Hi R-Help,
Data is below. I used a Kruskal Wallis to compare across 4 study groups for
my DV (beta - this is highly non-normal). Now I want to add a covariate
(cpz).
1) What package do I use and how do I specify the model? (I tried T.aov
from fANCOVA but received a lot of simpleLoess errors)
2) Can I specify "subject" as a random effect like in lme?
structure(list(subject = c("C5B1001", "C5B1002", "C5B1003", "C5B1004",
"C5B1005", "C5B1007", "C5B1008", "C5B1009", "C5B1010", "C5B1011",
"C5B1012", "C5B1013", "C5B1014", "C5B1015", "C5B1016", "C5B1017",
"C5B1018", "C5B1019", "C5B1020", "C5B1021", "C5B1022", "C5B1023",
"C5B1024", "C5B1025", "C5B1026", "C5B1027", "C5B1029", "C5B1030",
"C5B1031", "C5B1032", "C5B1033", "C5B1034", "C5B1035", "C5B1036",
"C5B1037", "C5B1038", "C5B1039", "C5B1040", "C5B1041", "C5B1042",
"C5B1043", "C5B1044", "C5B1045", "C5B1046", "C5B1047", "C5B1048",
"C5B1049", "C5D2002", "C5D2003", "C5D2005", "C5D2006", "C5D2007",
"C5D2009", "C5D2010", "C5D2011", "C5D2012", "C5D2013", "C5D2014",
"C5D2017", "C5D2021", "C5D2022", "C5D2023", "C5D2024", "C5D2025",
"C5D2026", "C5D2027", "C5D2028", "C5D2029", "C5D2030", "C5D2031",
"C5D2032", "C5D2035", "C5D2036", "C5D2037", "C5D2039", "C5D2040",
"C5D2042", "C5D2043", "C5D2044", "C5D2045", "C5D2046", "C5D2047",
"C5D2048", "C5D2049", "C5D2051", "C5D2052", "C5D2053", "C5D2054",
"C5D2055", "C5M3001", "C5M3003", "C5M3004", "C5M3005", "C5M3006",
"C5M3007", "C5M3008", "C5M3009", "C5M3010", "C5M3011", "C5M3013",
"C5M3014", "C5M3015", "C5M3016", "C5M3017", "C5M3019", "C5M3020",
"C5M3021", "C5M3022", "C5M3023", "C5M3024", "C5M3029", "C5M3030",
"C5M3031", "C5M3032", "C5M3033", "C5M3034", "C5M3035", "C5M3036",
"C5M3038", "C5M3039", "C5M3042", "C5M3043", "C5M3044", "C5M3046",
"C5M3047", "C5M3048", "C5M3049", "C5M3050", "C5M3051", "C5M3054",
"C5M3055", "C5M3056", "C5M3057", "C5M3058", "C5R4001", "C5R4004",
"C5R4005", "C5R4008", "C5R4009", "C5R4010", "C5R4011", "C5R4012",
"C5R4013", "C5R4014", "C5R4015", "C5R4016", "C5R4017", "C5R4019",
"C5R4020", "C5R4021", "C5R4022", "C5R4024", "C5R4025", "C5R4026",
"C5R4027", "C5R4028", "C5R4031", "C5R4032", "C5R4034", "C5R4037",
"C5R4038", "C5R4040", "C5R4041", "C5R4043", "C5R4048", "C5R4050",
"C5R4053", "C5R4056", "C5W5001", "C5W5002", "C5W5003", "C5W5004",
"C5W5005", "C5W5006", "C5W5007", "C5W5008", "C5W5012", "C5W5013",
"C5W5014", "C5W5015", "C5W5016", "C5W5017", "C5W5018", "C5W5019",
"C5W5020", "C5W5021", "C5W5022", "C5W5023", "C5W5024", "C5W5025",
"C5W5028", "C5W5029", "C5W5030", "C5W5031", "C5W5033", "C5W5035",
"C5W5037", "C5W5038", "C5W5039", "C5W5042", "C5W5043", "C5W5044",
"C5W5045", "C5W5046", "C5W5047", "C5W5048", "C5W5049", "C5W5050",
"C5W5051", "C5W5053", "C5W5054", "C5W5055", "C5W5057", "C5W5058",
"C5W5060"), beta = c(5, 5, 5, 4.84951578282477, 5, 1.75435411010482,
2.59653537897755, 4.58343041388045, 1.19813289503568, 5, 4.41030503473763,
3.48886522319213, 5, 3.69347465973804, 5, 3.61341511433856, 5,
5, 5, 5, 2.82540030433712, 5, 2.01269174411245, 5, 5, 5, 5,
3.66605514409922,
5, 5, 1.20492768779028, 5, 5, 5, 5, 4.71051510737403, 0.973607667104191,
2.13320899798223, 3.55527726960037, 5, 3.13840519694586, 5,
4.33164972914231,
3.2716034981509, 5, 3.59865983897491, 5, 5, 2.98982117172486,
3.15884653708899, 5, 1.21006283114433, 1.88594293315325, 2.37248899411035,
2.40289344741545, 0.262839947401338, 2.89061041570249, 2.98573373614306,
2.82385009686039, 1.78295361666595, 4.27268021897288, 5, 5, 5,
2.52131830224533, 5, 2.32463450150955, 5, 5, 2.18297518836912,
5, 2.53256388646574, 5, 5, 5, 1.11901989122708, 1.56266936421015,
5, 2.1480772866684, 1.03201411339444, 3.22476904165877, 5,
2.23963439946338,
5, 3.85477002456212, 5, 5, 3.15602152904957, 4.81306354520538,
1.20566795082516, 5, 5, 5, 5, 3.04288106123443, 5, 4.06490230904187,
