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带有多个协变量的分段回归，带有分段包

发布于 2025-02-13 14:57:47 字数 19233 浏览 3 评论 0原文

我想通过R上的分段包进行一些分段回归，但是我是此软件包的初学者，但我找不到如何做我想做的事情。

在这里，我有一个带有6个曲线的图，该图显示了3个参数的性能（轴y）（x轴上的数量；质量，以颜色为特征；温度，以颜色的梯度区分） “我的原始图，没有分段回归” 。 （颜色是温度和质量的组合，可以在我的ggplot上区分它们，但是每个变量都独立并且结合在一起）。

我想对每条曲线进行分段回归，以比较这些曲线的断点点，但是使用我的代码，我只得到一个 ” 。 这是为此的代码：

fitJ4 = lm(growth_rate ~ quantity, data = donnees_tot_g_J4)
segmented.fitJ4 = segmented(fitJ4, seg.Z = ~quantity)
fit=numeric(length(donnees_tot_g_J4$quantity))*NA
fit[complete.cases(rowSums(cbind(donnees_tot_g_J4$growth_rate,donnees_tot_g_J4$quantity)))] = broken.line(segmented.fitJ4)$fit

ggplot(donnees_tot_g_J4, aes(x=quantity, y=growth_rate, col=color))+
  geom_point(size=3,aes(col=color))+
  geom_line (aes (x = quantity, y = fit, color= color), alpha=0.2)+
  scale_x_continuous(breaks=c(0.1,0.3,0.6,0.9,1.5), limits=c(0.1,1.5))+
  scale_y_continuous(limits=c(0,0.8))+
  scale_colour_manual(values=c("20S" = "aquamarine1","25S" = "aquamarine3","28S" = "aquamarine4","20Y" = "darkgoldenrod1","25Y" = "darkgoldenrod3", "28Y" = "darkgoldenrod4"))+
  scale_fill_manual(values=c("20S" = "aquamarine1","25S" = "aquamarine3","28S" = "aquamarine4","20Y" = "darkgoldenrod1", "25Y" = "darkgoldenrod3","28Y" = "darkgoldenrod4"))+
  theme_minimal()+
  xlab("quantity") + ylab("perf")+
  theme_grey(base_size = 22)

在这里，我知道问题是我只为seg.z编写“数量”，但是当我试图编写它时：

segmented.fitJ4 = segmented(fitJ4, seg.Z = ~quantity+quality+temperature)

它不起作用，因为z和psi的名称是“长度或名称”不匹配”。因此，我知道我需要定义PSI，但是我不知道该为质量和温度提供什么...

