Efeito fixos e aleatórios
Caro Colegas da r-br e Walmes, Bom dia a todos. Eu posso incluir em um modelo uma variável independete no efeito aleatório e no efeito fixo assim como esta o modelo abaixo? Obrigado.
dput(estirpe_r_br)structure(list(Tratamento = structure(c(4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L), .Label = c("T_CN", "T_SN", "CIAT 899", "UFLA 02-100", "UFLA 02-127", "UFLA 02-68", "UFLA 04-195"), class = "factor"), Bloco = structure(c(1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L ), .Label = c("1", "2", "3", "4"), class = "factor"), Local = structure(c(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, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 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, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 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, 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, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 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, 6L), .Label = c("Lavras", "Lavras 2", "Patos de Minas", "Patos de Minas 2", "PO", "PO 2", "Bambui", "GM", "Luminarias", "Pitangui"), class = "factor"), MSPA = c(22.96, 20.75, 28.56, 22.95, 24.91, 15.2, 12, 20.65, 18.39, 16.18, 14.45, 17.14, 13.02, 26.95, 8.45, 11.47, 23.62, 10.62, 10.35, 19.78, 34.52, 34.16, 35.99, 47.89, 15.32, 12.61, 10.45, 10.45, 24.93, 33.3, 52.25, 19.67, 53.04, 48.77, 34.64, 57.85, 52.43, 36.78, 37.15, 24.28, 23.48, 32.41, 42.63, 32.35, 40.17, 25.84, 31.77, 46.27, 53.27, 29.05, 54.03, 43.19, 28.18, 49.61, 47.09, 42.65, 72.1, 78.8, 58.2, 63.9, 62.5, 90.8, 61.6, 35, 61.4, 98, 64, 68.6, 73.3, 99.3, 72.3, 47.3, 74.4, 45.8, 79.7, 66.6, 140.2, 105.2, 111.8, 127.8, 79.1, 100.8, 91.6, 69.1, 67.8, 76.6, 82.7, 76.4, 81.2, 75.1, 82.3, 73.5, 81, 79.4, 112.1, 73.3, 56.6, 91.8, 86.2, 79.4, 65.2, 85.8, 75.5, 66, 136.2, 152.5, 126.3, 129.8, 68.4, 71.3, 95.5, 64.9, 66.64, 43.91, 72.9, 53.02, 46.5, 50.22, 65.15, 35.87, 49.14, 49.84, 92.12, 34.62, 81.94, 51.85, 72.23, 72.92, 67.5, 46.71, 48.24, 58.98, 66.78, 56.53, 59.89, 38.13, 54.03, 52.78, 51.5, 62.31, 20.06, 26.91, 11.52, 51.73, 24.31, 12.99, 17.35, 26.46, 8.59, 14.13, 25.43, 26.04, 15.53, 16.8, 19.27, 27.85, 12.36, 19.09, 11.05, 41.24, 12.03, 20.47, 17.5, 26.95, 13.49, 16.04, 16.72, 14.69, 42.73, 42.28, 87.11, 54.69, 61.6, 90.5, 38.43, 101.82, 48.37, 72.41, 53.5, 60.12, 41.68, 44.1, 46.95, 43.33, 47.92, 36.53, 33.5, 42.78, 62.4, 75.84, 51.5, 62.83, 44.6, 42.08, 44.29, 44.25, 29.81, 18.11, 21.24, 40.49, 22.49, 26.99, 33.88, 25.28, 22.88, 21.6, 26.87, 19.58, 21.38, 22.74, 18.34, 33.79, 29.69, 25.85, 19.9, 40.38, 18.96, 29.66, 27.93, 14.67, 20.34, 31.48, 22.15, 28.91, 80.38, 98.7, 96.36, 115.54, 56.14, 127.9, 76.04, 65.84, 40.4, 54.64, 36.2, 31.96, 30.76, 72.56, 51.02, 23, 69.26, 75, 51.82, 74.38, 57, 106.94, 80.16, 35.02, 37.36, 38.92, 49.76, 23.4, 29.56, 36.61, 56.75, 51.24, 42.03, 23.76, 46.05, 34.88, 46.99, 37.68, 30.95, 37.45, 37.92, 46.35, 46.75, 37.85, 27.32, 30.34, 56.91, 47.88, 29.41, 33.5, 33.94, 36, 40.16, 33.35, 35.55, 35.3), Irrigacao = 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, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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("Sem", "Com"), class = "factor"), NT = 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, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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("2", "3", "4"), class = "factor"), Safra = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 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, 1L, 1L, 1L, 1L, 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, 1L, 1L, 1L, 1L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Seca", "Aguas", "Inverno"), class = "factor"), Prep_Solo = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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("CO", "PD" ), class = "factor")), .Names = c("Tratamento", "Bloco", "Local", "MSPA", "Irrigacao", "NT", "Safra", "Prep_Solo"), row.names = c(NA, -280L), class = "data.frame")
