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- pups.alt.NONA<-structure(list(ID_mum = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L,
- 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
- 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
- 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
- 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
- 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
- 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
- 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L,
- 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
- 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
- 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
- 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
- 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
- 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
- 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L,
- 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
- 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
- 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
- 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
- 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
- 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
- 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
- 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
- 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
- 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
- 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
- 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
- 23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
- 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
- 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
- 25L, 25L, 25L, 25L, 25L, 25L, 25L, 26L, 26L, 26L, 26L, 26L, 26L,
- 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
- 26L, 26L, 26L, 26L, 26L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L,
- 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L,
- 28L, 28L, 28L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
- 30L, 30L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L,
- 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L,
- 31L, 31L, 31L, 31L), .Label = c("2215", "2304", "2334", "2348",
- "2367", "2456", "2462", "2505", "2525", "2601", "2609", "2610",
- "2611", "2612", "2614", "2616", "2617", "2618", "2621", "2622",
- "2623", "2625", "2636", "2638", "2639", "2642", "2644", "Arjane",
- "Jos", "Teemu", "Turre"), class = "factor"), ID_pup = structure(c(17L,
- 17L, 17L, 17L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 20L, 20L,
- 20L, 20L, 19L, 19L, 19L, 19L, 18L, 18L, 18L, 18L, 23L, 23L, 23L,
- 23L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 126L, 126L, 126L,
- 126L, 53L, 53L, 53L, 53L, 54L, 54L, 54L, 54L, 55L, 55L, 55L,
- 55L, 70L, 70L, 70L, 70L, 66L, 66L, 66L, 66L, 67L, 67L, 67L, 67L,
- 68L, 68L, 68L, 68L, 84L, 84L, 84L, 84L, 85L, 85L, 85L, 85L, 86L,
- 86L, 86L, 86L, 87L, 87L, 87L, 87L, 88L, 88L, 88L, 88L, 120L,
- 120L, 120L, 120L, 121L, 121L, 121L, 121L, 122L, 122L, 122L, 123L,
- 123L, 123L, 123L, 125L, 125L, 125L, 125L, 127L, 127L, 127L, 127L,
- 128L, 128L, 128L, 128L, 129L, 129L, 129L, 129L, 130L, 130L, 130L,
- 130L, 131L, 131L, 131L, 131L, 78L, 78L, 78L, 78L, 79L, 79L, 79L,
- 79L, 80L, 80L, 80L, 80L, 81L, 81L, 81L, 81L, 82L, 82L, 82L, 82L,
- 83L, 83L, 83L, 83L, 31L, 31L, 31L, 31L, 32L, 32L, 32L, 32L, 33L,
- 33L, 33L, 33L, 34L, 34L, 34L, 34L, 35L, 35L, 35L, 35L, 36L, 36L,
- 36L, 36L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 62L, 62L, 62L,
- 62L, 63L, 63L, 63L, 63L, 64L, 64L, 64L, 64L, 65L, 65L, 65L, 65L,
- 26L, 26L, 26L, 26L, 27L, 27L, 27L, 27L, 43L, 43L, 43L, 43L, 44L,
- 44L, 44L, 44L, 45L, 45L, 45L, 45L, 46L, 46L, 46L, 46L, 100L,
- 100L, 100L, 100L, 101L, 101L, 101L, 101L, 102L, 102L, 102L, 102L,
- 103L, 103L, 103L, 103L, 104L, 104L, 104L, 104L, 105L, 105L, 105L,
- 105L, 106L, 106L, 106L, 106L, 37L, 37L, 37L, 37L, 38L, 38L, 38L,