我希望这很清楚。我给您一些我的数据尝试。谢谢你！

structure(list(name = c("J4_S03AC", "J4_S03BC", "J4_S03CC", "J4_S03DC", 
"J4_S06BC", "J4_S06CC", "J4_S06DC", "J4_S09AC", "J4_S09BC", "J4_S09CC", 
"J4_S09DC", "J4_Y03AC", "J4_Y03BC", "J4_Y03CC", "J4_Y06AC", "J4_Y06BC", 
"J4_Y06CC", "J4_Y06DC", "J4_Y09AC", "J4_Y09BC", "J4_Y09CC", "J4_Y09DC", 
"J4_S01AM", "J4_S01BM", "J4_S01CM", "J4_S01DM", "J4_S15AM", "J4_S15BM", 
"J4_S15CM", "J4_Y01AM", "J4_Y01BM", "J4_Y01CM", "J4_Y01DM", "J4_Y15AM", 
"J4_Y15BM", "J4_Y15CM", "J4_S01AC", "J4_S01CC", "J4_S01EC", "J4_S01FC", 
"J4_S03BC", "J4_S03CC", "J4_S03DC", "J4_S03EC", "J4_S03FC", "J4_S06AC", 
"J4_S06DC", "J4_S06EC", "J4_S06FC", "J4_S06KC", "J4_S06MC", "J4_S06NC", 
"J4_S09AC", "J4_S09BC", "J4_S09CC", "J4_S09DC", "J4_Y01AC", "J4_Y01BC", 
"J4_Y01CC", "J4_Y06AC", "J4_Y06BC", "J4_Y06CC", "J4_Y06DC", "J4_Y09AC", 
"J4_Y09BC", "J4_Y09CC", "J4_Y09DC", "J4_S03AM", "J4_S03BM", "J4_S03CM", 
"J4_S15AM", "J4_S15BM", "J4_S15CM", "J4_S15DM", "J4_Y03AM", "J4_Y03BM", 
"J4_Y03CM", "J4_Y03DM", "J4_Y15AM", "J4_Y15BM", "J4_Y15CM", "J4_Y15DM", 
"J4_Y15AC", "J4_Y15BC", "J4_Y15CC", "J4_Y15DC", "J4_Y15EC", "J4_Y15FC", 
"J4_Y15GC", "J4_Y15HC", "J4_Y15IC", "J4_Y15JC", "J4_Y15KC", "J4_Y15LC", 
"J4_Y15MC", "J4_Y15NC", "J4_Y15OC", "J4_Y15PC", "J4_Y15QC", "J4_Y15RC", 
"J4_Y15SC", "J4_Y15TC", "J4_Y15UC", "J4_Y15VC", "J4_Y15WC", "J4_S01AAC", 
"J4_S01AC", "J4_S01BC", "J4_S01CC", "J4_S06AC", "J4_S06BC", "J4_S06CC", 
"J4_S06DC", "J4_S09AC", "J4_S09BC", "J4_S09CC", "J4_S09DC", "J4_Y01AC", 
"J4_Y01CC", "J4_Y06AC", "J4_Y06BC", "J4_Y06CC", "J4_Y06DC", "J4_Y09AC", 
"J4_Y09BC", "J4_Y09CC", "J4_Y09DC", "J4_S03AM", "J4_S03BM", "J4_S03CM", 
"J4_S15AM", "J4_S15BM", "J4_S15CM", "J4_Y03AM", "J4_Y03BM", "J4_Y03CM", 
"J4_Y03DM", "J4_Y15AM", "J4_Y15BM", "J4_Y15CM", "J4_Y15DM"), 
    day = c("J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
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    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4"), quality = c("S", "S", "S", "S", "S", "S", "S", 
    "S", "S", "S", "S", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
    "Y", "Y", "Y", "S", "S", "S", "S", "S", "S", "S", "Y", "Y", 
    "Y", "Y", "Y", "Y", "Y", "S", "S", "S", "S", "S", "S", "S", 
    "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", 
    "S", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
    "S", "S", "S", "S", "S", "S", "S", "Y", "Y", "Y", "Y", "Y", 
    "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
    "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
    "Y", "Y", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", 
    "S", "S", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
    "S", "S", "S", "S", "S", "S", "Y", "Y", "Y", "Y", "Y", "Y", 
    "Y", "Y"), quantity = c(0.3, 0.3, 0.3, 0.3, 0.6, 0.6, 0.6, 
    0.9, 0.9, 0.9, 0.9, 0.3, 0.3, 0.3, 0.6, 0.6, 0.6, 0.6, 0.9, 
    0.9, 0.9, 0.9, 0.1, 0.1, 0.1, 0.1, 1.5, 1.5, 1.5, 0.1, 0.1, 
    0.1, 0.1, 1.5, 1.5, 1.5, 0.1, 0.1, 0.1, 0.1, 0.3, 0.3, 0.3, 
    0.3, 0.3, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.9, 0.9, 0.9, 
    0.9, 0.1, 0.1, 0.1, 0.6, 0.6, 0.6, 0.6, 0.9, 0.9, 0.9, 0.9, 
