0.704148347068081 df.mm.trans1:probe4 0.198173463832094 0.0413619618940529 4.79120077378601 2.04347172543511e-06 *** df.mm.trans1:probe5 0.0300748691899262 0.0413619618940529 0.727114184451933 0.467410368506491 df.mm.trans1:probe6 0.208347449979347 0.0413619618940529 5.03717523150911 6.0846082586001e-07 *** df.mm.trans1:probe7 0.00867711700270645 0.0413619618940529 0.209784947458067 0.833899391161076 df.mm.trans1:probe8 0.245117741720657 0.0413619618940529 5.9261633272744 4.96475990767756e-09 *** df.mm.trans1:probe9 0.121016279259736 0.0413619618940529 2.92578673056453 0.00355229208902175 ** df.mm.trans1:probe10 0.20580148336828 0.0413619618940529 4.97562189857997 8.27980258483077e-07 *** df.mm.trans1:probe11 0.178548285086139 0.0413619618940529 4.31672669549583 1.82316688375420e-05 *** df.mm.trans1:probe12 0.219457402080400 0.0413619618940529 5.30577835361223 1.52724589712217e-07 *** df.mm.trans1:probe13 0.298408792352625 0.0413619618940529 7.21457055438975 1.47059371441183e-12 *** df.mm.trans1:probe14 0.43223574168927 0.0413619618940529 10.4500783303371 8.82799557227887e-24 *** df.mm.trans1:probe15 0.141470196525105 0.0413619618940529 3.42029705668885 0.00066354672999103 *** df.mm.trans1:probe16 0.450086988413020 0.0413619618940529 10.8816644037800 1.6695490976898e-25 *** df.mm.trans1:probe17 0.123691870286764 0.0413619618940529 2.99047396744856 0.00288776303669085 ** df.mm.trans1:probe18 0.453073133234286 0.0413619618940529 10.9538598385351 8.50471231876478e-26 *** df.mm.trans1:probe19 0.322859009488557 0.0413619618940529 7.80569863478787 2.28632834974151e-14 *** df.mm.trans1:probe20 0.263009100682818 0.0413619618940529 6.35871918639899 3.76830343632802e-10 *** df.mm.trans1:probe21 0.365029577177631 0.0413619618940529 8.82524813771262 9.46743999745024e-18 *** df.mm.trans2:probe2 -0.254593054440419 0.0413619618940529 -6.15524609525414 1.29176862309666e-09 *** df.mm.trans2:probe3 -0.194163365293211 0.0413619618940529 -4.69424941182804 3.24714896276649e-06 *** df.mm.trans2:probe4 -0.141745889481902 0.0413619618940529 -3.42696243096445 0.00064774720957057 *** df.mm.trans2:probe5 0.000372290519085619 0.0413619618940529 0.00900079449904304 0.992821185311539 df.mm.trans2:probe6 -0.137708641192844 0.0413619618940529 -3.32935467484787 0.000918239223018708 *** df.mm.trans3:probe2 0.00358511534491548 0.0413619618940529 0.0866766270443993 0.930954490827478 df.mm.trans3:probe3 -0.323713721876072 0.0413619618940529 -7.82636284771144 1.96719176751508e-14 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.03459154882371 0.180584707631907 22.3418228582654 4.9969753522322e-83 *** df.mm.trans1 0.116144574250646 0.162011563032572 0.716890647041625 0.473691774431332 df.mm.trans2 0.075732076976748 0.149234095500507 0.50747167879267 0.61199115104598 df.mm.exp2 0.213611841346009 0.204486165586969 1.04462735037769 0.296572538754692 df.mm.exp3 0.0755537282593748 0.204486165586969 0.36948087926878 0.711886242078007 df.mm.exp4 0.241218937719026 0.204486165586969 1.17963450987805 0.238564921563064 df.mm.exp5 0.193608190383585 0.204486165586969 0.946803368471607 0.344080836231662 df.mm.exp6 0.221091616596919 0.204486165586969 1.08120574300117 0.279995192994441 df.mm.exp7 0.0671045071875974 0.204486165586969 0.328161599563357 0.742892160893208 df.mm.exp8 -0.0392456754787391 0.204486165586969 -0.191923377144297 0.847860463902387 df.mm.trans1:exp2 -0.198754619600685 0.195393296828384 -1.01720285612077 0.309424656118969 df.mm.trans2:exp2 -0.0762704720702576 0.170553252000580 -0.447194475482640 0.654879394906166 df.mm.trans1:exp3 -0.076660001120359 