To my surprise, this test comes back with significant p-values for each factor-level combination, seemingly contradicting the boxplot (ie the boxplox shows overlapping CISDs for the bank and shelf habitats).
To my surprise, this test comes back with significant p-values for each factor-level combination, seemingly contradicting the boxplot (ie the boxplox shows overlapping CI for the bank and shelf habitats).
To my surprise, this test comes back with significant p-values for each factor-level combination, seemingly contradicting the boxplot (ie the boxplox shows overlapping SDs for the bank and shelf habitats).
1-way ANOVA contradicts boxplot?
I'm trying to find out if there's a significant difference between relative abundance (response variable: rel.abund) and habitat (predictor variable with levels lagoon, bank, shelf: Habitat).
This is my data set
pr_uneq_smme <- structure(list(run_number = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L,
22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L,
35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L,
48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L,
61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L,
74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L,
87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L,
100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L,
40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L,
53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L,
66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L,
79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L,
92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L,
32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L,
58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L,
71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L,
84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L,
97L, 98L, 99L, 100L), time_step = c(5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L,
5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L, 5000L), Species = c("Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator", "Small mesopredator",
"Small mesopredator", "Small mesopredator"), Habitat = c("lagoon",
"lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon",
"lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon",
"lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon",
"lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon",
"lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon",
"lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon",
"lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon",
"lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon",
"lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon",
"lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon",
"lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon",
"lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon",
"lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon",
"lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon", "lagoon",
"lagoon", "bank", "bank", "bank", "bank", "bank", "bank", "bank",
"bank", "bank", "bank", "bank", "bank", "bank", "bank", "bank",
"bank", "bank", "bank", "bank", "bank", "bank", "bank", "bank",
"bank", "bank", "bank", "bank", "bank", "bank", "bank", "bank",
"bank", "bank", "bank", "bank", "bank", "bank", "bank", "bank",
"bank", "bank", "bank", "bank", "bank", "bank", "bank", "bank",
"bank", "bank", "bank", "bank", "bank", "bank", "bank", "bank",
"bank", "bank", "bank", "bank", "bank", "bank", "bank", "bank",
"bank", "bank", "bank", "bank", "bank", "bank", "bank", "bank",
"bank", "bank", "bank", "bank", "bank", "bank", "bank", "bank",
"bank", "bank", "bank", "bank", "bank", "bank", "bank", "bank",
"bank", "bank", "bank", "bank", "bank", "bank", "bank", "bank",
