fitVsDatCorrelation=0.891880211426253 cont.fitVsDatCorrelation=0.276353088153665 fstatistic=3464.04408822267,43,485 cont.fstatistic=758.3765948577,43,485 residuals=-0.968974059097492,-0.119739478867341,0.000119769476553495,0.127362056402332,1.10091349504224 cont.residuals=-0.941026204843142,-0.311948596802907,-0.126983389688586,0.157156019947715,2.30221497246928 predictedValues: Include Exclude Both Lung 58.5563754467784 47.351146231846 62.4913354790811 cerebhem 58.1972930511804 94.801651204022 160.856709552309 cortex 55.7921903685039 125.804650096559 183.982908864421 heart 54.1594456382448 51.2180726913743 66.9037312082962 kidney 60.8930624306031 49.8865099066387 77.0589779695467 liver 56.8564211075353 51.4198372873349 65.4051001181102 stomach 56.3305385869485 56.8472824571886 66.4989404725796 testicle 52.6015055852464 59.7132287905261 69.9894916178477 diffExp=11.2052292149325,-36.6043581528415,-70.0124597280549,2.9413729468705,11.0065525239644,5.4365838202004,-0.51674387024012,-7.11172320527969 diffExpScore=1.71087459162715 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=1,-1,-1,0,1,0,0,0 diffExp1.2Score=4 cont.predictedValues: Include Exclude Both Lung 85.1324111975517 74.2046583222778 81.818199181941 cerebhem 80.607802000508 87.777773709192 69.9812614377149 cortex 83.8055582601772 73.5852380488455 66.179194432441 heart 91.7895429874911 75.778700177412 73.4829577523768 kidney 85.674326279709 101.781022612379 82.9555564452034 liver 88.6915536323494 78.0333133242077 69.481082038193 stomach 71.2404480036337 88.4204099842185 84.2793749788065 testicle 78.8717919780189 69.84889025935 100.046571436149 cont.diffExp=10.9277528752739,-7.169971708684,10.2203202113318,16.0108428100791,-16.1066963326703,10.6582403081418,-17.1799619805847,9.02290171866886 cont.diffExpScore=5.59709445665335 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,-1,0 cont.diffExp1.2Score=2 tran.correlation=-0.0947599343300651 cont.tran.correlation=-0.180319846266346 tran.covariance=-0.00181667053637496 cont.tran.covariance=-0.00189594019530135 tran.mean=61.9018256800331 cont.tran.mean=82.2027150485826 weightedLogRatios: wLogRatio Lung 0.841904924619146 cerebhem -2.10198858971154 cortex -3.60053895349316 heart 0.221350219420133 kidney 0.79935553918607 liver 0.401043246894686 stomach -0.0368533469810609 testicle -0.510551309089517 cont.weightedLogRatios: wLogRatio Lung 0.60111249049172 cerebhem -0.377680678010839 cortex 0.567490887985404 heart 0.847931717800293 kidney -0.78153742346003 liver 0.566034699250714 stomach -0.944985718913152 testicle 0.523264648215249 varWeightedLogRatios=2.45936579657801 cont.varWeightedLogRatios=0.502514994943674 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 2.88103987122171 0.130547682517472 22.0688702829797 3.07276207598599e-75 *** df.mm.trans1 1.15059086970895 0.104510246917294 11.0093594039586 2.5814387761507e-25 *** df.mm.trans2 0.814210615663752 0.104510246917294 7.79072521288836 4.08142932412511e-14 *** df.mm.exp2 -0.257441381763539 0.139946340573452 -1.83957208676292 0.066442072960797 . df.mm.exp3 -0.151031742093224 0.139946340573452 -1.07921179985377 0.281029731031589 df.mm.exp4 -0.067782978790105 0.139946340573452 -0.484349776581179 0.62835630223483 df.mm.exp5 -0.118254287063065 0.139946340573452 -0.844997350973953 0.398528961955967 df.mm.exp6 0.00739987539976221 0.139946340573452 0.0528765194533853 0.957852064772042 df.mm.exp7 0.0818661311914041 0.139946340573452 0.584982292898442 0.558831667785953 df.mm.exp8 0.0114001299968370 0.139946340573452 0.0814607223748984 