I'm trying to replicate a method for canonical analysis I was taught on a new dataset. I've created a MANCOVA model with 1 categorical predictor (temperature), 4 continuous predictors (BM, Mass, SMR, and AS in attached dataset), and 4 response variables (SDA total, SDA duration, SDA peak, & SDA peak time). The model passes assumptions of both ANCOVA and MANOVA. This is the model code with interaction terms removed (they were non-significant):
lionfish.mancova.final<-manova(cbind(sqrt.SDA_integral,`SDA duration`,log.peak,log.peaktime)~Temperature+BM+Mass+SMR+AS, data=lionfish_data)
The goal of the canonical analysis is to find which predictor is driving most of the response. I was taught to find the percent of the model variation explained by the first eigenvector of each predictor using the candisc package. Using either
candiscList(lionfish.mancova.final)
which lists first-eigenvector information for each predictor or
mancova.temp<-candisc(lionfish.mancova.final,term="Temperature")
mancova.temp$pct
which prints percentage explained by first eigenvector for each predictor directly (temperature, in this case), the percentage explained for each predictor is always 100%. This obviously isn't happening, and this wasn't an issue in the previous dataset. Any thoughts as to why it'd be reporting 100% for each predictor's 1st eigenvector? Thanks in advance to anyone who can share advice!
Data (copied from CSV):
Fish,Date,Temperature,SMR end (hr),Mass,MMR,SMR,AS,RMR block min,BM,SDA_peak,SDA_peaktime,Fixed SDA end hour,SDA duration,SDA integral
1544,Mar 14-19,Low,24.7,227,263.8,55.58502519,208.2149748,70.51328101,4.3,168.3,5,113.6,88.9,3499.5
1545,Mar 14-19,Low,24.7,100,203.3,51.15976604,152.140234,88.54550957,3.6,167.8,2.9,65.1,40.4,2448.2
1541,Mar 14-19,Low,24.7,90,254.2,57.53883131,196.6611687,80.9416882,6.8,247.3,5.5,135.34,110.64,9587.5
1542,Mar 14-19,Low,24.7,98,259.3,48.95655884,210.3434412,72.16391583,3.6,188.5,3,135.34,110.64,5819.5
1543,Mar 14-19,Low,24.7,62,271.6,29.93112963,241.6688704,68.05718878,13.5,260.1,33,128.4,103.7,10868.8
1546,Mar 14-19,Low,24.7,91,240.2,63.8277889,176.3722111,75.31534816,3,189.9,0.5,65.1,40.4,1670.4
1541,Mar 21-26,Low,23,90,254.2,122.2637762,131.9362238,128.7860128,3.4,216.8,7.1,67.9,44.9,1448.7
1542,Mar 21-26,Low,23,98,259.3,76.61251742,182.6874826,99.27681948,1.6,178.1,3.5,92.3,69.3,2995.6
1543,Mar 21-26,Low,23,62,271.6,87.8949261,183.7050739,101.4613985,2.9,231.9,2,65.9,42.9,2252.5
1546,Mar 21-26,Low,23,91,240.2,94.72629332,145.4737067,103.8777369,9.8,269.6,2.5,84.3,61.3,4486.4
1696,Jun 29-Jul 7,Low,25.5,74.5,324,87.26157545,236.7384246,102.8344457,3.6,231.75,22.2,99.1,73.6,4835.7
1694,Jun 29-Jul 7,Low,25.5,70,299.7,76.99348356,222.7065164,95.56704067,5.1,226.62,7.2,91.1,65.6,4414.5