"bank", "bank", "bank", "bank", "bank", "shelf", "shelf", "shelf",
"shelf", "shelf", "shelf", "shelf", "shelf", "shelf", "shelf",
"shelf", "shelf", "shelf", "shelf", "shelf", "shelf", "shelf",
"shelf", "shelf", "shelf", "shelf", "shelf", "shelf", "shelf",
"shelf", "shelf", "shelf", "shelf", "shelf", "shelf", "shelf",
"shelf", "shelf", "shelf", "shelf", "shelf", "shelf", "shelf",
"shelf", "shelf", "shelf", "shelf", "shelf", "shelf", "shelf",
"shelf", "shelf", "shelf", "shelf", "shelf", "shelf", "shelf",
"shelf", "shelf", "shelf", "shelf", "shelf", "shelf", "shelf",
"shelf", "shelf", "shelf", "shelf", "shelf", "shelf", "shelf",
"shelf", "shelf", "shelf", "shelf", "shelf", "shelf", "shelf",
"shelf", "shelf", "shelf", "shelf", "shelf", "shelf", "shelf",
"shelf", "shelf", "shelf", "shelf", "shelf", "shelf", "shelf",
"shelf", "shelf", "shelf", "shelf", "shelf", "shelf", "shelf",
"shelf", "shelf", "shelf", "shelf", "shelf", "shelf"), value = c(238L,
270L, 226L, 258L, 275L, 285L, 297L, 260L, 267L, 267L, 264L, 267L,
236L, 283L, 284L, 254L, 254L, 270L, 275L, 246L, 256L, 284L, 255L,
271L, 262L, 244L, 240L, 263L, 261L, 264L, 259L, 276L, 289L, 256L,
247L, 276L, 260L, 260L, 273L, 254L, 286L, 269L, 277L, 266L, 271L,
263L, 279L, 251L, 274L, 261L, 255L, 279L, 263L, 249L, 270L, 266L,
277L, 275L, 251L, 264L, 248L, 256L, 263L, 271L, 259L, 253L, 255L,
255L, 258L, 282L, 281L, 283L, 311L, 250L, 250L, 258L, 264L, 286L,
275L, 285L, 253L, 255L, 265L, 294L, 283L, 262L, 285L, 267L, 293L,
265L, 293L, 263L, 272L, 269L, 279L, 241L, 261L, 263L, 247L, 276L,
217L, 153L, 204L, 199L, 203L, 177L, 164L, 188L, 181L, 185L, 203L,
192L, 208L, 194L, 199L, 188L, 198L, 205L, 180L, 207L, 219L, 178L,
186L, 192L, 205L, 215L, 211L, 215L, 210L, 170L, 163L, 211L, 176L,
208L, 167L, 167L, 188L, 191L, 165L, 185L, 199L, 169L, 203L, 193L,
187L, 188L, 191L, 196L, 189L, 168L, 192L, 188L, 176L, 177L, 196L,
198L, 176L, 179L, 185L, 209L, 212L, 185L, 177L, 168L, 183L, 196L,
189L, 191L, 198L, 166L, 227L, 162L, 165L, 196L, 176L, 192L, 171L,
175L, 207L, 178L, 218L, 200L, 192L, 192L, 186L, 178L, 203L, 194L,
168L, 223L, 183L, 197L, 204L, 181L, 177L, 202L, 191L, 173L, 191L,
209L, 169L, 201L, 176L, 153L, 145L, 182L, 157L, 163L, 174L, 176L,
162L, 176L, 175L, 164L, 159L, 175L, 167L, 164L, 178L, 174L, 160L,
156L, 190L, 165L, 156L, 151L, 175L, 158L, 157L, 174L, 184L, 144L,
161L, 177L, 192L, 175L, 162L, 160L, 185L, 166L, 141L, 177L, 148L,
156L, 156L, 161L, 151L, 166L, 149L, 177L, 176L, 173L, 185L, 192L,
153L, 161L, 185L, 182L, 173L, 148L, 164L, 178L, 167L, 160L, 170L,
178L, 180L, 154L, 157L, 164L, 139L, 162L, 166L, 171L, 195L, 170L,
198L, 166L, 151L, 176L, 152L, 170L, 175L, 152L, 158L, 172L, 139L,
153L, 162L, 122L, 161L, 154L, 136L, 157L, 160L, 174L, 167L, 180L,
197L, 143L), PR = c("Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal", "Unequal", "Unequal", "Unequal", "Unequal",
"Unequal", "Unequal"), total_predators = c(624L, 624L, 606L,
610L, 623L, 644L, 618L, 611L, 622L, 628L, 629L, 635L, 619L, 641L,
642L, 617L, 619L, 639L, 633L, 627L, 635L, 618L, 631L, 628L, 623L,
610L, 626L, 636L, 628L, 608L, 606L, 631L, 626L, 641L, 606L, 618L,
610L, 611L, 623L, 605L, 626L, 615L, 628L, 615L, 614L, 612L, 621L,
613L, 612L, 606L, 623L, 640L, 624L, 618L, 619L, 625L, 638L, 636L,
609L, 621L, 624L, 619L, 607L, 599L, 612L, 627L, 624L, 600L, 613L,
612L, 647L, 607L, 642L, 617L, 621L, 620L, 633L, 627L, 633L, 639L,
623L, 625L, 632L, 638L, 627L, 612L, 627L, 614L, 623L, 610L, 637L,
614L, 612L, 607L, 616L, 617L, 619L, 616L, 635L, 628L, 624L, 624L,
606L, 610L, 623L, 644L, 618L, 611L, 622L, 628L, 629L, 635L, 619L,
641L, 642L, 617L, 619L, 639L, 633L, 627L, 635L, 618L, 631L, 628L,
623L, 610L, 626L, 636L, 628L, 608L, 606L, 631L, 626L, 641L, 606L,
618L, 610L, 611L, 623L, 605L, 626L, 615L, 628L, 615L, 614L, 612L,
621L, 613L, 612L, 606L, 623L, 640L, 624L, 618L, 619L, 625L, 638L,