    0.3, 0.3, 0.3, 1.5, 1.5, 1.5, 1.5, 0.3, 0.3, 0.3, 0.3, 1.5, 
    1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 
    1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 
    1.5, 1.5, 0.1, 0.1, 0.1, 0.1, 0.6, 0.6, 0.6, 0.6, 0.9, 0.9, 
    0.9, 0.9, 0.1, 0.1, 0.6, 0.6, 0.6, 0.6, 0.9, 0.9, 0.9, 0.9, 
    0.3, 0.3, 0.3, 1.5, 1.5, 1.5, 0.3, 0.3, 0.3, 0.3, 1.5, 1.5, 
    1.5, 1.5), qual_quant = c("S03", "S03", "S03", "S03", "S06", 
    "S06", "S06", "S09", "S09", "S09", "S09", "Y03", "Y03", "Y03", 
    "Y06", "Y06", "Y06", "Y06", "Y09", "Y09", "Y09", "Y09", "S01", 
    "S01", "S01", "S01", "S15", "S15", "S15", "Y01", "Y01", "Y01", 
    "Y01", "Y15", "Y15", "Y15", "S01", "S01", "S01", "S01", "S03", 
    "S03", "S03", "S03", "S03", "S06", "S06", "S06", "S06", "S06", 
    "S06", "S06", "S09", "S09", "S09", "S09", "Y01", "Y01", "Y01", 
    "Y06", "Y06", "Y06", "Y06", "Y09", "Y09", "Y09", "Y09", "S03", 
    "S03", "S03", "S15", "S15", "S15", "S15", "Y03", "Y03", "Y03", 
    "Y03", "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", 
    "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", 
    "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", 
    "Y15", "S01", "S01", "S01", "S01", "S06", "S06", "S06", "S06", 
    "S09", "S09", "S09", "S09", "Y01", "Y01", "Y06", "Y06", "Y06", 
    "Y06", "Y09", "Y09", "Y09", "Y09", "S03", "S03", "S03", "S15", 
    "S15", "S15", "Y03", "Y03", "Y03", "Y03", "Y15", "Y15", "Y15", 
    "Y15"), temperature = c(20, 20, 20, 20, 20, 20, 20, 20, 20, 
    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 
    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 28, 28, 28, 
    28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 
    28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 
    28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 
    28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 
    28, 28, 28, 28, 28, 28, 28, 25, 25, 25, 25, 25, 25, 25, 25, 
    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25), time = c(105, 
    105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 
    105, 105, 105, 105, 105, 105, 105, 105, 105, 112, 112, 112, 
    112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 101, 
    101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 
    101, 101, 101, 102, 102, 102, 102, 102, 102, 102, 102, 102, 
    102, 102, 102, 102, 102, 102, 107.5, 107.5, 107.5, 107.5, 
    107.5, 107.5, 107.5, 107.5, 107.5, 107.5, 107.5, 107.5, 107.5, 
    107.5, 107.5, 108, 108, 108, 108, 108, 108, 108, 108, 108, 
    108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 
    108, 108, 109, 104, 104, 104, 104, 104, 104, 104, 104, 104, 
    104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 
    112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 
    112, 112), size = c(1681.616, 1769.388, 1640.182, 1749.236, 
    1772.734, 1751.192, 1538.992, 1771.498, 1712.612, 1648.414, 
    1964.914, 1693.468, 1990.782, 1660.296, 2125.122, 2181.51, 
    1936.8, 2047.176, 1886.724, 2262.896, 2267.56, 2078.214, 
    1523.922, 1452.24, 1621.674, 1479.096, 1625.708, 1826.186, 