lmer1 <- lmer(MSPA ~ Tratamento + (1 + Tratamento | Local ), data = estirpe_r_br,+ control=lmerControl(optCtrl=list(maxfun=50000)))> > summary(lmer1)Linear mixed model fit by REML ['lmerMod'] Formula: MSPA ~ Tratamento + (1 + Tratamento | Local) Data: estirpe_r_br Control: lmerControl(optCtrl = list(maxfun = 50000))
REML criterion at convergence: 2253.7 Scaled residuals: Min 1Q Median 3Q Max -2.8159 -0.4992 -0.1138 0.4992 3.8454 Random effects: Groups Name Variance Std.Dev. Corr Local (Intercept) 1535.5 39.19 TratamentoT_SN 390.8 19.77 -0.90 TratamentoCIAT 899 573.7 23.95 -0.92 0.89 TratamentoUFLA 02-100 716.9 26.77 -0.80 0.71 0.95 TratamentoUFLA 02-127 635.4 25.21 -0.83 0.75 0.92 0.95 TratamentoUFLA 02-68 370.3 19.24 -0.91 0.98 0.92 0.77 0.84 TratamentoUFLA 04-195 496.5 22.28 -0.88 0.98 0.94 0.79 0.79 0.97 Residual 150.7 12.28 Number of obs: 280, groups: Local, 10 Fixed effects: Estimate Std. Error t value (Intercept) 60.399 12.543 4.815 TratamentoT_SN -17.636 6.828 -2.583 TratamentoCIAT 899 -15.847 8.056 -1.967 TratamentoUFLA 02-100 -9.145 8.901 -1.027 TratamentoUFLA 02-127 -10.860 8.430 -1.288 TratamentoUFLA 02-68 -15.496 6.676 -2.321 TratamentoUFLA 04-195 -16.052 7.562 -2.123 Correlation of Fixed Effects: (Intr) TrT_SN TCIAT8 TUFLA02-10 TUFLA02-12 TUFLA02-6 TratmntT_SN -0.857 TrtmCIAT899 -0.888 0.837 TUFLA02-100 -0.784 0.679 0.900 TUFLA02-127 -0.812 0.716 0.878 0.909 TrUFLA02-68 -0.868 0.903 0.854 0.729 0.792 TUFLA04-195 -0.851 0.913 0.884 0.755 0.759 0.898 -- Alisson Lucrecio da Costa
Alisson, Apesar de ser um modelo "declarável", não acredito que ele seja razoável para esse caso. Eu tô entendendo que se trata de um grupo de experimentos que avaliou tratamentos (efeito fixo) em vários locais (efeito aleatório). Sendo assim, é justificável você partir do modelo maior e abandonar termos nulos até chegar ao modelo ideal 1) y~trt+(1|loc/trt) : trt fixo, local e trt dentro de local como aleatório (interação aleatória) 2) y~trt+(1|loc) : interação nula? 3) y~(1|loc) : trt nulo? Aplicar anova() para os pares ao longo da sequência vai testar se o termo não comum tem efeito zero (variância zero para os aleatórios). O modelo que você especificou não faz sentido pro caso porque você tá dizendo que o efeito aleatório de local ocorre dentro de trt, o que não é verdade. Ao declarar assim, você estima uma variância diferente para o efeito de local dentro de cada nível de trt. Talvez esse modelo fosse apropriado numa situação com locais, blocos e tratamentos e onde a variância entre blocos (efeito aleatório) fosse à princípio diferente entre locais (efeito aleatório). Entende o que quero dizer? O locais deveriam ser níveis casualizados dentro dos níveis de tratamento para justificar (tratamento|local). Na realidade tem-se o contrário, trt casualizados dentro de locais. Além do mais, penso que deveria-se remover o intercepto declarando assim (0+trt|loc). Eu avaliei um experimento onde era de interesse avaliar as variâncias dentro de locais. O caso era uma amostra aleatória de isolados (13 deles) de fungos do brasil (região sul) e uma da califórnia. Para uma resposta y qualquer, patogenicidade digamos, existe diferença em termos de efeito médio do brasil para califórnia? Os locais foram tratados como fixos pois foram escolhidos pelo pesquisador que só quer comparar os dois. Existe quanto de variabilidade entre os isolados dentro de cada local? Isolados foram considerados como aleatório pois de fato não foram escolhidos e sim provenientes de armadilhas instaladas, etc, etc, ou seja, os níveis que participaram do experimento são uma amostra de todos os possíveis isolados ali existentes (aqui se assume que a tal armadilha não possui viés de seleção, que qualquer isolado seria selecionando com mesma chance). Não se deseja inferir sobre o efeito de um nível específico de isolado. O interesse está em descrever a distribuição dos efeitos (sua média, sua variância, etc). Os modelos avaliados foram. 1) y~loc+(0+loc|iso), variâncias diferentes para efeito de isolados dentro dos locais 2) y~loc+(1|iso), variâncias iguais para o efeito de isolados 3) y~(1|iso), efeito nulo de local À disposição. Walmes.