0.935109165140182 df.mm.trans1:exp2 0.251290251226641 0.109782941759990 2.28897356181271 0.022509880052036 * df.mm.trans2:exp2 0.951637181655742 0.109782941759990 8.66835198984042 6.60492061491304e-17 *** df.mm.trans1:exp3 0.102675671087314 0.109782941759990 0.935260701173288 0.350119388883303 df.mm.trans2:exp3 1.12817102289696 0.109782941759990 10.2763781404527 1.51934911735466e-22 *** df.mm.trans1:exp4 -0.0102746013334041 0.109782941759990 -0.0935901440486693 0.92547338840202 df.mm.trans2:exp4 0.146284403824362 0.109782941759990 1.33248755662034 0.183325726985868 df.mm.trans1:exp5 0.157383564849648 0.109782941759990 1.43358851864003 0.152334065940662 df.mm.trans2:exp5 0.170413883738043 0.109782941759990 1.55228017218381 0.121247467413936 df.mm.trans1:exp6 -0.036860686384292 0.109782941759990 -0.335759689013233 0.737197287062608 df.mm.trans2:exp6 0.0750331350671707 0.109782941759990 0.68346806766401 0.494637500236647 df.mm.trans1:exp7 -0.120619290094182 0.109782941759990 -1.09870703189829 0.272440987403087 df.mm.trans2:exp7 0.100911258897191 0.109782941759990 0.919188876517866 0.358453705626776 df.mm.trans1:exp8 -0.118645360413662 0.109782941759990 -1.08072673688275 0.280355781883224 df.mm.trans2:exp8 0.220562426654750 0.109782941759990 2.00907739507427 0.0450825373610398 * df.mm.trans1:probe2 0.244663767599934 0.0751632420390278 3.25509864879025 0.00121275882861179 ** df.mm.trans1:probe3 0.131312398724484 0.0751632420390278 1.74702946762596 0.0812651891000293 . df.mm.trans1:probe4 0.0589980803163504 0.0751632420390278 0.784932617538187 0.43287631584604 df.mm.trans1:probe5 0.0371148621318133 0.0751632420390278 0.493790064464513 0.621677991335824 df.mm.trans1:probe6 0.141658605048992 0.0751632420390278 1.88467928213418 0.0600708395446073 . df.mm.trans2:probe2 1.14344537509657 0.0751632420390278 15.2128266966298 5.18911067951412e-43 *** df.mm.trans2:probe3 0.312857673590482 0.0751632420390278 4.162375984634 3.72695351681222e-05 *** df.mm.trans2:probe4 -0.000917319963775719 0.0751632420390278 -0.0122043693019443 0.990267582384107 df.mm.trans2:probe5 0.964208162910206 0.0751632420390278 12.8281875123155 1.21957987693420e-32 *** df.mm.trans2:probe6 0.177854749895587 0.0751632420390278 2.36624638680750 0.0183615851586681 * df.mm.trans3:probe2 -0.697439236607075 0.0751632420390278 -9.27899353044053 5.68248464090907e-19 *** df.mm.trans3:probe3 -0.309622580005642 0.0751632420390278 -4.11933508462652 4.46743185350981e-05 *** df.mm.trans3:probe4 -1.22266301073418 0.0751632420390278 -16.2667678717121 8.48112651484325e-48 *** df.mm.trans3:probe5 -0.837451697775044 0.0751632420390278 -11.1417718961644 7.93343590977959e-26 *** df.mm.trans3:probe6 -1.05833931744814 0.0751632420390278 -14.0805437436907 5.37487132945302e-38 *** df.mm.trans3:probe7 -0.805912734946806 0.0751632420390278 -10.7221656900902 3.24195559373678e-24 *** df.mm.trans3:probe8 -0.435724150413301 0.0751632420390278 -5.7970377353741 1.21720167104024e-08 *** df.mm.trans3:probe9 -0.796533869862306 0.0751632420390278 -10.5973857467286 9.61287259135975e-24 *** df.mm.trans3:probe10 -0.94444782017859 0.0751632420390278 -12.5652884915235 1.51652245928051e-31 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.26398391896288 0.277407710938464 15.3708197387085 1.00965978189136e-43 *** df.mm.trans1 0.191807892994334 0.222079379793356 0.863690691016924 0.388184749408353 df.mm.trans2 0.111434500958210 0.222079379793356 0.501777792525806 0.616051537425102 df.mm.exp2 0.269641203866393 0.297379419106119 0.906724495854141 0.365002764522542 df.mm.exp3 