fitVsDatCorrelation=0.926735655434946 cont.fitVsDatCorrelation=0.248173614390590 fstatistic=7598.8097002658,49,623 cont.fstatistic=1132.2550676084,49,623 residuals=-0.79344857894296,-0.0992026576523515,-0.00856079752222076,0.0928841519259488,0.902939529342897 cont.residuals=-0.702662706581375,-0.278622479986741,-0.115653304614894,0.132359986083497,2.350212303941 predictedValues: Include Exclude Both Lung 51.4625121709707 74.5679747647102 153.788128570565 cerebhem 57.0824719079935 58.6252550094395 129.191109769851 cortex 51.6223407190389 86.791646147448 204.418091022080 heart 51.8793203200041 92.9595832620235 213.295592827600 kidney 49.0927569545037 65.470117184543 115.116892988346 liver 50.9858981454822 55.3519412858884 91.7322890676841 stomach 49.9568587698352 63.6414993402319 156.348198607307 testicle 50.2634867395673 63.5729404732712 134.288837870455 diffExp=-23.1054625937395,-1.54278310144593,-35.169305428409,-41.0802629420194,-16.3773602300393,-4.36604314040619,-13.6846405703967,-13.3094537337038 diffExpScore=0.993317085463513 diffExp1.5=0,0,-1,-1,0,0,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=-1,0,-1,-1,0,0,0,0 diffExp1.4Score=0.75 diffExp1.3=-1,0,-1,-1,-1,0,0,0 diffExp1.3Score=0.8 diffExp1.2=-1,0,-1,-1,-1,0,-1,-1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 63.3378973956002 63.6620582480821 62.9274745824795 cerebhem 69.0860233782223 64.4299657564333 63.6598439393595 cortex 73.6383052545456 78.180897844969 64.0238715949631 heart 64.4933118325055 72.1749610309945 62.4691190569225 kidney 71.3401516821433 71.4429462545951 66.2701082927181 liver 60.1757512676674 69.4100852573405 67.0025746491106 stomach 61.607226236721 65.0501999275838 63.3807953318815 testicle 63.5351600447904 74.3876248637362 55.3470065472266 cont.diffExp=-0.324160852481917,4.65605762178897,-4.5425925904234,-7.681649198489,-0.102794572451742,-9.23433398967306,-3.44297369086274,-10.8524648189457 cont.diffExpScore=1.25556149760480 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=-0.0730046560296394 cont.tran.correlation=0.458035431348134 tran.covariance=-0.000702611488320816 cont.tran.covariance=0.00235427198837725 tran.mean=60.8329126996845 cont.tran.mean=67.8720353922456 weightedLogRatios: wLogRatio Lung -1.53026275993910 cerebhem -0.108216189397306 cortex -2.1840737308918 heart -2.47327332956446 kidney -1.16236875306913 liver -0.326401817751241 stomach -0.976222725332417 testicle -0.947795238131357 cont.weightedLogRatios: wLogRatio Lung -0.0211906591746385 cerebhem 0.293081659848271 cortex -0.259139657882607 heart -0.475201771051041 kidney -0.00614562567150692 liver -0.59512788586704 stomach -0.225567004932264 testicle -0.667124304768654 varWeightedLogRatios=0.681070032107264 cont.varWeightedLogRatios=0.107288741613230 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.46077536721297 0.0912138803811387 37.9413237629193 4.25334240243426e-164 *** df.mm.trans1 0.489002815987253 0.0805442675075578 6.07123053097935 2.20674148448897e-09 *** df.mm.trans2 0.733597399493751 0.0738994382837235 9.92696854713884 1.16566945682216e-21 *** df.mm.exp2 0.0373810158536875 0.099846410178107 0.374385176061983 0.708245162729208 df.mm.exp3 -0.129691328687997 0.099846410178107 -1.29890827779038 0.194455936209926 df.mm.exp4 -0.0985827742345668 0.099846410178107 -0.987344202547833 0.323857225435852 df.mm.exp5 0.112368367888671 0.099846410178107 1.12541219747638 0.260847771478428 df.mm.exp6 0.209397506085016 0.099846410178107 2.09719614066736 0.0363786791984174 * df.mm.exp7 -0.204648870305824 0.099846410178107 -2.04963673647124 0.0408180450648935 * df.mm.exp8 -0.0475150466582444 0.099846410178107 -0.47588137193402 0.63432571997398 df.mm.trans1:exp2 0.0662624582460502 0.093789371146773 0.706502852464535 0.480139616036278 df.mm.trans2:exp2 -0.277926561594489 0.0803164190524697 -3.46039533227849 0.000576237883609895 *** df.mm.trans1:exp3 0.132792243549084 0.093789371146773 1.41585599653157 0.157317402021533 df.mm.trans2:exp3 0.281490580842798 