I've seen a fair few of these posts already, and I feel a bit silly for posting yet another question on the topic - but I just cant seem to get my head around it. I'm very new to R and esp new to lme, so bear with me.
My study is on behavioural traits in fish, where I have 'sampled' 49 fish over a 3 month period. My response variables are MEANDEPTH (mean depth), CUMDIST (cumulative distance traveled) and HRMND (home range size). My explanatory variables are L (length), A (age), W (weight), K (K-factor) and L1 (1st yr growth).
What I'm trying to figure out is how my responses vary on a monthly basis and what influence these changes - for example, Do lager fish behave in a different fashion?
An example of what I've got so far is
mod11<-lme(log(HRMND)~A+K+L1+L+A*L*L1*K,data=CODDING,random=~1|ID,na.action=na.exclude)
But doesnt this include all months, and just gives me an overview over how things change between FISH? SO I'm thinking that in a way or another that I should nest or group with MONTH?
mod11<-lme(log(HRMND)~A+K+L1+L+A*L*L1*K,data=CODDING,random=~1|ID/MONTH,na.action=na.exclude)
or
mod11<-lme(log(HRMND)~MONTH*A+K+L1+L+A*L*L1*K,data=CODDING,random=~1|ID,na.action=na.exclude)
OR
mod11<-lme(log(HRMND)~A+K+L1+L+A*L*L1*K,data=CODDING,random=~1|ID:MONTH,na.action=na.exclude)
But I still have a feeling that this is all wrong..
Below I've attached my dataset;
> dput(CODDING)
structure(list(ID = c(7288L, 7288L, 7288L, 7293L, 7293L, 7293L,
7294L, 7294L, 7294L, 7296L, 7296L, 7296L, 7298L, 7298L, 7298L,
7300L, 7300L, 7300L, 7303L, 7303L, 7303L, 7305L, 7305L, 7305L,
7306L, 7306L, 7306L, 7307L, 7307L, 7307L, 7308L, 7308L, 7308L,
7309L, 7309L, 7309L, 7311L, 7311L, 7311L, 7312L, 7312L, 7312L,
7313L, 7313L, 7313L, 7314L, 7314L, 7314L, 7318L, 7318L, 7318L,
7320L, 7320L, 7320L, 7321L, 7321L, 7321L, 7325L, 7325L, 7325L,
7326L, 7326L, 7326L, 7327L, 7327L, 7327L, 7328L, 7328L, 7328L,
7330L, 7330L, 7330L, 7333L, 7333L, 7333L, 7334L, 7334L, 7334L,
7336L, 7336L, 7336L, 7338L, 7338L, 7338L, 7339L, 7339L, 7339L,
7341L, 7341L, 7341L, 7342L, 7342L, 7342L, 7343L, 7343L, 7343L,
7344L, 7344L, 7344L, 7345L, 7345L, 7345L, 7346L, 7346L, 7346L,
7347L, 7347L, 7347L, 7349L, 7349L, 7349L, 7351L, 7351L, 7351L,
7353L, 7353L, 7353L, 7356L, 7356L, 7356L, 7357L, 7357L, 7357L,
7358L, 7358L, 7358L, 7359L, 7359L, 7359L, 7360L, 7360L, 7360L,
7362L, 7362L, 7362L, 7363L, 7363L, 7363L, 7364L, 7364L, 7364L,
7365L, 7365L, 7365L, 7366L, 7366L, 7366L), MEANDEPTHMND = c(10.725262,
12.200786, 13.564287, 10.707745, 13.587189, 14.198203, 8.462647,
8.896712, 16.015541, 8.481038, 7.041678, 7.285891, 8.663365,
9.253053, 17.173524, 11.97339, 11.331733, 13.794026, 11.47386,
13.07904, 16.5411, 8.771731, 20.405117, 22.202886, 13.29951,
15.7675, 19.4119, 8.197132, 8.25664, 10.082283, 14.13052, 14.73271,
18.59407, 11.258819, 10.885207, 9.014883, 13.936096, 18.509176,
15.562334, 14.165146, 16.427362, 13.590945, 13.65453, 15.08443,
20.93181, 10.16241, 10.286637, 13.86002, 14.407088, 12.637779,