636L, 609L, 621L, 624L, 619L, 607L, 599L, 612L, 627L, 624L, 600L,
613L, 612L, 647L, 607L, 642L, 617L, 621L, 620L, 633L, 627L, 633L,
639L, 623L, 625L, 632L, 638L, 627L, 612L, 627L, 614L, 623L, 610L,
637L, 614L, 612L, 607L, 616L, 617L, 619L, 616L, 635L, 628L, 624L,
624L, 606L, 610L, 623L, 644L, 618L, 611L, 622L, 628L, 629L, 635L,
619L, 641L, 642L, 617L, 619L, 639L, 633L, 627L, 635L, 618L, 631L,
628L, 623L, 610L, 626L, 636L, 628L, 608L, 606L, 631L, 626L, 641L,
606L, 618L, 610L, 611L, 623L, 605L, 626L, 615L, 628L, 615L, 614L,
612L, 621L, 613L, 612L, 606L, 623L, 640L, 624L, 618L, 619L, 625L,
638L, 636L, 609L, 621L, 624L, 619L, 607L, 599L, 612L, 627L, 624L,
600L, 613L, 612L, 647L, 607L, 642L, 617L, 621L, 620L, 633L, 627L,
633L, 639L, 623L, 625L, 632L, 638L, 627L, 612L, 627L, 614L, 623L,
610L, 637L, 614L, 612L, 607L, 616L, 617L, 619L, 616L, 635L, 628L
), rel.abund = c(38.1410256410256, 43.2692307692308, 37.2937293729373,
42.2950819672131, 44.1412520064205, 44.2546583850932, 48.0582524271845,
42.5531914893617, 42.9260450160772, 42.515923566879, 41.9713831478537,
42.0472440944882, 38.1260096930533, 44.1497659906396, 44.2367601246106,
41.1669367909238, 41.0339256865913, 42.2535211267606, 43.4439178515008,
39.2344497607655, 40.3149606299213, 45.9546925566343, 40.4120443740095,
43.1528662420382, 42.0545746388443, 40, 38.3386581469649, 41.3522012578616,
41.5605095541401, 43.421052631579, 42.7392739273927, 43.7400950871632,
46.1661341853035, 39.9375975039002, 40.7590759075908, 44.6601941747573,
42.6229508196721, 42.5531914893617, 43.8202247191011, 41.9834710743802,
45.6869009584665, 43.739837398374, 44.1082802547771, 43.2520325203252,
44.1368078175896, 42.9738562091503, 44.9275362318841, 40.9461663947798,
44.7712418300654, 43.0693069306931, 40.9309791332263, 43.59375,
42.1474358974359, 40.2912621359223, 43.6187399030695, 42.56,
43.4169278996865, 43.2389937106918, 41.2151067323481, 42.512077294686,
39.7435897435897, 41.3570274636511, 43.32784184514, 45.2420701168614,
42.3202614379085, 40.3508771929825, 40.8653846153846, 42.5, 42.0880913539967,
46.078431372549, 43.4312210200927, 46.6227347611203, 48.4423676012461,
40.5186385737439, 40.2576489533011, 41.6129032258065, 41.7061611374408,
45.6140350877193, 43.4439178515008, 44.6009389671362, 40.6099518459069,
40.8, 41.9303797468354, 46.0815047021944, 45.1355661881978, 42.8104575163399,
45.4545454545455, 43.485342019544, 47.0304975922953, 43.4426229508197,
45.9968602825746, 42.8338762214984, 44.4444444444444, 44.3163097199341,
45.2922077922078, 39.0599675850891, 42.1647819063005, 42.6948051948052,
38.8976377952756, 43.9490445859873, 34.775641025641, 24.5192307692308,
33.6633663366337, 32.6229508196721, 32.5842696629214, 27.4844720496894,
26.537216828479, 30.7692307692308, 29.0996784565916, 29.4585987261146,
32.2734499205087, 30.2362204724409, 33.6025848142165, 30.2652106084243,
30.9968847352025, 30.4700162074554, 31.9870759289176, 32.0813771517997,
28.436018957346, 33.0143540669856, 34.488188976378, 28.8025889967638,
29.4770206022187, 30.5732484076433, 32.9052969502408, 35.2459016393443,
33.7060702875399, 33.8050314465409, 33.4394904458599, 27.9605263157895,
26.8976897689769, 33.4389857369255, 28.1150159744409, 32.4492979719189,
27.5577557755776, 27.0226537216829, 30.8196721311475, 31.2602291325696,
26.4847512038523, 30.5785123966942, 31.7891373801917, 27.479674796748,
32.3248407643312, 31.3821138211382, 30.4560260586319, 30.718954248366,
30.7568438003221, 31.973898858075, 30.8823529411765, 27.7227722772277,
30.8186195826645, 29.375, 28.2051282051282, 28.6407766990291,
31.6639741518578, 31.68, 27.5862068965517, 28.1446540880503,
30.3776683087028, 33.6553945249597, 33.974358974359, 29.8869143780291,
29.159802306425, 28.0467445742905, 29.9019607843137, 31.2599681020734,
30.2884615384615, 31.8333333333333, 32.300163132137, 27.1241830065359,