Qual a biblioteca disto?
Caro Colegas da r-br e Walmes,
Bom dia a todos.
Eu posso incluir em um modelo uma variável independete no efeito aleatório e no efeito fixo assim como esta o modelo abaixo?
Obrigado.
> dput(estirpe_r_br)
structure(list(Tratamento = structure(c(4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L,
6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L,
7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L,
4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L,
3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L,
6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L,
7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L,
4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L,
3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L,
6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L), .Label = c("T_CN", "T_SN", "CIAT 899", "UFLA 02-100",
"UFLA 02-127", "UFLA 02-68", "UFLA 04-195"), class = "factor"),
Bloco = structure(c(1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L,
2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L,
1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L,
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L,
2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L,
1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L,
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L,
2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L,
1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L,
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L,
2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L,
1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L,
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L,
2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L
), .Label = c("1", "2", "3", "4"), class = "factor"), Local = structure(c(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, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 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, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 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, 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, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 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, 6L), .Label = c("Lavras", "Lavras 2", "Patos de Minas",
"Patos de Minas 2", "PO", "PO 2", "Bambui", "GM", "Luminarias",
"Pitangui"), class = "factor"), MSPA = c(22.96, 20.75, 28.56,
22.95, 24.91, 15.2, 12, 20.65, 18.39, 16.18, 14.45, 17.14,
13.02, 26.95, 8.45, 11.47, 23.62, 10.62, 10.35, 19.78, 34.52,
34.16, 35.99, 47.89, 15.32, 12.61, 10.45, 10.45, 24.93, 33.3,
52.25, 19.67, 53.04, 48.77, 34.64, 57.85, 52.43, 36.78, 37.15,
24.28, 23.48, 32.41, 42.63, 32.35, 40.17, 25.84, 31.77, 46.27,
53.27, 29.05, 54.03, 43.19, 28.18, 49.61, 47.09, 42.65, 72.1,
78.8, 58.2, 63.9, 62.5, 90.8, 61.6, 35, 61.4, 98, 64, 68.6,
73.3, 99.3, 72.3, 47.3, 74.4, 45.8, 79.7, 66.6, 140.2, 105.2,
111.8, 127.8, 79.1, 100.8, 91.6, 69.1, 67.8, 76.6, 82.7,
76.4, 81.2, 75.1, 82.3, 73.5, 81, 79.4, 112.1, 73.3, 56.6,
91.8, 86.2, 79.4, 65.2, 85.8, 75.5, 66, 136.2, 152.5, 126.3,
129.8, 68.4, 71.3, 95.5, 64.9, 66.64, 43.91, 72.9, 53.02,
46.5, 50.22, 65.15, 35.87, 49.14, 49.84, 92.12, 34.62, 81.94,
51.85, 72.23, 72.92, 67.5, 46.71, 48.24, 58.98, 66.78, 56.53,
59.89, 38.13, 54.03, 52.78, 51.5, 62.31, 20.06, 26.91, 11.52,
51.73, 24.31, 12.99, 17.35, 26.46, 8.59, 14.13, 25.43, 26.04,