18.089143, 6.728938, 14.2741, 14.094891, 5.957861, 5.914509,
5.826612, 15.764523, 19.651839, 23.124057, 6.865263, 6.678091,
7.54115, 14.533215, 11.785265, 14.19784, 8.668182, 9.134189,
11.899737, 9.243074, 9.704021, 12.313194, 8.1842, 8.616996, 13.320201,
7.443299, 10.705514, 21.653235, 12.58174, 13.93734, 18.04723,
10.17801, 10.522297, 23.451312, 9.314671, 9.801458, 14.862017,
14.532722, 13.119887, 10.412089, 5.341649, 5.094039, 5.465425,
9.124215, 10.050294, 12.503342, 7.170584, 7.343889, 13.018498,
4.978633, 7.359922, 11.55276, 11.619463, 12.955063, 16.950109,
8.12889, 9.888701, 18.692396, 8.214024, 9.616029, 15.434882,
10.157199, 20.685936, 26.921978, 13.24223, 14.71168, 15.24348,
13.99331, 13.71569, 14.52495, 16.23245, 14.70182, 20.9764, 9.837841,
20.111058, 21.78553, 7.449171, 8.706451, 11.070614, 10.676216,
14.008959, 25.81545, 10.453505, 4.206622, 3.582907, 12.899, 13.8475,
14.38309, 17.55982, 17.32079, 16.53955, 30.602365, 6.328528,
3.704692, 11.696339, 11.786703, 16.052708), MONTH = c(6L, 7L,
8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L,
6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L,
7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L,
8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L,
6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L,
7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L,
8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L,
6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L,
7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L, 8L, 6L, 7L,
8L), CUMMND = c(148974.1, 171929.3, 99069.97, 129907, 129835.1,
149201.9, 69416.58, 62566.24, 130587.8, 50782.51, 31477.03, 9249.42,
89551.78, 95519.84, 58154.92, 173916.9, 168504.2, 144269.7, 101039.1,
53324.26, 33865.99, 137442.3, 115678.4, 117456, 175852.3, 168014.9,
165600.3, 149324.3, 155302.1, 113610.5, 149992, 135209.2, 148616.7,
95172.35, 90167.98, 101916.9, 141753.1, 127389.7, 152162.4, 154257.1,
97991.67, 105943.1, 138843.2, 125017.5, 111700.4, 102351.6, 79254.78,
9964.566, 132743.5, 109509.4, 52689.9, 145669.6, 137691.1, 113990,
71017.53, 44875.13, 80049.09, 96738.65, 92763.12, 26388.68, 68470.29,
66847.35, 46495.22, 176603.6, 157265.4, 154788, 79326.2, 40961.61,
22673.42, 100548.5, 107353.8, 138281.2, 98429.28, 102775.3, 90914.61,
120933.2, 127609.6, 165635.3, 185937.1, 172564.9, 149174.3, 106384.8,
53641.83, 65757.93, 62080.82, 49910.08, 130763.9, 168503.3, 156763.7,
152423.7, 81643.54, 83967.29, 78720.64, 169761.6, 164332.6, 135755.9,
137047.2, 59321.11, 74910.94, 31278.17, 56611.2, 112278, 154989.7,
136986, 171017.8, 128664.1, 149874.2, 94740.11, 145985.7, 119399.8,
104811.8, 83963.69, 56427.02, 26790.39, 125646.7, 125337.3, 131328.9,
121618.4, 146529.1, 154746.8, 76478.93, 97928.08, 77460.88, 129896.2,
78492.33, 76384.62, 126209, 97418.64, 116615.9, 99622.77, 101672.3,
150328.7, 120802.7, 135307.6, 103726.2, 109304.2, 121024.7, 112431.6,
148247.3, 151481.6, 181459, 85739.81, NA, 83232.05, 170169.9,
161198.6, 61503.5), HRMND = c(1873168, 637235.6, 2686066, 797586.5,