0.188042589274294 0.297379419106119 0.632332223391666 0.527467786870325 df.mm.exp4 0.203727071886992 0.297379419106119 0.685074550550161 0.49362415474851 df.mm.exp5 0.308536823296213 0.297379419106119 1.03751908663899 0.30001099091672 df.mm.exp6 0.254710926294379 0.297379419106119 0.85651833963495 0.392134119231037 df.mm.exp7 -0.0325086499596151 0.297379419106119 -0.109317080708987 0.912996213803599 df.mm.exp8 -0.338012995320758 0.297379419106119 -1.13663883108245 0.256250585335756 df.mm.trans1:exp2 -0.324253583231045 0.233283609378921 -1.38995441683330 0.165180333478645 df.mm.trans2:exp2 -0.101659810918926 0.233283609378921 -0.435777769340839 0.663191860618321 df.mm.trans1:exp3 -0.203751079505021 0.233283609378921 -0.873405037102583 0.382874577330561 df.mm.trans2:exp3 -0.19642508247123 0.233283609378921 -0.842001214719625 0.400202263797247 df.mm.trans1:exp4 -0.128436514766779 0.233283609378921 -0.550559531845035 0.582189145738959 df.mm.trans2:exp4 -0.182736747851045 0.233283609378921 -0.783324419308976 0.433818966101733 df.mm.trans1:exp5 -0.302191442446964 0.233283609378921 -1.29538223131706 0.195804626267501 df.mm.trans2:exp5 0.00745991626976542 0.233283609378921 0.031977884299828 0.974502842851944 df.mm.trans1:exp6 -0.213754088706237 0.233283609378921 -0.91628421420314 0.359973215426008 df.mm.trans2:exp6 -0.204402025709731 0.233283609378921 -0.876195401185352 0.38135756819802 df.mm.trans1:exp7 -0.145638426192040 0.233283609378921 -0.624297723186718 0.532725625155916 df.mm.trans2:exp7 0.207784546302039 0.233283609378921 0.890695007914317 0.373534437727173 df.mm.trans1:exp8 0.261628819920769 0.233283609378921 1.12150536686788 0.262627766218885 df.mm.trans2:exp8 0.277520264588656 0.233283609378921 1.1896260750059 0.234775433841663 df.mm.trans1:probe2 0.0344894330913381 0.159718368941324 0.21593905146883 0.829126042899577 df.mm.trans1:probe3 -0.0972124924487793 0.159718368941324 -0.608649418931223 0.543041793079291 df.mm.trans1:probe4 -0.0232024849146904 0.159718368941324 -0.145271236292266 0.884557075855324 df.mm.trans1:probe5 0.0130535050495569 0.159718368941324 0.0817282641694923 0.934896518184555 df.mm.trans1:probe6 -0.112471780792715 0.159718368941324 -0.704188137771646 0.48165358423159 df.mm.trans2:probe2 -0.268143610212669 0.159718368941324 -1.67885267042251 0.093824862394355 . df.mm.trans2:probe3 -0.274525849847747 0.159718368941324 -1.71881200432620 0.0862867274398572 . df.mm.trans2:probe4 -0.105907514138539 0.159718368941324 -0.663089128949509 0.507588492640636 df.mm.trans2:probe5 -0.252209247783760 0.159718368941324 -1.57908729882169 0.114967982907587 df.mm.trans2:probe6 -0.196677635532284 0.159718368941324 -1.23140272991729 0.218769033538995 df.mm.trans3:probe2 -0.306702431644145 0.159718368941324 -1.92027024616574 0.0554098915883094 . df.mm.trans3:probe3 -0.119592258904837 0.159718368941324 -0.748769597996408 0.454359056099511 df.mm.trans3:probe4 -0.129188098006731 0.159718368941324 -0.808849344399399 0.418998404561433 df.mm.trans3:probe5 -0.203667274647642 0.159718368941324 -1.27516500448652 0.202861333806592 df.mm.trans3:probe6 -0.290954205062604 0.159718368941324 -1.82167027494185 0.0691208456855374 . df.mm.trans3:probe7 -0.102914452228517 0.159718368941324 -0.644349506638935 0.519653438210081 df.mm.trans3:probe8 -0.0903671891781695 0.159718368941324 -0.565790834060972 0.571797643730208 df.mm.trans3:probe9 -0.0721909403466437 0.159718368941324 -0.451988965484395 0.651479002631936 df.mm.trans3:probe10 -0.311689251638555 0.159718368941324 -1.95149282893730 0.0515736976789776 .