- 38L, 39L, 39L, 39L, 39L, 40L, 40L, 40L, 40L, 41L, 41L, 41L, 41L,
- 132L, 132L, 132L, 132L, 133L, 133L, 133L, 133L, 134L, 134L, 134L,
- 134L, 135L, 135L, 135L, 135L, 29L, 29L, 29L, 29L, 30L, 30L, 30L,
- 30L, 28L, 28L, 28L, 28L, 42L, 42L, 42L, 42L, 47L, 47L, 47L, 47L,
- 48L, 48L, 48L, 48L, 49L, 49L, 49L, 49L, 50L, 50L, 50L, 50L, 51L,
- 51L, 51L, 51L, 52L, 52L, 52L, 52L, 9L, 9L, 9L, 9L, 8L, 8L, 8L,
- 8L, 14L, 14L, 14L, 14L, 13L, 13L, 13L, 13L, 11L, 11L, 11L, 11L,
- 10L, 10L, 10L, 10L, 12L, 12L, 12L, 12L, 4L, 4L, 4L, 4L, 6L, 6L,
- 6L, 6L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 5L, 5L, 5L, 5L, 7L, 7L,
- 7L, 7L, 110L, 110L, 110L, 110L, 111L, 111L, 111L, 111L, 112L,
- 112L, 112L, 112L, 113L, 113L, 113L, 113L, 117L, 117L, 117L, 117L,
- 118L, 118L, 118L, 118L, 119L, 119L, 119L, 119L, 140L, 140L, 140L,
- 140L, 141L, 141L, 141L, 141L, 124L, 124L, 124L, 124L, 136L, 136L,
- 136L, 136L, 137L, 137L, 137L, 138L, 138L, 138L, 138L, 139L, 139L,
- 139L, 139L, 89L, 89L, 89L, 89L, 90L, 90L, 90L, 90L, 91L, 91L,
- 91L, 91L, 92L, 92L, 92L, 92L, 93L, 93L, 93L, 93L, 114L, 114L,
- 114L, 114L, 116L, 116L, 116L, 116L, 1L, 1L, 1L, 1L, 69L, 69L,
- 69L, 69L, 115L, 115L, 115L, 115L, 56L, 56L, 56L, 56L, 57L, 57L,
- 57L, 57L, 58L, 58L, 58L, 58L, 59L, 59L, 59L, 59L, 60L, 60L, 60L,
- 60L, 61L, 61L, 61L, 61L, 94L, 94L, 94L, 94L, 95L, 95L, 95L, 95L,
- 96L, 96L, 96L, 96L, 97L, 97L, 97L, 97L, 98L, 98L, 98L, 98L, 99L,
- 99L, 99L, 99L, 107L, 107L, 107L, 107L, 108L, 108L, 108L, 108L,
- 109L, 109L, 109L, 109L, 71L, 71L, 71L, 71L, 72L, 72L, 72L, 72L,
- 73L, 73L, 73L, 73L, 74L, 74L, 74L, 74L, 75L, 75L, 75L, 75L, 76L,
- 76L, 76L, 76L, 77L, 77L, 77L, 77L), .Label = c("2201", "2202",
- "2203", "2204", "2205", "2206", "2207", "2208", "2209", "2210",
- "2211", "2212", "2213", "2214", "2216", "2217", "2218", "2219",
- "2220", "2221", "2224", "2225", "2301", "2302", "2303", "2305",
- "2306", "2307", "2308", "2309", "2310", "2311", "2312", "2313",
- "2314", "2315", "2316", "2317", "2318", "2319", "2320", "2321",
- "2322", "2323", "2324", "2325", "2326", "2327", "2328", "2329",
- "2330", "2331", "2332", "2333", "2335", "2336", "2337", "2338",
- "2339", "2340", "2341", "2342", "2343", "2344", "2345", "2346",
- "2347", "2349", "2350", "2351", "2352", "2353", "2355", "2356",
- "2357", "2358", "2359", "2360", "2361", "2362", "2363", "2364",
- "2365", "2366", "2368", "2369", "2370", "2371", "2372", "2373",
- "2374", "2375", "2376", "2377", "2379", "2380", "2381", "2382",
- "2383", "2384", "2385", "2386", "2387", "2388", "2389", "2390",
- "2391", "2392", "2393", "2394", "2395", "2396", "2397", "2398",
- "2399", "2400", "2451", "2453", "2454", "2455", "2457", "2458",
- "2459", "2460", "2461", "2463", "2464", "2465", "2466", "2467",
- "2468", "2469", "2470", "2471", "2472", "2473", "2474", "2475",
- "2482", "2483", "2484", "2505_1", "2601_2", "2601_3", "2601_4",
- "2616_2", "2636_6", "2644_1", "2644_2", "2644_3", "2644_4", "2644_5",
- "2644_6", "Arjane_1", "Jos_1", "Jos_2", "Jos_3", "Jos_4", "Jos_5",
- "Jos_6", "Jos_7", "Teemu_1", "Teemu_3", "Teemu_4", "Teemu_7"), class = "factor"),
- Day = c(0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L,
- 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L,
- 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L,
- 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L,
- 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L,
- 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L,
- 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L,
- 6L, 13L, 20L, 0L, 6L, 13L, 20L, 6L, 13L, 20L, 0L, 6L, 13L,
- 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L,
- 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L,
- 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L,
- 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L,
- 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L,
- 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L,
- 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L,