3.06547070051755, 5, 2.5258266208828, 3.52552152448218, 0.0968896467078101,
5, 5, 5, 5, 5, 5, 5, 4.99152057373263, 5, 5, 5, 1.1311501363613,
1.28951722667904, 0.001, 5, 4.58718394461838, 1.22231984982818,
5, 5, 3.35873683772968, 5, 3.87156907439221, 4.8859664986002,
5, 5, 0.976932521703834, 5, 4.50479324287729, 4.65093425894735,
4.22173593981599, 3.15590632469025, 4.86144574792365, 3.39926845337078,
1.24825519695535, 5, 3.27167737085564, 2.2107731064995, 0.187339326704238,
5, 5, 2.78773672362584, 0.977242332964066, 1.05162966383033,
4.24031503174416, 1.9558880208883, 4.01331863994726, 5, 5,
4.50553723427244,
4.03830955873134, 0.0731678404955063, 0.326005643499137, 1.48169477386196,
5, 5, 2.1217771592687, 1.55381162571676, 5, 0.388739153131157,
5, 5, 1.22549904356884, 4.30605773910623, 5, 5, 3.90617032103214,
0.884096418271427, 1.7166358084411, 4.26908188373059, 1.97226101693004,
5, 0.831616239014777, 0.001, 4.15065454327444, 5, 5, 2.6582186770924,
4.69752970800906, 4.50106281557844, 4.21353152726281, 5, 0.620184007188853,
5, 3.86897558241413, 3.63483407688021, 3.18900423687508, 1.24002620770954,
5, 5, 5, 5, 5, 1.20112016323594, 1.99534703415304, 5, 2.13269987318149,
3.76529884137316, 2.88523566628984, 1.93828880175044, 5, 1.04561250178734,
3.74875347444577, 5, 5, 2.48460418075441, 5, 4.55602711347155,
3.97926864514993, 3.59636722716411, 5, 2.95039073432615, 4.82668935707021,
3.70517802450053), group = structure(c(1L, 1L, 1L, 1L, 2L, 3L,
3L, 2L, 3L, 2L, 3L, 4L, 4L, 4L, 2L, 2L, 1L, 3L, 3L, 1L, 3L, 1L,
3L, 2L, 3L, 3L, 3L, 2L, 4L, 2L, 3L, 4L, 1L, 2L, 1L, 1L, 2L, 1L,
2L, 4L, 2L, 1L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 3L, 2L, 4L, 4L, 4L,
2L, 2L, 2L, 2L, 2L, 4L, 4L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 4L, 3L,
1L, 4L, 2L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 2L, 4L, 1L, 3L, 1L,
2L, 2L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 3L, 3L, 3L, 4L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 1L, 1L, 1L, 4L, 1L, 4L, 4L, 3L, 3L, 3L, 2L,
4L, 2L, 4L, 3L, 4L, 2L, 2L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 2L,
3L, 1L, 4L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 4L,
2L, 2L, 3L, 2L, 1L, 1L, 3L, 2L, 2L, 1L, 3L, 4L, 1L, 1L, 4L, 1L,
3L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 4L, 4L, 2L, 3L,
2L, 2L, 3L, 4L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 3L, 2L, 3L, 2L,
2L, 4L, 2L, 4L, 4L, 1L, 1L, 1L, 3L, 1L, 1L, 4L, 4L, 1L, 1L, 4L,
4L), .Label = c("1", "2", "3", "4"), class = "factor"), cpz = c(0,
0, 0, 0, 200, 220, 262.5, 1200, 519.6, 450, 400, 0, 780, 0, 960,
750, 0, 450, 262.5, 0, 910, 0, 236.156156156156, 400, 1120, 300,
599.8, 820, 300, 89.5, 266.6, 200, 0, 262.5, 0, 0, 640, 0, 302.4,
600, 600, 0, 750, 200, 149.925037481259, 0, 100, 0, 0, 788.666666666667,
500, 560, 0, 300, 350, 0, 700, 600, 100, 0, 200, 0, 0, 0, 240,
0, 0, 520, 0, 0, 0, 286.666666666667, 160, 0, 320, 360, 720,
16, 0, 200, 680, 200, 50, 0, 900, 0, 150, 300, 400, 0, 0, 4714.2,
0, 0, 0, 100, 300, 0, 480, 600, 300, 0, 450, 450, 1000, 300,
899.850074962519, 0, 0, 0, 300, 0, 0, 0, 600, 0, 120, 1574.76,
400, 1200, 0, 1240, 10, 0, 450, 300, 0, 450, 0, 0, 0, 0, 0, 600,
300, 0, 600, 346.666666666667, 320, 1050, 0, 0, 450, 6000, 300,
400, 0, 1050, 300, 200, 300, 1050.24, 450, 450, 0, 0, 225, 400,
500, 0, 6000, 750, 0, 0, 300, 0, 2140, 300, 320, 400, 800,
766.666666666667,
0, 2960, 1200, 200, 640, 640, 198, 600, 600, 0, 600, 666.666666666667,
80, 150, 200, 0, 0, 0, 0, 299.850074962519, 0, 0, 200, 400, 600,
800, 0, 0, 1342.66666666667, 0, 0, 0, 0, 0, 640, 0, 0, 120, 280,
0, 0, 0, 200)), class = "data.frame", row.names = c(NA, -215L
))
--
Edward Patzelt, PhD
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