680754.9, 397648.9, 480643.1, 373537.4, 1254880, 1608148, 463576.7,
383217.1, 497585.5, 486003.1, 2877401, 1103932, 473820.5, 4776109,
329410, 260994.9, 300850.3, 654988.1, 1010306, 1098825, 407661.2,
528440.8, 1121661, 279148.5, 206099, 206450.8, 573508.3, 541271.5,
260193.7, 387968.5, 254684.4, 479391.8, 633900.6, 239233.3, 304772.3,
1354606, 294176.6, 918708.4, 696910.9, 822802.7, 3184885, 3184885,
581067.4, 1598596, 2384490, 544204.6, 2133508, 252528.5, 153201.8,
3246378, 253993.7, 194549.3, 112046.9, 512059.9, 339163.6, 762145.2,
344128.7, 298777.9, 938502.4, 1403911, 706129, 452398.4, 2509116,
500621.6, 2796525, 309062.7, 191797.2, 2916431, 264717.6, 201020.3,
222392.5, 477403.8, 398037, 2102339, 1062142, 773654.8, 726203.1,
416095.5, 109564.9, 3401014, 421404.2, 503160.4, 4815527, 268447.2,
506682, 4600924, 404484.7, 175489.8, 413650.9, 308857.2, 241956.2,
673644.6, 956416.3, 414738.8, 4335718, 142446.9, 700196.2, 657465.8,
1440978, 347600.9, 980805.8, 3658006, 927301, 3628336, 281490.8,
222893.7, 1887973, 2498449, 404445.7, NA, 315003.8, 274804.6,
222542.3, 227833, 315854.4, 171730.1, 165038, 480690.2, 328394.1,
258172.9, 94474.71, 130301, 456144.7, 339891.1, 1939505, 326920,
326491.4, 1959026, 342101, 226885.6, 373924.5, 389011.7, 418293.7,
1255376, 914433, 950183.6, 903774.1, 2217491, NA, 1269254, 1810494,
306736.1, 2733252), L = c(420L, 420L, 420L, 390L, 390L, 390L,
560L, 560L, 560L, 600L, 600L, 600L, 330L, 330L, 330L, 350L, 350L,
350L, 400L, 400L, 400L, 560L, 560L, 560L, 420L, 420L, 420L, 450L,
450L, 450L, 460L, 460L, 460L, 400L, 400L, 400L, 300L, 300L, 300L,
430L, 430L, 430L, 410L, 410L, 410L, 740L, 740L, 740L, 440L, 440L,
440L, 600L, 600L, 600L, 460L, 460L, 460L, 370L, 370L, 370L, 540L,
540L, 540L, 460L, 460L, 460L, 690L, 690L, 690L, 560L, 560L, 560L,
500L, 500L, 500L, 610L, 610L, 610L, 380L, 380L, 380L, 520L, 520L,
520L, 630L, 630L, 630L, 370L, 370L, 370L, 330L, 330L, 330L, 500L,
500L, 500L, 560L, 560L, 560L, 480L, 480L, 480L, 510L, 510L, 510L,
490L, 490L, 490L, 400L, 400L, 400L, 580L, 580L, 580L, 370L, 370L,
370L, 560L, 560L, 560L, 800L, 800L, 800L, 630L, 630L, 630L, 600L,
600L, 600L, 530L, 530L, 530L, 390L, 390L, 390L, 790L, 790L, 790L,
410L, 410L, 410L, 310L, 310L, 310L, 540L, 540L, 540L), W = c(695L,
695L, 695L, 615L, 615L, 615L, 1770L, 1770L, 1770L, 2150L, 2150L,
2150L, 400L, 400L, 400L, 505L, 505L, 505L, 625L, 625L, 625L,
1825L, 1825L, 1825L, 715L, 715L, 715L, 815L, 815L, 815L, 820L,
820L, 820L, 670L, 670L, 670L, 280L, 280L, 280L, 720L, 720L, 720L,
630L, 630L, 630L, 4210L, 4210L, 4210L, 730L, 730L, 730L, 1910L,
1910L, 1910L, 1080L, 1080L, 1080L, 495L, 495L, 495L, 1540L, 1540L,
1540L, 920L, 920L, 920L, 2880L, 2880L, 2880L, 1780L, 1780L, 1780L,
1320L, 1320L, 1320L, 2000L, 2000L, 2000L, 545L, 545L, 545L, 1465L,
1465L, 1465L, 1945L, 1945L, 1945L, 500L, 500L, 500L, 345L, 345L,
345L, 1330L, 1330L, 1330L, 1580L, 1580L, 1580L, 1145L, 1145L,
1145L, 1190L, 1190L, 1190L, 1135L, 1135L, 1135L, 700L, 700L,
700L, 2105L, 2105L, 2105L, 470L, 470L, 470L, 1850L, 1850L, 1850L,