    1675.434, 1591.866, 1574.092, 1583.592, 1595.3, 2199.116, 
    2334.592, 2098.526, 1344.366, 1291.962, 1304.811, 1128.264, 
    1286.151, 1358.421, 1396.671, 1353.076, 1505.565, 1297.17, 
    0, 1243.029, 1323.85, 1368.364, 1506.396, 0, 1663.735, 1632.28, 
    2115.303, 1921.46, 1407.825, 1248.956, 1402.668, 1910.167, 
    2106.313, 2016.868, 2043.982, 2188.033, 1983.409, 2326.42, 
    2082.324, 1506.581, 1501.196, 1370.99, 1870.489, 1941.425, 
    1942.186, 1827.395, 1713.149, 1996.395, 2011.623, 1798.349, 
    2171.226, 2252.35, 2071.279, 2485.113, 2215.084, 2380.252, 
    2337.003, 2384.119, 2371.255, 2437.183, 2355.166, 2384.359, 
    2256.135, 2413.416, 2251.848, 2326.666, 2463.171, 2419.457, 
    2418.257, 2409.51, 2399.095, 2415.043, 2474.581, 2529.032, 
    2401.006, 2392.16, 2462.465, 1336.588, 1184.353, 1239.748, 
    1460.383, 1635.684, 1724.768, 1754.185, 1737.566, 1753.176, 
    1959.044, 1701.156, 1770.752, 1336.1, 1372.907, 2175.332, 
    1997.874, 1896.37, 2269.925, 2213.498, 2192.229, 2041.227, 
    2474.221, 1628.119, 1662.795, 1578.399, 1911.944, 2045.866, 
    2190.013, 1690.419, 1672.256, 2133.311, 1772.034, 2423.857, 
    2492.047, 2703.446, 2459.673), weight = c(34, 53, 36, 34, 
    42, 46, 48, 63, 54, 51, 71, 65, 63, 61, 91, 93, 96, 84, 93, 
    137, 140, 125, 18, 26, 22, 21, 47, 52, 44, 20, 19, 19, 24, 
    155, 142, 136, 11, 16, 10, 10, 12, 12, 13, 16, 14, 12, 11, 
    12, 10, 10, 15, 25, 46, 35, 66, 46, 17, 15, 11, 71, 68, 68, 
    58, 115, 96, 114, 82, 20, 16, 15, 49, 73, 63, 60, 35, 39, 
    44, 27, 99, 94, 86, 168, 126, 134, 124, 130, 139, 183, 136, 
    143, 79, 129, 163, 148, 164, 186, 144, 155, 125, 133, 143, 
    161, 135, 142, 155, 11, 13, 12, 17, 39, 42, 42, 28, 37, 37, 
    20, 39, 14, 15, 70, 61, 53, 73, 107, 74, 77, 93, 28, 23, 
    24, 50, 42, 68, 31, 23, 38, 31, 119, 139, 162, 112), fecundity = c(0, 
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    0, 6, 0, 0, 9, 6, 7, 7, 6, 7, 10, 7, 8, 5, 6, 10, 7, 8, 8, 
    7, 6, 8, 8, 9, 8, 8, 2, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 7, 8, 12, 6), metabolic_rate = c(0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.346, 
    0.357, 0.3585, 0.512, 1.528, 1.927, 1.431, 0.225, 0.268, 
    0.471, 0.477, 3.334, 3.103, 3.384, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0.382, 0.1435, 0.2405, 1.436, 2.014, 3.464, 2.742, 
    1.624, 1.909, 2.014, 1.392, 5.058, 4.439, 4.242, 7.193, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA), time_maturity = c(0, 0, 0, 0, 0, 0, 0, 0, 
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    104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 
    104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139.52, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 110.03, 110.03, 110.03, 110.03
    ), weight_maturity = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
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    0, 0, 0, 0, 0, 0, 0, 0, 0, 142.231, 0, 0, 142.231, 142.231, 
    142.231, 142.231, 142.231, 142.231, 142.231, 142.231, 142.231, 
    142.231, 142.231, 142.231, 142.231, 142.231, 142.231, 142.231, 