35.0850077279753, 26.6886326194399, 25.7009345794392, 31.7666126418152,
28.341384863124, 30.9677419354839, 27.0142180094787, 27.9106858054226,
32.7014218009479, 27.8560250391236, 34.991974317817, 32, 30.379746835443,
30.0940438871473, 29.6650717703349, 29.0849673202614, 32.3763955342903,
31.5960912052117, 26.9662921348315, 36.5573770491803, 28.7284144427002,
32.0846905537459, 33.3333333333333, 29.8187808896211, 28.7337662337662,
32.7390599675851, 30.8562197092084, 28.0844155844156, 30.0787401574803,
33.2802547770701, 27.0833333333333, 32.2115384615385, 29.042904290429,
25.0819672131148, 23.2744783306581, 28.2608695652174, 25.4045307443366,
26.6775777414075, 27.9742765273312, 28.0254777070064, 25.7551669316375,
27.7165354330709, 28.2714054927302, 25.585023400936, 24.7663551401869,
28.3630470016207, 26.9789983844911, 25.6651017214397, 28.1200631911532,
27.7511961722488, 25.1968503937008, 25.2427184466019, 30.1109350237718,
26.2738853503185, 25.0401284109149, 24.7540983606557, 27.9552715654952,
24.8427672955975, 25, 28.6184210526316, 30.3630363036304, 22.8209191759113,
25.7188498402556, 27.613104524181, 31.6831683168317, 28.3171521035599,
26.5573770491803, 26.1865793780687, 29.6950240770466, 27.4380165289256,
22.5239616613419, 28.780487804878, 23.5668789808917, 25.3658536585366,
25.4071661237785, 26.3071895424837, 24.3156199677939, 27.0799347471452,
24.3464052287582, 29.2079207920792, 28.2504012841091, 27.03125,
29.6474358974359, 31.0679611650485, 24.7172859450727, 25.76,
28.9968652037618, 28.6163522012579, 28.4072249589491, 23.8325281803543,
26.2820512820513, 28.7560581583199, 27.5123558484349, 26.7111853088481,
27.7777777777778, 28.3891547049442, 28.8461538461538, 25.6666666666667,
25.6117455138662, 26.797385620915, 21.483771251932, 26.6886326194399,
25.8566978193146, 27.7147487844408, 31.4009661835749, 27.4193548387097,
31.2796208530806, 26.4752791068581, 23.8546603475513, 27.5430359937402,
24.3980738362761, 27.2, 27.6898734177215, 23.8244514106583, 25.1993620414673,
28.1045751633987, 22.1690590111643, 24.9185667752443, 26.0032102728732,
20, 25.2747252747253, 25.0814332247557, 22.2222222222222, 25.8649093904448,
25.974025974026, 28.2009724473258, 26.9789983844911, 29.2207792207792,
31.0236220472441, 22.7707006369427)), row.names = c(NA, -300L
), class = "data.frame")
whose first 6 rows look like this
run_number time_step Species Habitat value PR total_predators rel.abund
1 1 5000 Small mesopredator lagoon 238 Unequal 624 38.14103
2 2 5000 Small mesopredator lagoon 270 Unequal 624 43.26923
3 3 5000 Small mesopredator lagoon 226 Unequal 606 37.29373
4 4 5000 Small mesopredator lagoon 258 Unequal 610 42.29508
5 5 5000 Small mesopredator lagoon 275 Unequal 623 44.14125
6 6 5000 Small mesopredator lagoon 285 Unequal 644 44.25466
I first visualized the data using a boxplot
[![boxplot(rel.abund~Habitat, data = pr_uneq_smme)][1]][1]
I then ran a 1-way anova as follows
res.aov <- aov(rel.abund ~ Habitat, data = pr_uneq_smme)
summary(res.aov)
The anova comes back with a significant p-value, so i then run a TukeyHSD
To my surprise, this test comes back with significant p-values for each factor-level combination, seemingly contradicting the boxplot (ie the boxplox shows overlapping CI for the bank and shelf habitats).
I evaluated model assumptions for running this 1-way anova, but these seem to be honored by my data
# 1. Homogeneity of variances
plot(res.aov, 1)
library(car); leveneTest(resid(res.aov)~Habitat, data = pr_uneq_smme) # p = 0.31: fine
# Bartlett's test
bartlett.test(resid(res.aov)~Habitat, data = pr_uneq_smme) # p = 0.51: fine
### Check the normality assumption
plot(res.aov, 2)
# Extract the residuals
aov_residuals <- residuals(object = res.aov )
# Run Shapiro-Wilk test
shapiro.test(x = aov_residuals)
So, my question is can I trust this analysis? Or did I go wrong somewhere?
lang-r