4765L, 4765L, 4765L, 2405L, 2405L, 2405L, 2130L, 2130L, 2130L,
1330L, 1330L, 1330L, 665L, 665L, 665L, 4050L, 4050L, 4050L, 645L,
645L, 645L, 325L, 325L, 325L, 1630L, 1630L, 1630L), A = c(2L,
2L, 2L, 3L, 3L, 3L, NA, NA, NA, NA, NA, NA, 3L, 3L, 3L, 2L, 2L,
2L, 2L, 2L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 5L, 5L, 5L, 3L,
3L, 3L, NA, NA, NA, 3L, 3L, 3L, 2L, 2L, 2L, NA, NA, NA, 4L, 4L,
4L, 5L, 5L, 5L, NA, NA, NA, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L,
4L, 4L, 4L, 5L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 5L,
5L, 5L, NA, NA, NA, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 4L, 4L,
4L, 3L, 3L, 3L, 3L, 3L, 3L, 6L, 6L, 6L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 3L, 3L, 3L, 5L, 5L, 5L, 3L, 3L, 3L, 2L, 2L, 2L, 3L,
3L, 3L), K = c(0.94, 0.94, 0.94, 1.04, 1.04, 1.04, 1.01, 1.01,
1.01, 1, 1, 1, 1.11, 1.11, 1.11, 1.18, 1.18, 1.18, 0.98, 0.98,
0.98, 1.04, 1.04, 1.04, 0.97, 0.97, 0.97, 0.89, 0.89, 0.89, 0.84,
0.84, 0.84, 1.05, 1.05, 1.05, 1.04, 1.04, 1.04, 0.91, 0.91, 0.91,
0.91, 0.91, 0.91, 1.04, 1.04, 1.04, 0.86, 0.86, 0.86, 0.88, 0.88,
0.88, 1.11, 1.11, 1.11, 0.98, 0.98, 0.98, 0.89, 0.89, 0.89, 0.95,
0.95, 0.95, 0.88, 0.88, 0.88, 1.01, 1.01, 1.01, 1.06, 1.06, 1.06,
0.88, 0.88, 0.88, 0.99, 0.99, 0.99, 1.04, 1.04, 1.04, 0.78, 0.78,
0.78, 0.99, 0.99, 0.99, 0.96, 0.96, 0.96, 1.06, 1.06, 1.06, 0.9,
0.9, 0.9, 1.04, 1.04, 1.04, 0.9, 0.9, 0.9, 0.96, 0.96, 0.96,
1.09, 1.09, 1.09, 1.08, 1.08, 1.08, 0.93, 0.93, 0.93, 1.05, 1.05,
1.05, 0.93, 0.93, 0.93, 0.96, 0.96, 0.96, 0.99, 0.99, 0.99, 0.89,
0.89, 0.89, 1.12, 1.12, 1.12, 0.82, 0.82, 0.82, 0.94, 0.94, 0.94,
1.09, 1.09, 1.09, 1.04, 1.04, 1.04), L1 = c(144.5, 144.5, 144.5,
152.6, 152.6, 152.6, NA, NA, NA, NA, NA, NA, 118.3, 118.3, 118.3,
102.1, 102.1, 102.1, 148.6, 148.6, 148.6, 172.3, 172.3, 172.3,
95.5, 95.5, 95.5, 183.8, 183.8, 183.8, 125.1, 125.1, 125.1, 92.3,
92.3, 92.3, 133.3, 133.3, 133.3, 140.1, 140.1, 140.1, 136.7,
136.7, 136.7, 211.4, 211.4, 211.4, 165, 165, 165, NA, NA, NA,
167.3, 167.3, 167.3, 167.3, 167.3, 167.3, NA, NA, NA, 95.4, 95.4,
95.4, 167.3, 167.3, 167.3, NA, NA, NA, 136.4, 136.4, 136.4, 183,
183, 183, 190, 190, 190, 197.2, 197.2, 197.2, 159.4, 159.4, 159.4,
134.5, 134.5, 134.5, 105, 105, 105, 174.8, 174.8, 174.8, 126.5,
126.5, 126.5, NA, NA, NA, 143.4, 143.4, 143.4, 132.8, 132.8,
132.8, 194.7, 194.7, 194.7, 153.1, 153.1, 153.1, 260.4, 260.4,
260.4, 140, 140, 140, 258.8, 258.8, 258.8, 160.8, 160.8, 160.8,
201.4, 201.4, 201.4, 138.1, 138.1, 138.1, 140.4, 140.4, 140.4,
210.7, 210.7, 210.7, 114.8, 114.8, 114.8, 134.5, 134.5, 134.5,
172.8, 172.8, 172.8)), .Names = c("ID", "MEANDEPTHMND", "MONTH",
"CUMMND", "HRMND", "L", "W", "A", "K", "L1"), class = "data.frame", row.names = c(NA,
-147L))
As mentioned, I'm sure the answer to my question is out there on the interwebs somewhere, but I'm confused and reading all these mixed effects model books are not helping either.
All help, pointers and advice would be greatly appreciated!
