fitVsDatCorrelation=0.927285553361654
cont.fitVsDatCorrelation=0.213767693929285

fstatistic=11207.1526083840,68,1060
cont.fstatistic=1632.49539172311,68,1060

residuals=-1.18938558377107,-0.0880796861460947,-0.00321513034762365,0.080641642520894,0.800436411276855
cont.residuals=-0.723484382151508,-0.265337844832967,-0.111459387694732,0.126918279928922,2.05308557101826

predictedValues:
Include	Exclude	Both
Lung	54.7572167587541	50.4796838883815	79.7343885611068
cerebhem	59.494378018529	53.883809680536	70.718546834549
cortex	54.9551004938627	50.8707356833613	73.9998183870338
heart	56.1975328938454	54.7308304035764	87.322150477387
kidney	55.2772358810891	47.1573932502874	74.141815749669
liver	56.4533760278209	47.4821693005709	72.8039285269407
stomach	57.6909593153936	53.7409075718686	71.9376962601592
testicle	58.7401157881588	52.1175961259418	79.151873827099


diffExp=4.2775328703726,5.61056833799302,4.0843648105014,1.46670249026904,8.11984263080167,8.97120672725,3.95005174352501,6.622519662217
diffExpScore=0.977325697161431
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	64.1875144228986	54.5429875020408	67.8281136873928
cerebhem	61.2480664233215	60.7992552336642	62.8795089895865
cortex	73.4779807449398	70.886797106603	58.4384218672711
heart	67.0467366870553	71.3229887085895	65.810059124914
kidney	71.7891772180315	69.8975212328944	67.3247043089664
liver	64.6821811695653	63.8599937779644	64.2801310354242
stomach	65.2998745498744	57.2981526065475	61.1997596015703
testicle	65.5065231722363	69.6068617219958	60.9216941639041
cont.diffExp=9.64452692085781,0.448811189657285,2.59118363833683,-4.27625202153421,1.89165598513713,0.822187391600949,8.00172194332692,-4.10033854975948
cont.diffExpScore=1.98313006433546

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=0.525437500878128
cont.tran.correlation=0.65701898764168

tran.covariance=0.000921237050538735
cont.tran.covariance=0.00406847356817263

tran.mean=54.0018150676236
cont.tran.mean=65.7157882673889

weightedLogRatios:
wLogRatio
Lung	0.322281541612028
cerebhem	0.399808180656835
cortex	0.306435831895018
heart	0.106196593259576
kidney	0.624825432459578
liver	0.683051165624638
stomach	0.285096750652951
testicle	0.480073547030186

cont.weightedLogRatios:
wLogRatio
Lung	0.664368759286149
cerebhem	0.0302372145412049
cortex	0.153624125359591
heart	-0.261925438881585
kidney	0.113767291007455
liver	0.0532570156806209
stomach	0.537740742957838
testicle	-0.255755682567354

varWeightedLogRatios=0.0359123304667022
cont.varWeightedLogRatios=0.11012520499956