15.53, 16.8, 19.27, 27.85, 12.36, 19.09, 11.05, 41.24, 12.03,
20.47, 17.5, 26.95, 13.49, 16.04, 16.72, 14.69, 42.73, 42.28,
87.11, 54.69, 61.6, 90.5, 38.43, 101.82, 48.37, 72.41, 53.5,
60.12, 41.68, 44.1, 46.95, 43.33, 47.92, 36.53, 33.5, 42.78,
62.4, 75.84, 51.5, 62.83, 44.6, 42.08, 44.29, 44.25, 29.81,
18.11, 21.24, 40.49, 22.49, 26.99, 33.88, 25.28, 22.88, 21.6,
26.87, 19.58, 21.38, 22.74, 18.34, 33.79, 29.69, 25.85, 19.9,
40.38, 18.96, 29.66, 27.93, 14.67, 20.34, 31.48, 22.15, 28.91,
80.38, 98.7, 96.36, 115.54, 56.14, 127.9, 76.04, 65.84, 40.4,
54.64, 36.2, 31.96, 30.76, 72.56, 51.02, 23, 69.26, 75, 51.82,
74.38, 57, 106.94, 80.16, 35.02, 37.36, 38.92, 49.76, 23.4,
29.56, 36.61, 56.75, 51.24, 42.03, 23.76, 46.05, 34.88, 46.99,
37.68, 30.95, 37.45, 37.92, 46.35, 46.75, 37.85, 27.32, 30.34,
56.91, 47.88, 29.41, 33.5, 33.94, 36, 40.16, 33.35, 35.55,
35.3), Irrigacao = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L,
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NT = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
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1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
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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), .Label = c("Seca", "Aguas",
"Inverno"), class = "factor"), Prep_Solo = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
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("CO", "PD"
), class = "factor")), .Names = c("Tratamento", "Bloco",
"Local", "MSPA", "Irrigacao", "NT", "Safra", "Prep_Solo"), row.names = c(NA,
-280L), class = "data.frame")
> lmer1 <- lmer(MSPA ~ Tratamento + (1 + Tratamento | Local ), data = estirpe_r_br,
+ control=lmerControl(optCtrl=list(maxfun=50000)))
>
> summary(lmer1)
Linear mixed model fit by REML ['lmerMod']
Formula: MSPA ~ Tratamento + (1 + Tratamento | Local)
Data: estirpe_r_br
Control: lmerControl(optCtrl = list(maxfun = 50000))
REML criterion at convergence: 2253.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.8159 -0.4992 -0.1138 0.4992 3.8454
Random effects:
Groups Name Variance Std.Dev. Corr
Local (Intercept) 1535.5 39.19
TratamentoT_SN 390.8 19.77 -0.90
TratamentoCIAT 899 573.7 23.95 -0.92 0.89
TratamentoUFLA 02-100 716.9 26.77 -0.80 0.71 0.95
TratamentoUFLA 02-127 635.4 25.21 -0.83 0.75 0.92 0.95
TratamentoUFLA 02-68 370.3 19.24 -0.91 0.98 0.92 0.77 0.84
TratamentoUFLA 04-195 496.5 22.28 -0.88 0.98 0.94 0.79 0.79 0.97
Residual 150.7 12.28
Number of obs: 280, groups: Local, 10
Fixed effects:
Estimate Std. Error t value
(Intercept) 60.399 12.543 4.815
TratamentoT_SN -17.636 6.828 -2.583
TratamentoCIAT 899 -15.847 8.056 -1.967
TratamentoUFLA 02-100 -9.145 8.901 -1.027
TratamentoUFLA 02-127 -10.860 8.430 -1.288
TratamentoUFLA 02-68 -15.496 6.676 -2.321
TratamentoUFLA 04-195 -16.052 7.562 -2.123
Correlation of Fixed Effects:
(Intr) TrT_SN TCIAT8 TUFLA02-10 TUFLA02-12 TUFLA02-6
TratmntT_SN -0.857
TrtmCIAT899 -0.888 0.837
TUFLA02-100 -0.784 0.679 0.900
TUFLA02-127 -0.812 0.716 0.878 0.909
TrUFLA02-68 -0.868 0.903 0.854 0.729 0.792
TUFLA04-195 -0.851 0.913 0.884 0.755 0.759 0.898
--
Alisson Lucrecio da Costa
--------------------------------------------------------------------------------
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Leia o guia de postagem (http://www.leg.ufpr.br/r-br-guia) e forneça código mínimo reproduzível.
