[R-br] Efeito fixos e aleatórios
Alisson Lucrecio
alissonluc em gmail.com
Quinta Julho 3 08:54:54 BRT 2014
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 em 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
>
> ------------------------------
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--
Alisson Lucrecio da Costa
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