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.86901085814951	0.0720924684861953	53.6673377870307	1.75992577776710e-304	***
df.mm.trans1	0.136269057223177	0.0615279985201223	2.21474874042281	0.0269893715331838	*  
df.mm.trans2	0.0356970359652819	0.0536382287087789	0.665514817036443	0.505866053362834	   
df.mm.exp2	0.268224845578054	0.0673523813395429	3.98241072169156	7.28747303700445e-05	***
df.mm.exp3	0.085962517692873	0.0673523813395429	1.27630999800157	0.202125521145182	   
df.mm.exp4	0.0159167148268478	0.0673523813395428	0.236320001019815	0.813230023576505	   
df.mm.exp5	0.0140931260924744	0.0673523813395429	0.209244659389650	0.834297455677108	   
df.mm.exp6	0.0602202735597312	0.0673523813395429	0.894107563267041	0.371467303067014	   
df.mm.exp7	0.217695392221989	0.0673523813395429	3.23218552770278	0.00126632447655495	** 
df.mm.exp8	0.109477904551649	0.0673523813395428	1.62544964816818	0.104364040170246	   
df.mm.trans1:exp2	-0.185252197529529	0.0612985583231648	-3.02212976287114	0.00257030573209772	** 
df.mm.trans2:exp2	-0.202965745867089	0.0409955265877679	-4.9509242290755	8.58846960038911e-07	***
df.mm.trans1:exp3	-0.082355193471694	0.0612985583231648	-1.34350946783314	0.179394597758522	   
df.mm.trans2:exp3	-0.0782456530321277	0.0409955265877679	-1.90863880878833	0.0565785718660447	.  
df.mm.trans1:exp4	0.0100469694678708	0.0612985583231648	0.163902214712838	0.869839369002465	   
df.mm.trans2:exp4	0.064939506847741	0.0409955265877679	1.58406324428376	0.113477607637378	   
df.mm.trans1:exp5	-0.00464112298960235	0.0612985583231648	-0.0757134118087156	0.9396613922857	   
df.mm.trans2:exp5	-0.0821732826393169	0.040995526587768	-2.00444510606275	0.0452763969775503	*  
df.mm.trans1:exp6	-0.0297143520775424	0.0612985583231648	-0.484747975978308	0.627955282737995	   
df.mm.trans2:exp6	-0.121436972236196	0.040995526587768	-2.96220057025515	0.00312262862109021	** 
df.mm.trans1:exp7	-0.165504088248134	0.0612985583231648	-2.69996705918596	0.00704505579262751	** 
df.mm.trans2:exp7	-0.155091857074531	0.040995526587768	-3.78314099082232	0.000163525529421881	***
df.mm.trans1:exp8	-0.039264180540644	0.0612985583231648	-0.640540032501973	0.521960012063506	   
df.mm.trans2:exp8	-0.0775462313680375	0.0409955265877679	-1.89157788233352	0.0588196488662381	.  
df.mm.trans1:probe2	0.114770162676572	0.0462793966460682	2.47994077265748	0.0132948750091155	*  
df.mm.trans1:probe3	0.0333733001956778	0.0462793966460682	0.7211265188029	0.470990690259329	   
df.mm.trans1:probe4	-0.149526160107876	0.0462793966460682	-3.23094445788501	0.00127178119517149	** 
df.mm.trans1:probe5	-0.0387476788214228	0.0462793966460682	-0.837255487960531	0.402637697476851	   
df.mm.trans1:probe6	-0.0174701244438499	0.0462793966460682	-0.377492484991896	0.70588321610467	   
df.mm.trans1:probe7	1.19241527349094	0.0462793966460682	25.7655751783067	4.58299094058661e-114	***
df.mm.trans1:probe8	0.48163340006485	0.0462793966460682	10.4070803633904	3.22527380001926e-24	***
df.mm.trans1:probe9	0.197245658243004	0.0462793966460682	4.26206200896445	2.20580458160753e-05	***
df.mm.trans1:probe10	0.0577615545493092	0.0462793966460682	1.24810517714942	0.212268163329203	   
df.mm.trans1:probe11	-0.171014130962022	0.0462793966460682	-3.69525411642442	0.000230896082370665	***