0.0803164190524697 3.50477006026507 0.000489788684278232 *** df.mm.trans1:exp4 0.106649408997024 0.0937893711467731 1.13711615392032 0.255926893913964 df.mm.trans2:exp4 0.319036462007474 0.0803164190524697 3.97224460168041 7.95166597629899e-05 *** df.mm.trans1:exp5 -0.159510483769068 0.0937893711467731 -1.70073092311757 0.0894925631271878 . df.mm.trans2:exp5 -0.24248567787945 0.0803164190524697 -3.01912959691389 0.00263867904342284 ** df.mm.trans1:exp6 -0.218702042144848 0.093789371146773 -2.33184250486760 0.0200267914402596 * df.mm.trans2:exp6 -0.507396896968599 0.0803164190524697 -6.3174740975083 5.06166109248818e-10 *** df.mm.trans1:exp7 0.174955055085540 0.0937893711467731 1.86540386129414 0.06259464497807 . df.mm.trans2:exp7 0.0462035108734319 0.0803164190524697 0.575268561752581 0.565317382524302 df.mm.trans1:exp8 0.0239403268159952 0.0937893711467731 0.255256288887261 0.798609402783672 df.mm.trans2:exp8 -0.112008160126989 0.0803164190524697 -1.39458608150614 0.163637757385129 df.mm.trans1:probe2 0.115725988771060 0.0513705542313118 2.2527689354872 0.0246208660203218 * df.mm.trans1:probe3 0.119436774684218 0.0513705542313118 2.32500459594844 0.0203920734677759 * df.mm.trans1:probe4 0.158138320508121 0.0513705542313118 3.07838455073026 0.00217273788177517 ** df.mm.trans1:probe5 -0.0475631188545524 0.0513705542313118 -0.92588292196313 0.354865339855243 df.mm.trans1:probe6 0.118033980222782 0.0513705542313118 2.29769723120560 0.0219095430054017 * df.mm.trans1:probe7 -0.0989280205545783 0.0513705542313118 -1.92577288750135 0.0545875085259911 . df.mm.trans1:probe8 -0.0103038609364042 0.0513705542313118 -0.200579127295530 0.841093141258735 df.mm.trans1:probe9 -0.00903446684571086 0.0513705542313118 -0.175868588161038 0.860454332055399 df.mm.trans1:probe10 0.0353596629405056 0.0513705542313118 0.688325510004968 0.491504038276134 df.mm.trans1:probe11 -0.00537480209114521 0.0513705542313118 -0.104628072863367 0.916704612721983 df.mm.trans1:probe12 0.0218553922708584 0.0513705542313118 0.425445911532270 0.670658563610884 df.mm.trans1:probe13 -0.127529217784470 0.0513705542313118 -2.48253536861273 0.0133073707290331 * df.mm.trans1:probe14 -0.0858356539475206 0.0513705542313118 -1.67091158022199 0.0952413661812393 . df.mm.trans1:probe15 -0.114572774157495 0.0513705542313118 -2.23031999307611 0.0260815925792749 * df.mm.trans1:probe16 -0.00356809752569179 0.0513705542313118 -0.069458030560179 0.944647326610353 df.mm.trans1:probe17 -0.164925968359873 0.0513705542313118 -3.2105156509942 0.00139311870365082 ** df.mm.trans1:probe18 -0.097254449594933 0.0513705542313118 -1.89319447785232 0.0587951650396341 . df.mm.trans2:probe2 0.148664632868841 0.0513705542313118 2.89396591283467 0.00393714609291441 ** df.mm.trans2:probe3 0.268175692640354 0.0513705542313118 5.22041657235796 2.43575581168264e-07 *** df.mm.trans2:probe4 0.209162867611781 0.0513705542313118 4.07164903594305 5.26982248464803e-05 *** df.mm.trans2:probe5 0.160543493170807 0.0513705542313118 3.12520461523367 0.00185943143768168 ** df.mm.trans2:probe6 0.386836870030675 0.0513705542313118 7.53032307747395 1.78481142531209e-13 *** df.mm.trans3:probe2 0.999993722757797 0.0513705542313118 19.4662825371713 2.72819281405760e-66 *** df.mm.trans3:probe3 0.106526723780857 0.0513705542313118 2.07369231994632 0.0385182058853471 * df.mm.trans3:probe4 0.302280689264943 0.0513705542313118 5.88431824005306 6.52894325455183e-09 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.27386550097113 0.235179688426040 18.1727662349344 1.56166321099142e-59 *** df.mm.trans1 -0.0428377562840474 0.207669881577015 -0.206278137006405 0.83664104809045 df.mm.trans2 -0.0290514091317024 0.190537304166914 -0.152470978104387 0.878864844346742 df.mm.exp2 0.0872875728986613 0.257437218305224 0.339063533522068 0.734676123717492 df.mm.exp3 0.338844947212193 0.257437218305224 1.31622361926879 0.188583054739815 df.mm.exp4 0.150892639877056 0.257437218305224 0.586133741152197 0.557997931199917 df.mm.exp5 