0.195393296828384 -0.392336903899474 0.6949342606425 df.mm.trans2:exp3 0.0326930355514953 0.170553252000580 0.191688139440368 0.848044661622856 df.mm.trans1:exp4 -0.297228067936227 0.195393296828384 -1.52117842710483 0.128687552132993 df.mm.trans2:exp4 -0.0419419204743934 0.170553252000580 -0.245916861639500 0.805822017683041 df.mm.trans1:exp5 -0.157929382394832 0.195393296828384 -0.808264075371755 0.419225893542235 df.mm.trans2:exp5 -0.0826423088324377 0.170553252000580 -0.484554283562748 0.628151150669381 df.mm.trans1:exp6 -0.184898683473662 0.195393296828384 -0.946289798447182 0.344342451938204 df.mm.trans2:exp6 -0.130146882998265 0.170553252000580 -0.763086493348266 0.445680807080468 df.mm.trans1:exp7 -0.108874063885415 0.195393296828384 -0.557204702784867 0.577573945581586 df.mm.trans2:exp7 0.095445413841037 0.170553252000580 0.55962236264315 0.575924357930468 df.mm.trans1:exp8 0.0752479764572726 0.195393296828384 0.385110327113032 0.700278082726077 df.mm.trans2:exp8 0.118343441668768 0.170553252000579 0.693879713700014 0.487998503298425 df.mm.trans1:probe2 0.072285733527296 0.0976966484141918 0.739899829734542 0.459620337833486 df.mm.trans1:probe3 0.129829850143197 0.0976966484141918 1.32890792315386 0.184331405159224 df.mm.trans1:probe4 0.00751786348458074 0.0976966484141918 0.0769510889739864 0.938685462158639 df.mm.trans1:probe5 0.116024948810456 0.0976966484141918 1.18760418800203 0.235410865681479 df.mm.trans1:probe6 0.0287076849128454 0.0976966484141918 0.293845135721925 0.768967382354782 df.mm.trans1:probe7 0.0164707725434995 0.0976966484141918 0.168590968173959 0.866169342369197 df.mm.trans1:probe8 0.13353917993054 0.0976966484141918 1.3668757536532 0.172123393674263 df.mm.trans1:probe9 0.0375373090367506 0.0976966484141918 0.384223099216347 0.700935191896708 df.mm.trans1:probe10 0.0287471747095154 0.0976966484141918 0.294249344027030 0.768658650972022 df.mm.trans1:probe11 0.104608294176971 0.0976966484141918 1.07074598642808 0.284669797769836 df.mm.trans1:probe12 0.0113165375015628 0.0976966484141918 0.115833426071952 0.907819318707755 df.mm.trans1:probe13 0.0324764598306307 0.0976966484141918 0.332421432646743 0.739675187636171 df.mm.trans1:probe14 0.110456394488177 0.0976966484141918 1.13060577083351 0.258626215676782 df.mm.trans1:probe15 0.0138797479827111 0.0976966484141918 0.142069847922182 0.887067607363436 df.mm.trans1:probe16 -0.0238430600448786 0.0976966484141918 -0.244051975496583 0.807265390530931 df.mm.trans1:probe17 -0.0250681837141755 0.0976966484141918 -0.256592054293379 0.797572596677832 df.mm.trans1:probe18 0.0865908187752855 0.0976966484141918 0.886323330235216 0.375761806552148 df.mm.trans1:probe19 0.0160827633259405 0.0976966484141918 0.164619396744876 0.869293288716557 df.mm.trans1:probe20 0.0122497875851184 0.0976966484141918 0.125385955239576 0.900255675230366 df.mm.trans1:probe21 -0.073183715594707 0.0976966484141918 -0.749091363753233 0.454065439901137 df.mm.trans2:probe2 0.0454931697017596 0.0976966484141918 0.465657424693713 0.641612286700177 df.mm.trans2:probe3 0.0343183204895492 0.0976966484141918 0.351274286749882 0.725493181722277 df.mm.trans2:probe4 0.0140058698432016 0.0976966484141918 0.143360801732141 0.88604840806171 df.mm.trans2:probe5 0.0654976272502536 0.0976966484141918 0.67041836453357 0.502822490697622 df.mm.trans2:probe6 0.038083720309703 0.0976966484141918 0.389816036966227 0.69679665256026 df.mm.trans3:probe2 -0.0196897919811119 0.0976966484141918 -0.201540096827433 0.84033754353063 df.mm.trans3:probe3 0.0401353588648914 0.0976966484141918 0.41081612845852 0.681338915343881