df.mm.trans1:probe12	-0.150415339935161	0.0462793966460682	-3.25015775563142	0.00118969274449241	** 
df.mm.trans1:probe13	-0.181916074699277	0.0462793966460682	-3.93082209110287	9.0147165023027e-05	***
df.mm.trans1:probe14	-0.198923210876037	0.0462793966460682	-4.29831037766862	1.87947672097222e-05	***
df.mm.trans1:probe15	-0.158710820771621	0.0462793966460682	-3.42940557296797	0.000628184381370598	***
df.mm.trans1:probe16	-0.112037139650324	0.0462793966460682	-2.42088591835266	0.0156498849435294	*  
df.mm.trans1:probe17	-0.173645242642245	0.0462793966460682	-3.75210688182119	0.000184859243810427	***
df.mm.trans1:probe18	-0.128201034699003	0.0462793966460682	-2.77015354541998	0.00570087328059503	** 
df.mm.trans1:probe19	-0.238648268797494	0.0462793966460682	-5.15668496334575	2.99803225150524e-07	***
df.mm.trans1:probe20	-0.166971234163787	0.0462793966460682	-3.60789565691049	0.000323091888842531	***
df.mm.trans1:probe21	-0.122647792329278	0.0462793966460682	-2.65015970859028	0.00816518277950998	** 
df.mm.trans1:probe22	-0.165525535432616	0.0462793966460682	-3.57665716125274	0.000363723109597982	***
df.mm.trans2:probe2	0.091100851756078	0.0462793966460682	1.96849696319059	0.0492711435752869	*  
df.mm.trans2:probe3	-0.0316539616691662	0.0462793966460682	-0.683975245210018	0.494140292154408	   
df.mm.trans2:probe4	0.195550326805481	0.0462793966460682	4.22542947785155	2.59005081137378e-05	***
df.mm.trans2:probe5	-0.0292698008853937	0.0462793966460682	-0.632458567021539	0.52722367082328	   
df.mm.trans2:probe6	0.195849133555634	0.0462793966460682	4.23188606051703	2.51799331039873e-05	***
df.mm.trans3:probe2	0.455057635526801	0.0462793966460682	9.83283423089878	6.79074664886357e-22	***
df.mm.trans3:probe3	0.151074898697133	0.0462793966460682	3.26440942721253	0.00113200020182146	** 
df.mm.trans3:probe4	0.64899828687731	0.0462793966460682	14.0234820224790	4.13806231782212e-41	***
df.mm.trans3:probe5	0.123092921649041	0.0462793966460682	2.65977801289029	0.00793718356379098	** 
df.mm.trans3:probe6	0.750539173701733	0.0462793966460682	16.2175660897580	5.27344910069335e-53	***
df.mm.trans3:probe7	0.614341875876598	0.0462793966460682	13.2746301896482	2.58176660482432e-37	***
df.mm.trans3:probe8	0.430464469924381	0.0462793966460682	9.30142787332454	7.69102261280861e-20	***
df.mm.trans3:probe9	0.580463015238413	0.0462793966460682	12.5425795776386	9.52955820239926e-34	***
df.mm.trans3:probe10	0.294461180890857	0.0462793966460682	6.36268409337342	2.94443334692099e-10	***
df.mm.trans3:probe11	-0.135657277392675	0.0462793966460682	-2.93126719931429	0.00344828820526255	** 
df.mm.trans3:probe12	0.895617403329033	0.0462793966460682	19.3524001658549	1.09143676839754e-71	***
df.mm.trans3:probe13	0.182079877708538	0.0462793966460682	3.93436152811225	8.8848043704e-05	***
df.mm.trans3:probe14	-0.0764672438587121	0.0462793966460682	-1.65229560885402	0.0987704562044	.  
df.mm.trans3:probe15	-0.0103339001792208	0.0462793966460682	-0.223293753335886	0.823349911795248	   
df.mm.trans3:probe16	1.65367309509044	0.0462793966460682	35.7323823328395	3.65153309315467e-184	***
df.mm.trans3:probe17	-0.0283196763825357	0.0462793966460682	-0.611928383576749	0.54071636836206	   
df.mm.trans3:probe18	-0.0591071556509585	0.0462793966460682	-1.27718077448143	0.201818112953649	   