- 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L,
- 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L,
- 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L,
- 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L,
- 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L,
- 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L,
- 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L,
- 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L,
- 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L,
- 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L,
- 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L,
- 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L,
- 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L,
- 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L,
- 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L,
- 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L,
- 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L,
- 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 6L, 13L, 20L, 0L,
- 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L,
- 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L,
- 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L,
- 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L,
- 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L,
- 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L,
- 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L,
- 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L,
- 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L,
- 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L, 20L, 0L, 6L, 13L,
- 20L, 0L, 6L, 13L, 20L), Weight = c(1.68, 4.2, 7.1, 10.56,
- 1.72, 4.23, 6.77, 10.58, 1.74, 4.34, 6.7, 10.41, 1.83, 4.33,
- 6.18, 9.89, 1.81, 4.25, 6.7, 10.89, 1.72, 4.3, 6.73, 10.62,
- 1.71, 4.49, 7.45, 11.34, 1.79, 4.38, 7.39, 11.9, 1.71, 4.59,
- 7.45, 11.56, 1.52, 4.24, 7.17, 10.66, 1.79, 4.05, 7.07, 9.98,
- 1.79, 3.16, 7.12, 10.49, 1.67, 4.03, 7.26, 10.61, 1.63, 4.2,
- 7.66, 10.94, 1.89, 5.18, 9.53, 13.93, 2.1, 5.1, 9.47, 13.15,
- 1.93, 5.24, 9.07, 12.23, 1.53, 3.52, 6.41, 10.68, 1.61, 4.32,
- 6.95, 11.88, 1.8, 3.96, 6.17, 9.38, 1.75, 4.16, 6.71, 10.9,
- 1.63, 4.22, 6.61, 10.64, 1.41, 3.79, 5.9, 8.6, 1.6, 3.46,
- 6.27, 10.27, 3.75, 6.63, 10.4, 1.4, 3.2, 5.83, 9.17, 1.86,
- 4.02, 5.88, 8.94, 1.78, 4.04, 6.6, 10.62, 1.81, 3.92, 6.36,
- 9.49, 1.89, 4.12, 6.57, 10.02, 1.71, 4.44, 6.67, 10.72, 1.95,
- 4.12, 6.27, 9.97, 0.93, 3.34, 6.57, 10.03, 1.87, 3.22, 6.36,
- 10.15, 1.58, 3.18, 6.31, 10.3, 1.61, 3.66, 7.18, 10.82, 1.52,
- 3.39, 6.57, 9.93, 1.69, 3.45, 6.72, 10.41, 1.7, 3.29, 6.08,
- 9.56, 1.43, 3.51, 6.21, 9.98, 1.57, 3.78, 6.61, 11.48, 1.64,
- 3.76, 6.29, 10.34, 1.56, 3.46, 6.17, 10.25, 1.58, 3.65, 6.3,
- 9.59, 1.87, 4.23, 6.55, 9.75, 1.83, 4.19, 6.45, 9.39, 1.92,
- 4.99, 7.97, 13.22, 1.81, 5.66, 9.1, 14.26, 2.3, 4.78, 8.16,
- 12.08, 1.84, 4.96, 8.13, 12.77, 1.49, 4.14, 6.56, 10.62,
- 1.53, 3.89, 6.33, 9.47, 1.66, 3.64, 5.49, 8.91, 1.44, 3.93,
- 6.38, 11.29, 1.62, 4.17, 6.37, 10.33, 1.61, 3.73, 5.84, 9.38,
- 1.58, 3.69, 6.26, 8, 1.78, 3.83, 6.25, 10.27, 1.69, 3.49,
- 5.7, 8.53, 1.5, 3.89, 6.6, 10.47, 1.77, 3.75, 6.38, 10.1,
- 1.68, 3.35, 5.53, 9.73, 1.59, 3.58, 6.09, 9.38, 1.51, 3.95,
- 7.02, 10.6, 1.37, 3.66, 6.6, 9.53, 1.58, 3.6, 6.75, 10.6,
- 1.73, 2.99, 5.91, 9.19, 1.62, 4.09, 7.17, 10.85, 1.79, 4.39,
- 7.6, 11.4, 1.72, 4.21, 7.32, 10.89, 1.81, 4.56, 7.55, 11,
- 1.93, 4.51, 7.76, 11.82, 1.68, 4.06, 7.35, 9.54, 1.84, 4.21,
- 7.26, 10.46, 1.75, 3.72, 6.87, 6.9, 1.86, 4.11, 7.24, 8.23,
- 2.13, 4.89, 8.64, 11.26, 1.61, 4.62, 8.34, 11.27, 2.03, 4.8,
- 8.2, 11.1, 1.91, 4.48, 7.89, 11, 1.7, 4.95, 8.28, 12.03,
- 1.51, 4.2, 7.77, 12.1, 1.73, 3.69, 6.09, 10.73, 1.57, 3.81,
- 6.27, 10.64, 1.56, 3.65, 6.3, 10.3, 1.88, 3.57, 6.01, 10.19,
- 1.63, 3.25, 5.72, 9.52, 1.7, 3.62, 5.99, 10.07, 1.9, 3.57,
- 6.17, 10.31, 1.52, 3.54, 6.69, 9.85, 1.66, 3.48, 6.44, 10.45,
- 1.5, 3.93, 6.77, 9.82, 1.66, 3.98, 7.08, 10.94, 1.49, 3.74,