Caro Wal,mes, Bom dia. Entendo que locais não são níveis casualizados dentro de cada tratamento, entretanto o modelo y ~ trt + (1 | loc / trt) não retorna a variância de cada local (preciso disso para ter uma ideia de estabilidade de cada trt em cada local), diferente do que acontece no modelo y ~ trt + (0 + trt | loc). Assim não consigo responder minha pergunta Qual estirpe apresenta melhor fixção de N e maior estabilidade considerando todos os locais. Obrigado. On Fri, Jun 27, 2014 at 10:05 PM, Mauro Sznelwar <sznelwar@uol.com.br> wrote:
Qual a biblioteca disto?
Caro Colegas da r-br e Walmes,
Bom dia a todos.
Eu posso incluir em um modelo uma variável independete no efeito aleatório e no efeito fixo assim como esta o modelo abaixo?
Obrigado.
dput(estirpe_r_br)structure(list(Tratamento = structure(c(4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L), .Label = c("T_CN", "T_SN", "CIAT 899", "UFLA 02-100", "UFLA 02-127", "UFLA 02-68", "UFLA 04-195"), class = "factor"), Bloco = structure(c(1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L ), .Label = c("1", "2", "3", "4"), class = "factor"), Local = structure(c(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, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 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, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 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, 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, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 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, 6L), .Label = c("Lavras", "Lavras 2", "Patos de Minas", "Patos de Minas 2", "PO", "PO 2", "Bambui", "GM", "Luminarias", "Pitangui"), class = "factor"), MSPA = c(22.96, 20.75, 28.56, 22.95, 24.91, 15.2, 12, 20.65, 18.39, 16.18, 14.45, 17.14, 13.02, 26.95, 8.45, 11.47, 23.62, 10.62, 10.35, 19.78, 34.52, 34.16, 35.99, 47.89, 15.32, 12.61, 10.45, 10.45, 24.93, 33.3, 52.25, 19.67, 53.04, 48.77, 34.64, 57.85, 52.43, 36.78, 37.15, 24.28, 23.48, 32.41, 42.63, 32.35, 40.17, 25.84, 31.77, 46.27, 53.27, 29.05, 54.03, 43.19, 28.18, 49.61, 47.09, 42.65, 72.1, 78.8, 58.2, 63.9, 62.5, 90.8, 61.6, 35, 61.4, 98, 64, 68.6, 73.3, 99.3, 72.3, 47.3, 74.4, 45.8, 79.7, 66.6, 140.2, 105.2, 111.8, 127.8, 79.1, 100.8, 91.6, 69.1, 67.8, 76.6, 82.7, 76.4, 81.2, 75.1, 82.3, 73.5, 81, 79.4, 112.1, 73.3, 56.6, 91.8, 86.2, 79.4, 65.2, 85.8, 75.5, 66, 136.2, 152.5, 126.3, 129.8, 68.4, 71.3, 95.5, 64.9, 66.64, 43.91, 72.9, 53.02, 46.5, 50.22, 65.15, 35.87, 49.14, 49.84, 92.12, 34.62, 81.94, 51.85, 72.23, 72.92, 67.5, 46.71, 48.24, 58.98, 66.78, 56.53, 59.89, 38.13, 54.03, 52.78, 51.5, 62.31, 20.06, 26.91, 11.52, 51.73, 24.31, 12.99, 17.35, 26.46, 8.59, 14.13, 25.43, 26.04, 15.53, 16.8, 19.27, 27.85, 12.36, 19.09, 11.05, 41.24, 12.03, 20.47, 17.5, 26.95, 13.49, 16.04, 16.72, 14.69, 42.73, 42.28, 87.11, 54.69, 61.6, 90.5, 38.43, 101.82, 48.37, 72.41, 53.5, 60.12, 41.68, 44.1, 46.95, 43.33, 47.92, 36.53, 33.5, 42.78, 62.4, 75.84, 51.5, 62.83, 44.6, 42.08, 44.29, 44.25, 29.81, 18.11, 21.24, 40.49, 22.49, 26.99, 33.88, 25.28, 22.88, 21.6, 26.87, 19.58, 21.38, 22.74, 18.34, 33.79, 29.69, 25.85, 19.9, 40.38, 18.96, 29.66, 27.93, 14.67, 20.34, 31.48, 22.15, 28.91, 80.38, 98.7, 96.36, 115.54, 56.14, 127.9, 76.04, 65.84, 40.4, 54.64, 36.2, 31.96, 30.76, 72.56, 51.02, 23, 69.26, 75, 51.82, 74.38, 57, 106.94, 