df.mm.trans3:probe19	-0.0532222685936713	0.0462793966460682	-1.15002079652637	0.250394692309909	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.98007927114411	0.188126398299953	21.1564103023871	3.74407403086867e-83	***
df.mm.trans1	0.248827440647734	0.160558252467273	1.54976425580151	0.121496604601494	   
df.mm.trans2	0.0155877498812986	0.139969777565655	0.111365111471917	0.911347907901938	   
df.mm.exp2	0.137468380880388	0.175757068448273	0.782149941928774	0.43430113702918	   
df.mm.exp3	0.546275295615639	0.175757068448273	3.10812703260589	0.00193292450516124	** 
df.mm.exp4	0.342014836050978	0.175757068448272	1.94595209780503	0.0519244986026714	.  
df.mm.exp5	0.367415570369588	0.175757068448273	2.09047393435401	0.0368130079836598	*  
df.mm.exp6	0.219107167593013	0.175757068448273	1.24664782775151	0.212802064308028	   
df.mm.exp7	0.169294136693837	0.175757068448273	0.963228040775285	0.335652779012991	   
df.mm.exp8	0.371602441750711	0.175757068448272	2.11429585752381	0.0347230106855405	*  
df.mm.trans1:exp2	-0.184344812940924	0.15995952476679	-1.15244661554031	0.249397426843524	   
df.mm.trans2:exp2	-0.0288799936952607	0.106978453163156	-0.269960845771577	0.787242922876959	   
df.mm.trans1:exp3	-0.411098228370152	0.15995952476679	-2.57001406430473	0.0103051218548351	*  
df.mm.trans2:exp3	-0.284180250208678	0.106978453163156	-2.65642511932067	0.0080160064006851	** 
df.mm.trans1:exp4	-0.298433609738525	0.15995952476679	-1.86568202283434	0.0623619276841844	.  
df.mm.trans2:exp4	-0.0737852905829225	0.106978453163156	-0.689721045698711	0.490520592264285	   
df.mm.trans1:exp5	-0.255490553190242	0.15995952476679	-1.59722000651558	0.110514719021115	   
df.mm.trans2:exp5	-0.119374535642365	0.106978453163156	-1.11587457205334	0.264728725221679	   
df.mm.trans1:exp6	-0.211430123367757	0.15995952476679	-1.32177264014761	0.186529053611856	   
df.mm.trans2:exp6	-0.0614032300044789	0.106978453163156	-0.573977545841226	0.566104764266561	   
df.mm.trans1:exp7	-0.152112733936694	0.15995952476679	-0.95094514789578	0.341848991807069	   
df.mm.trans2:exp7	-0.120014906481974	0.106978453163156	-1.12186055166582	0.262175799507692	   
df.mm.trans1:exp8	-0.351261426040723	0.15995952476679	-2.19593942000539	0.0283123473126829	*  
df.mm.trans2:exp8	-0.127728443575759	0.106978453163156	-1.19396420306206	0.232759094051585	   
df.mm.trans1:probe2	-0.118755454737636	0.120766792833386	-0.983345271919871	0.325661924948267	   
df.mm.trans1:probe3	-0.153786535912461	0.120766792833386	-1.27341740477145	0.203149139761204	   
df.mm.trans1:probe4	-0.117804943024570	0.120766792833386	-0.975474633884647	0.329547435989878	   
df.mm.trans1:probe5	-0.157656686251200	0.120766792833386	-1.30546388251536	0.192018211494922	   
df.mm.trans1:probe6	-0.112121641144687	0.120766792833386	-0.928414496353928	0.353403958949306	   
df.mm.trans1:probe7	0.0220794179439196	0.120766792833386	0.182826896582251	0.854968804785645	   
df.mm.trans1:probe8	-0.109301191614635	0.120766792833386	-0.905059984207999	0.365639269243836	   
df.mm.trans1:probe9	-0.145338815770893	0.120766792833386	-1.20346671763825	0.229064462916682	   
df.mm.trans1:probe10	-0.186389564726194	0.120766792833386	-1.5433842396009	0.123036046833590	   
df.mm.trans1:probe11	-0.115540058503639	0.120766792833386	-0.956720434424737	0.338926524605213	   