0.182529810480051 0.257437218305224 0.709026502390337 0.478573267996778 df.mm.exp6 -0.0275191323863820 0.257437218305224 -0.106896479722503 0.91490551881079 df.mm.exp7 -0.0133121838199279 0.257437218305224 -0.0517104088816894 0.958776018557638 df.mm.exp8 0.287170742139015 0.257437218305224 1.11549815535428 0.265067098877977 df.mm.trans1:exp2 -0.000418974216536011 0.241820159298183 -0.00173258597526348 0.998618151717698 df.mm.trans2:exp2 -0.0752974944537965 0.207082412559667 -0.363611247923339 0.716271679603677 df.mm.trans1:exp3 -0.188163450074312 0.241820159298183 -0.778113167324036 0.436797705137265 df.mm.trans2:exp3 -0.133408355803422 0.207082412559667 -0.644228325111783 0.519664496017901 df.mm.trans1:exp4 -0.132814959008002 0.241820159298183 -0.549230301532598 0.583044212234326 df.mm.trans2:exp4 -0.0253882074728037 0.207082412559667 -0.122599534933893 0.902463760117598 df.mm.trans1:exp5 -0.063554349402024 0.241820159298183 -0.262816588933169 0.792778837038313 df.mm.trans2:exp5 -0.0672193870171438 0.207082412559667 -0.324602104960389 0.745591195843277 df.mm.trans1:exp6 -0.0236952443453113 0.241820159298183 -0.0979870512618978 0.921974091971403 df.mm.trans2:exp6 0.113962556831339 0.207082412559667 0.550324652985694 0.58229397384761 df.mm.trans1:exp7 -0.0143924885200218 0.241820159298183 -0.0595173229634456 0.952559154181237 df.mm.trans2:exp7 0.0348827089977849 0.207082412559668 0.168448438313099 0.866285175572614 df.mm.trans1:exp8 -0.284061133020652 0.241820159298183 -1.17467928995276 0.240571836962132 df.mm.trans2:exp8 -0.131469899832141 0.207082412559667 -0.634867530308786 0.52574789498644 df.mm.trans1:probe2 -0.117692750131335 0.132450356107107 -0.888580095897679 0.374571737298194 df.mm.trans1:probe3 -0.0674511430176277 0.132450356107107 -0.509256033732991 0.610753092837845 df.mm.trans1:probe4 -0.104790709055284 0.132450356107107 -0.791169704145934 0.429146060522531 df.mm.trans1:probe5 -0.118119534327926 0.132450356107107 -0.891802315974202 0.372843202928562 df.mm.trans1:probe6 -0.110025028464467 0.132450356107107 -0.830688808231619 0.406467676616254 df.mm.trans1:probe7 0.0320577984942676 0.132450356107107 0.242036332981571 0.808831660234347 df.mm.trans1:probe8 -0.102439575122992 0.132450356107107 -0.773418646305131 0.43956801505324 df.mm.trans1:probe9 -0.102023893537803 0.132450356107107 -0.770280250928885 0.441425656206104 df.mm.trans1:probe10 -0.280020995496285 0.132450356107107 -2.11415811724842 0.0348984601332281 * df.mm.trans1:probe11 -0.120080116767026 0.132450356107107 -0.906604710597544 0.364966379187671 df.mm.trans1:probe12 -0.197777379155576 0.132450356107107 -1.49321893099057 0.135886135752149 df.mm.trans1:probe13 -0.0077296129303232 0.132450356107107 -0.058358566617009 0.953481730661103 df.mm.trans1:probe14 0.054021046795119 0.132450356107107 0.407858826377441 0.68351744499353 df.mm.trans1:probe15 -0.0973969792582948 0.132450356107107 -0.735347054707302 0.462404887563635 df.mm.trans1:probe16 -0.272231837607178 0.132450356107107 -2.05534998627738 0.0402615209279734 * df.mm.trans1:probe17 -0.213185666327416 0.132450356107107 -1.60955147719664 0.108002366676793 df.mm.trans1:probe18 0.00892058360685688 0.132450356107107 0.0673503935289019 0.94632436821901 df.mm.trans2:probe2 -0.254946680978916 0.132450356107107 -1.92484707834799 0.0547035087615914 . df.mm.trans2:probe3 -0.257347335695690 0.132450356107107 -1.94297201804111 0.0524696544886476 . df.mm.trans2:probe4 -0.242295193939112 0.132450356107107 -1.82932836921312 0.0678282260362518 . df.mm.trans2:probe5 -0.100544784827401 0.132450356107107 -0.759112982271596 0.448072086135014 df.mm.trans2:probe6 -0.0571193907459153 0.132450356107107 -0.431251318793927 0.66643482361935 df.mm.trans3:probe2 -0.121458068756048 0.132450356107107 -0.917008246152463 0.359493159143647 df.mm.trans3:probe3 -0.081322774841471 0.132450356107107 -0.613986834249871 0.539448156141403 df.mm.trans3:probe4 -0.0371930340173567 0.132450356107107 -0.280807353868344 0.77895139112115