1693,Jun 29-Jul 7,Low,25.5,226.3,261.4,75.95940767,185.4405923,92.31355058,3,199.94,3,89.1,63.6,3404.65
1692,Jun 29-Jul 7,Low,46.7,141.5,270.8,83.85898147,186.9410185,96.81155237,2.4,180.48,6,89.1,42.4,1738.4
1691,Jun 29-Jul 7,Low,25.5,218,249.7,75.92790049,173.7720995,88.05633355,1.4,164.09,12.6,74.6,49.1,1317.2
1690,Jun 29-Jul 7,Low,25.5,187.5,261.8,79.22664204,182.573358,100.1478754,6.1,234.03,2.5,91.1,65.6,4827.6
1689,Jun 29-Jul 7,Low,25.5,85.5,275.6,87.38845731,188.2115427,103.862512,9.1,291.47,22.7,121.2,95.7,9134.9
1688,Jun 29-Jul 7,Low,25.5,111,257.6,79.78106848,177.8189315,89.49529046,6.5,253.84,4,89.1,63.6,5088.8
1673,Jul 8-13,High,26,229,222.8,117.8055028,104.9944972,134.9063456,5.8,246.7,6.6,126.6,100.6,4788.8
1671,Jul 8-13,High,26,181,256,122.8775872,133.1224128,137.2840714,5.4,262.1,5.1,72.9,46.9,3667.4
1670,Jul 8-13,High,26,217,246,114.796743,131.203257,130.5734965,1.8,196.2,2,52.9,26.9,989.1
1669,Jul 8-13,High,26,93,329,141.7096183,187.2903817,167.6722328,6.8,355.3,8.6,90.9,64.9,5262.1
1668,Jul 8-13,High,26,57,280.4,161.9566443,118.4433557,183.9718023,10.2,434.9,4.1,80.9,54.9,5770.6
1666,Jul 8-13,High,26,167,247.3,140.7646959,106.5353041,163.9397034,13.5,304.4,9.6,78.9,52.9,3978
1665,Jul 8-13,High,26,168,232.6,125.9586414,106.6413586,137.1149026,6.4,298.2,4.6,70.9,44.9,4016.5
1663,Jul 14-19,High,23.5,270,202.1,82.17449207,119.9255079,95.2014343,7.4,229.2,5,117,93.5,5634.2
1689,Jul 14-19,High,23.5,89.5,273.98,102.5626464,171.4173536,111.5290659,7.8,292.5,3.5,123.3,99.8,6285.6
1693,Jul 14-19,High,23.5,236.5,291.7,122.196061,169.503939,136.3122595,6.8,251.9,8.1,84.2,60.7,4493.5
1696,Jul 14-19,High,23.5,76,313.97,97.56856134,216.4014387,123.7317292,5,285.6,7.2,110.2,86.7,6327.3
1694,Jul 14-19,High,23.5,66,353.4,104.5609708,248.8390292,123.5321187,6.7,284.3,5,92.2,68.7,5350.1
1688,Jul 14-19,High,23.5,115,238,98.88968145,139.1103186,111.4421464,7.4,290.1,7.7,116.5,93,5502.7
1691,Jul 14-19,High,23.5,214.5,301,89.00250227,211.9974977,112.2052501,8.8,262.8,7.7,64.2,40.7,4173.3
untagged 1,Jul 20-24,High,25,43.5,261.4,137.3095495,124.0904505,151.6263175,13.8,456,8.1,99.3,74.3,11933.9
1680,Jul 20-25,High,25,323,157.3,87.10641649,70.19358351,94.0451836,7.1,179.8,1,69.3,44.3,2088.7
1682,Jul 20-25,High,25,241.5,198.6,99.07483092,99.52516908,106.9928424,8.4,266,6.1,107.3,82.3,5861.6
1686,Jul 20-25,High,25,176.5,284.5,109.6719638,174.8280362,115.200684,11.6,330.3,9.7,113.4,88.4,9332.4
1685,Jul 20-25,High,25,192,212.3,101.1924491,111.1075509,106.0441964,6,255.1,11.1,95.3,70.3,4732.3
1687,Jul 20-25,High,25,181.5,253.4,106.5658012,146.8341988,116.7643461,4.1,247.6,1,91.3,66.3,3840.5
1684,Jul 20-25,High,25,105,262.7,104.7064061,157.9935939,124.7503246,8.5,340.9,7.1,93.3,68.3,6681.3
1681,Jul 20-25,High,25,136.5,245.5038,111.3920692,134.1117308,120.3785521,8.6,306.6,5.6,113.4,88.4,6911.2