df.mm.trans1:probe12	-0.104481833178673	0.120766792833386	-0.865153662918085	0.387150206293812	   
df.mm.trans1:probe13	-0.228849875914377	0.120766792833386	-1.89497353159081	0.0583678155181351	.  
df.mm.trans1:probe14	-0.109404398445479	0.120766792833386	-0.905914580313625	0.365186935617882	   
df.mm.trans1:probe15	-0.189264026521602	0.120766792833386	-1.56718599609346	0.117369681085964	   
df.mm.trans1:probe16	-0.160188317519920	0.120766792833386	-1.32642685759587	0.184984051861429	   
df.mm.trans1:probe17	-0.0523407599759409	0.120766792833386	-0.433403576827217	0.66480978358413	   
df.mm.trans1:probe18	0.0084053129475665	0.120766792833386	0.0695995376739265	0.944525532248074	   
df.mm.trans1:probe19	-0.165471103715819	0.120766792833386	-1.37017055627294	0.170923786589032	   
df.mm.trans1:probe20	-0.203122696842087	0.120766792833386	-1.68194163375956	0.0928746987536243	.  
df.mm.trans1:probe21	-0.180875650117158	0.120766792833386	-1.49772670014266	0.134501982898557	   
df.mm.trans1:probe22	-0.170809152522120	0.120766792833386	-1.41437185268118	0.157546403686853	   
df.mm.trans2:probe2	0.106985915891414	0.120766792833386	0.885888524331487	0.375878524650725	   
df.mm.trans2:probe3	-0.00881767887881449	0.120766792833386	-0.0730141015749229	0.94180867069107	   
df.mm.trans2:probe4	0.017184474850058	0.120766792833386	0.142294702433361	0.88687427161413	   
df.mm.trans2:probe5	-0.131714011951964	0.120766792833386	-1.09064759328072	0.275675834707227	   
df.mm.trans2:probe6	0.0994145810955805	0.120766792833386	0.823194677635735	0.410582452009984	   
df.mm.trans3:probe2	-0.0826216547293693	0.120766792833386	-0.684142161855348	0.494034937728335	   
df.mm.trans3:probe3	0.00216532529678956	0.120766792833386	0.0179298070768256	0.985698224404434	   
df.mm.trans3:probe4	-0.138107529169199	0.120766792833386	-1.14358861346709	0.253052480969213	   
df.mm.trans3:probe5	-0.0263195146558550	0.120766792833386	-0.217936686388339	0.827520388460494	   
df.mm.trans3:probe6	-0.0300543190288505	0.120766792833386	-0.248862442429141	0.8035153970564	   
df.mm.trans3:probe7	-0.0457681045492723	0.120766792833386	-0.37897921668265	0.704779194956705	   
df.mm.trans3:probe8	-0.0537503756056404	0.120766792833386	-0.445075789002664	0.656355855151001	   
df.mm.trans3:probe9	0.0775115269983074	0.120766792833386	0.641828148117216	0.521123537700838	   
df.mm.trans3:probe10	-0.0206899759159427	0.120766792833386	-0.171321730340950	0.864003492956439	   
df.mm.trans3:probe11	0.0543406042184803	0.120766792833386	0.449963130953147	0.652829027604236	   
df.mm.trans3:probe12	-0.0327864556139271	0.120766792833386	-0.271485686128641	0.786070369689856	   
df.mm.trans3:probe13	-0.0555555369794139	0.120766792833386	-0.460023286832337	0.645593848108211	   
df.mm.trans3:probe14	0.0104299099600249	0.120766792833386	0.0863640551787643	0.931193330298143	   
df.mm.trans3:probe15	-0.0390546808932171	0.120766792833386	-0.323389236204181	0.74646426724357	   
df.mm.trans3:probe16	0.043700348858351	0.120766792833386	0.361857327110124	0.71753077089049	   
df.mm.trans3:probe17	-0.0321382514180746	0.120766792833386	-0.266118281889076	0.790199868270497	   
df.mm.trans3:probe18	-0.140199756930289	0.120766792833386	-1.16091314210616	0.245938594344631	   
df.mm.trans3:probe19	-0.0139377936594656	0.120766792833386	-0.115410812297506	0.908141367893691