- 6.58, 10.13, 1.69, 3.54, 6.36, 9.6, 2.06, 4.82, 7.85, 10.95,
- 1.27, 3.68, 6.53, 9.5, 0.99, 4.63, 7.43, 10.89, 1.85, 2.93,
- 5.64, 8.54, 1.85, 4.9, 8.16, 12.25, 1.59, 4.23, 7.47, 11.2,
- 1.83, 4.74, 8.18, 11.93, 1.82, 4.93, 8.23, 11.57, 1.9, 4.86,
- 8.08, 11.56, 1.62, 4.04, 6.87, 9.38, 1.79, 3.84, 6.64, 9.34,
- 4.21, 6.96, 9.68, 1.64, 3.98, 6.76, 9.15, 1.53, 3.62, 6.4,
- 6.72, 1.72, 4.38, 7.89, 11.39, 1.76, 4.26, 7.68, 11.45, 1.36,
- 3.9, 6.97, 8.67, 1.67, 4.51, 8.05, 10.81, 1.43, 4.12, 7.29,
- 10.72, 1.31, 4.32, 7.55, 12.05, 1.55, 3.14, 5.64, 7.37, 1.37,
- 3.93, 7.15, 10.35, 1.54, 3.77, 7.16, 10.86, 1.78, 3.84, 6.77,
- 10.69, 1.61, 3.03, 5.2, 8.09, 1.62, 3.12, 5.31, 8.72, 1.58,
- 3.16, 5.61, 9.7, 1.57, 3.08, 5.35, 8.9, 1.53, 3.02, 5.5,
- 8.88, 1.59, 3.22, 5.6, 9.02, 1.09, 3.05, 5.31, 9, 1.52, 3.29,
- 5.39, 8.97, 1.38, 3.12, 5.48, 8.92, 1.39, 3.56, 5.82, 9.9,
- 1.35, 3.35, 5.61, 9.13, 1.19, 2.74, 4.88, 8.27, 1.63, 3.87,
- 6.57, 9.14, 1.26, 4.06, 6.58, 8.9, 1.5, 3.87, 6.39, 7.83,
- 1.96, 4.01, 6.66, 9.94, 1.75, 3.39, 5.22, 7.58, 1.8, 3.62,
- 6.07, 9.29, 1.81, 3.88, 6.27, 9.96, 1.63, 4.1, 6.76, 10.24,
- 1.95, 4.07, 6.89, 10.53, 1.75, 3.78, 6.38, 9.66), Treatment = structure(c(2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L,
- 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("C", "PT", "AP"), class = "factor"),
- Site = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("RM",
- "S"), class = "factor"), Enclosure = structure(c(8L, 8L,
- 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
- 8L, 8L, 8L, 8L, 8L, 8L, 8L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 7L, 7L, 7L, 7L, 7L, 7L,
- 7L, 7L, 7L, 7L, 7L, 7L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
- 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 8L,
- 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
- 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
- 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L,
- 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
- 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 8L, 8L, 8L, 8L, 8L, 8L,
- 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
- 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
- 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 7L, 7L, 7L,
- 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 5L, 5L,
- 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
- 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
- 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
- 5L, 5L, 5L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
- 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 4L, 4L, 4L,
- 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
- 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
- 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
- 1L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
- 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 4L, 4L, 4L, 4L, 4L,
- 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 3L, 3L, 3L), .Label = c("RM1", "RM2", "RM5", "RM6",
- "RM7", "S3", "S6", "S8"), class = "factor"), Litter_size = c(6L,
- 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
- 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
- 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
- 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L,
- 3L, 3L, 3L, 3L, 3L, 3L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
- 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L,
- 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L,
- 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
- 6L, 6L, 6L, 6L, 6L, 6L, 7L, 6L, 6L, 6L, 7L, 6L, 6L, 6L, 7L,
- 6L, 6L, 6L, 7L, 6L, 6L, 6L, 7L, 6L, 6L, 6L, 7L, 6L, 6L, 6L,
- 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
- 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 2L, 5L, 5L,
- 5L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
- 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
- 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L,
