R 中的多项式混合效应模型

Question

我想做一个具有随机效应的多项式模型，但我不知道怎么做。

模型看起来像这样：

native_driftertype ~treat+(1|replica)+(1|compartment/originhive)

,

native_driftertype

一个有 5 个水平的因素，

treat

一个有 3 个水平的因素，

replica

一个有 2 个水平的因素，

compartment

一个有 3 个水平的因素，以及

originhive

一个有 24 个水平的因素。

数据是这样的：

data6 <- structure(list(origin_hive = structure(c(1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 21L, 21L, 
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 23L, 23L, 23L, 23L, 23L, 
23L, 23L, 23L, 23L, 23L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 14L, 14L, 14L, 14L, 
14L, 14L, 14L, 14L, 14L, 14L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 
19L, 19L, 19L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 13L, 13L, 13L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 
17L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 18L, 18L, 
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 20L, 20L, 20L, 20L, 20L, 
20L, 20L, 20L, 20L, 20L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 
22L, 22L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 15L, 15L, 15L, 15L, 15L, 15L, 
15L, 15L, 15L, 15L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 
24L, 3L, 3L, 19L, 3L, 3L, 19L, 3L, 3L, 3L, 3L, 19L, 19L, 19L, 
3L, 3L, 3L, 6L, 9L, 6L, 6L, 6L, 6L, 9L, 6L, 9L, 9L, 6L, 6L, 6L, 
9L, 6L, 8L, 16L, 8L, 16L, 16L, 16L, 8L, 16L, 16L, 16L, 8L, 1L, 
1L, 23L, 14L, 1L, 23L, 1L, 23L, 1L, 3L, 7L, 3L, 19L, 3L, 9L, 
9L, 9L, 6L, 9L, 16L, 16L, 8L, 1L, 23L, 1L, 23L, 14L, 3L, 3L, 
7L, 7L, 9L, 11L, 11L, 16L, 16L, 8L, 21L, 23L, 1L, 23L, 19L, 3L, 
19L, 19L, 19L, 19L, 6L, 6L, 6L, 11L, 11L, 6L, 6L, 6L, 6L, 9L, 
9L, 6L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 1L, 14L, 1L, 
10L, 13L, 10L, 10L, 13L, 13L, 13L, 10L, 20L, 24L, 24L, 5L, 20L, 
20L, 20L, 5L, 20L, 24L, 5L, 5L, 20L, 12L, 17L, 12L, 12L, 12L, 
15L, 12L, 12L, 12L, 17L, 17L, 17L, 12L, 15L, 15L, 12L, 12L, 17L, 
12L, 15L, 17L, 12L, 12L, 12L, 12L, 12L, 22L, 22L, 2L, 4L, 22L, 
2L, 22L, 2L, 13L, 18L, 13L, 5L, 5L, 12L, 17L, 22L, 22L, 22L, 
22L, 13L, 13L, 18L, 18L, 18L, 20L, 20L, 20L, 20L, 5L, 5L, 5L, 
5L, 24L, 5L, 5L, 12L, 17L, 17L, 12L, 17L, 12L, 4L, 10L, 13L, 
18L, 13L, 10L, 5L, 5L, 24L, 20L, 20L, 20L, 5L, 20L, 24L, 12L, 
17L, 12L, 17L, 17L, 12L, 17L, 22L, 22L), levels = c("10C1", "10C2", 
"11C1", "11UV2", "12C2", "12UV1", "13G1", "14UV1", "16C1", "1UV2", 
"2G1", "2G2", "3C2", "4UV1", "4UV2", "5C1", "5C2", "6G2", "6UV1", 
"7G2", "8G1", "8G2", "9G1", "9UV2"), class = "factor"), treat = 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 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, 3L, 1L, 
    1L, 3L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 1L, 3L, 
    3L, 3L, 3L, 1L, 3L, 1L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 
    1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 3L, 1L, 2L, 1L, 
    2L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 
    1L, 2L, 1L, 2L, 3L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 3L, 
    2L, 2L, 1L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 
    3L, 3L, 3L, 3L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 3L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 2L, 3L, 3L, 1L, 
    2L, 2L, 2L, 1L, 2L, 3L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 3L, 
    2L, 2L, 2L, 1L, 1L, 1L, 2L, 3L, 3L, 2L, 2L, 1L, 2L, 3L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 1L, 2L, 1L, 1L, 2L, 
    1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 
    2L, 3L, 3L, 1L, 2L, 1L, 3L, 1L, 1L, 3L, 2L, 2L, 2L, 1L, 2L, 
    3L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L), levels = c("C", 
    "G", "UV"), class = "factor"), native_driftertype = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 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, 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, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 5L, 5L, 5L, 
    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), levels = c("native", 
    "Resident", "Transient", "Voyeur", "unknown"), class = "factor"), 
    replica = 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L), levels = c("1", "2"), class = "factor"), 
    compartment = 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 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, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 
    2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 
    1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 
    2L, 2L, 2L, 1L, 2L, 2L, 3L, 1L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 
    2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 3L, 
    1L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 1L, 3L, 3L, 2L, 3L, 
    1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 
    2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 
    1L, 3L, 3L, 1L, 1L, 1L, 3L, 2L, 3L, 3L, 1L, 2L, 2L, 2L, 1L, 
    2L, 3L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 1L, 
    1L, 1L, 2L, 3L, 3L, 2L, 2L, 1L, 2L, 3L, 1L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 3L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 
    1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
    1L, 1L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 3L, 3L, 1L, 
    2L, 1L, 3L, 1L, 1L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 1L, 2L, 
    1L, 1L, 2L, 1L, 2L, 2L), levels = c("12", "14", "16"), class = "factor")), row.names = c(NA, -464L
), class = "data.frame")

我尝试使用 brms 包中的

brm 函数（拟合贝叶斯广义（非）线性多元多级模型），但是在运行以下模型时，出现以下错误：

fit=brm(native_driftertype\~treat+(1|replica)+(1|compartment/origin_hive), family = multinomial, data = data6)

brm(native_driftertype ~ treat + (1 | replica) + (1 | compartment/origin_hive) 错误，： 未使用的参数（族 = 多项式，数据 = data6）