80.16, 35.02, 37.36, 38.92, 49.76, 23.4, 29.56, 36.61, 56.75, 51.24, 42.03, 23.76, 46.05, 34.88, 46.99, 37.68, 30.95, 37.45, 37.92, 46.35, 46.75, 37.85, 27.32, 30.34, 56.91, 47.88, 29.41, 33.5, 33.94, 36, 40.16, 33.35, 35.55, 35.3), Irrigacao = 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, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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("Sem", "Com"), class = "factor"), NT = 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, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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("2", "3", "4"), class = "factor"), Safra = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 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, 1L, 1L, 1L, 1L, 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, 1L, 1L, 1L, 1L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Seca", "Aguas", "Inverno"), class = "factor"), Prep_Solo = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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("CO", "PD" ), class = "factor")), .Names = c("Tratamento", "Bloco", "Local", "MSPA", "Irrigacao", "NT", "Safra", "Prep_Solo"), row.names = c(NA, -280L), class = "data.frame")
lmer1 <- lmer(MSPA ~ Tratamento + (1 + Tratamento | Local ), data = estirpe_r_br,+ control=lmerControl(optCtrl=list(maxfun=50000)))> > summary(lmer1)Linear mixed model fit by REML ['lmerMod'] Formula: MSPA ~ Tratamento + (1 + Tratamento | Local) Data: estirpe_r_br Control: lmerControl(optCtrl = list(maxfun = 50000))
REML criterion at convergence: 2253.7
Scaled residuals: Min 1Q Median 3Q Max -2.8159 -0.4992 -0.1138 0.4992 3.8454
Random effects: Groups Name Variance Std.Dev. Corr Local (Intercept) 1535.5 39.19 TratamentoT_SN 390.8 19.77 -0.90 TratamentoCIAT 899 573.7 23.95 -0.92 0.89 TratamentoUFLA 02-100 716.9 26.77 -0.80 0.71 0.95 TratamentoUFLA 02-127 635.4 25.21 -0.83 0.75 0.92 0.95 TratamentoUFLA 02-68 370.3 19.24 -0.91 0.98 0.92 0.77 0.84 TratamentoUFLA 04-195 496.5 22.28 -0.88 0.98 0.94 0.79 0.79 0.97 Residual 150.7 12.28 Number of obs: 280, groups: Local, 10
Fixed effects: Estimate Std. Error t value (Intercept) 60.399 12.543 4.815 TratamentoT_SN -17.636 6.828 -2.583 TratamentoCIAT 899 -15.847 8.056 -1.967 TratamentoUFLA 02-100 -9.145 8.901 -1.027 TratamentoUFLA 02-127 -10.860 8.430 -1.288 TratamentoUFLA 02-68 -15.496 6.676 -2.321 TratamentoUFLA 04-195 -16.052 7.562 -2.123
Correlation of Fixed Effects: (Intr) TrT_SN TCIAT8 TUFLA02-10 TUFLA02-12 TUFLA02-6 TratmntT_SN -0.857 TrtmCIAT899 -0.888 0.837 TUFLA02-100 -0.784 0.679 0.900 TUFLA02-127 -0.812 0.716 0.878 0.909 TrUFLA02-68 -0.868 0.903 0.854 0.729 0.792 TUFLA04-195 -0.851 0.913 0.884 0.755 0.759 0.898
-- Alisson Lucrecio da Costa
------------------------------ _______________________________________________ R-br mailing list R-br@listas.c3sl.ufpr.br https://listas.inf.ufpr.br/cgi-bin/mailman/listinfo/r-br Leia o guia de postagem (http://www.leg.ufpr.br/r-br-guia) e forneça código mínimo reproduzível.
_______________________________________________ R-br mailing list R-br@listas.c3sl.ufpr.br https://listas.inf.ufpr.br/cgi-bin/mailman/listinfo/r-br Leia o guia de postagem (http://www.leg.ufpr.br/r-br-guia) e forneça código mínimo reproduzível.
-- Alisson Lucrecio da Costa
participantes (3)
-
Alisson Lucrecio -
Mauro Sznelwar -
walmes .