- 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
- 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 5L, 5L, 5L, 5L, 5L,
- 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
- 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
- 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
- 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L,
- 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 7L, 7L, 7L, 7L,
- 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
- 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 6L, 6L, 6L, 6L, 6L, 6L,
- 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
- 6L, 6L, 6L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
- 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
- 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 5L, 6L, 6L,
- 6L, 5L, 6L, 6L, 5L, 6L, 6L, 6L, 5L, 6L, 6L, 6L, 5L, 5L, 5L,
- 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
- 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
- 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
- 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
- 6L, 6L, 7L, 6L, 6L, 6L, 7L, 6L, 6L, 6L, 7L, 6L, 6L, 6L, 7L,
- 6L, 6L, 6L, 7L, 6L, 6L, 6L, 7L, 6L, 6L, 6L, 7L, 6L, 6L, 4L,
- 7L, 6L, 6L, 4L, 7L, 6L, 6L, 4L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
- 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
- 7L, 7L, 7L, 7L, 7L, 7L), Gender_pup = structure(c(2L, 2L,
- 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
- 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
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- 1L, 2L, 2L, 2L, 2L), .Label = c("M", "F"), class = "factor""ID_mum", "ID_pup", "Day", "Weight", "Treatment",
- "Site", "Enclosure", "Litter_size", "Gender_pup", "log.weight"
- ), row.names = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
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- 651L, 652L, 653L, 654L, 655L, 656L, 657L, 658L, 659L, 660L), na.action = structure(c(97L,
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- "153", "154", "155", "156", "185", "186", "187", "188", "189",
- "190", "191", "192", "193", "194", "195", "196", "317", "318",
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- "626", "627", "628", "629", "630", "631", "632"), class = "omit"), class = "data.frame")
- .df <- data.frame(x = pups.alt.NONA$Weight, y = pups.alt.NONA$Day, z=pups.alt.NONA$Litter_size, treat=pups.alt.NONA$Treatment)
- levels(.df$treat)[levels(.df$treat)=="PT"] <- "PO"
- .df$treat <- with(.df, factor(treat, levels=c('PO','AP','C')))
- .df$color_map <- with(.df, ifelse(treat=="PO","rgb(68, 1, 84)", ifelse(treat=='AP',"rgb(40, 125, 142)", "rgb(253, 231, 37)" )))
- ### Calculate z on a grid of x-y values
- x2.seq <- seq(min(.df$y),max(.df$y),length.out=30)
- x1.seq <- seq(min(.df$z),max(.df$z),length.out=30)
- z <- t(outer(x1.seq, x2.seq, function(x,y) exp(0.8657611+0.0943184*y-0.0389263*x)))
- z2 <- t(outer(x1.seq, x2.seq, function(x,y) exp((0.8657611+0.4887420)+(0.0943184-0.0437714)*y-(0.0389263+0.0833936)*x+(-0.0008683+0.0071139)*x*y)))
- #### Draw the plane with "plot_ly" and add points with "add_trace"
- library(plotly)
- p <- plot_ly(x=~x1.seq, y=~x2.seq, z=~z, colorscale = list(c(0,1),c("rgb(253, 231, 37)","rgb(253, 231, 37)")),
- type="surface", opacity=0.35, showlegend=F, showscale = FALSE) %>%
- add_trace(inherit=F, x=~x1.seq, y=~x2.seq, z=~z2, colorscale = list(c(0,1),c("rgb(40, 125, 142)","rgb(40, 125, 142)")),
- type="surface", opacity=0.35, showlegend=F, showscale = FALSE) %>%
- add_trace(inherit=F, data=.df, x=~z, y=~y, z=~x, mode="markers", type="scatter3d",
- marker = list(opacity=0.6, symbol=105, size=7, color=~color_map)) %>%
- layout(legend = list(x = 0.1, y = 0.9), scene = list(
- aspectmode = "manual", aspectratio = list(x=1, y=1, z=1),
- xaxis = list(title = "Litter size", range = c(2,7)),
- yaxis = list(title = "Day", range = c(0,20)),
- zaxis = list(title = "Weight", range = c(0.5,15))))
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