    142.231, 142.231, 142.231, 142.231, 142.231, 142.231, 142.231, 
    142.231, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, NA, 
    NA, NA), aire = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, NA, 
    NA, NA), jour = c(4.375035, 4.375035, 4.375035, 4.375035, 
    4.375035, 4.375035, 4.375035, 4.375035, 4.375035, 4.375035, 
    4.375035, 4.375035, 4.375035, 4.375035, 4.375035, 4.375035, 
    4.375035, 4.375035, 4.375035, 4.375035, 4.375035, 4.375035, 
    4.666704, 4.666704, 4.666704, 4.666704, 4.666704, 4.666704, 
    4.666704, 4.666704, 4.666704, 4.666704, 4.666704, 4.666704, 
    4.666704, 4.666704, 4.208367, 4.208367, 4.208367, 4.208367, 
    4.208367, 4.208367, 4.208367, 4.208367, 4.208367, 4.208367, 
    4.208367, 4.208367, 4.208367, 4.208367, 4.208367, 4.208367, 
    4.250034, 4.250034, 4.250034, 4.250034, 4.250034, 4.250034, 
    4.250034, 4.250034, 4.250034, 4.250034, 4.250034, 4.250034, 
    4.250034, 4.250034, 4.250034, 4.4792025, 4.4792025, 4.4792025, 
    4.4792025, 4.4792025, 4.4792025, 4.4792025, 4.4792025, 4.4792025, 
    4.4792025, 4.4792025, 4.4792025, 4.4792025, 4.4792025, 4.4792025, 
    4.500036, 4.500036, 4.500036, 4.500036, 4.500036, 4.500036, 
    4.500036, 4.500036, 4.500036, 4.500036, 4.500036, 4.500036, 
    4.500036, 4.500036, 4.500036, 4.500036, 4.500036, 4.500036, 
    4.500036, 4.500036, 4.500036, 4.500036, 4.500036, 4.541703, 
    4.333368, 4.333368, 4.333368, 4.333368, 4.333368, 4.333368, 
    4.333368, 4.333368, 4.333368, 4.333368, 4.333368, 4.333368, 
    4.333368, 4.333368, 4.333368, 4.333368, 4.333368, 4.333368, 
    4.333368, 4.333368, 4.333368, 4.666704, 4.666704, 4.666704, 
    4.666704, 4.666704, 4.666704, 4.666704, 4.666704, 4.666704, 
    4.666704, 4.666704, 4.666704, 4.666704, 4.666704), growth_rate = c(0.303800071834841, 
    0.405269291837872, 0.316864747623708, 0.303800071834841, 
    0.352098906853808, 0.37289228981091, 0.382620123855391, 0.444775904433979, 
    0.409541745203879, 0.396477069415012, 0.472100291701688, 
    0.451919285802152, 0.444775904433979, 0.437402052060633, 
    0.52882661034269, 0.533795710392497, 0.541052497667245, 0.510531280665662, 
    0.533795710392497, 0.622339329512085, 0.627290489167077, 
    0.601386996896272, 0.148530350448613, 0.22732788723803, 0.191530869757777, 
    0.181562374726831, 0.354194957377592, 0.375858237686643, 
    0.34006122020639, 0.17110742318728, 0.160116090892034, 0.160116090892034, 
    0.210176015665816, 0.609895236463043, 0.591124373918946, 
    0.58187326824239, 0.0378568479840844, 0.126892202895244, 
    0.0152090656484838, 0.0152090656484838, 0.0585326533474545, 
    0.0585326533474545, 0.0775525505364441, 0.126892202895244, 
    0.0951622248242906, 0.0585326533474545, 0.0378568479840844, 
    0.0585326533474545, 0.0152090656484838, 0.0152090656484838, 
    0.111556439370444, 0.232939774941222, 0.374129156018743, 
    0.309825356331568, 0.459072793066665, 0.374129156018743, 
    0.139912664472351, 0.110462748788381, 0.037485702415613, 
    0.476255040318073, 0.466096963496756, 0.466096963496756, 
    0.428670275938566, 0.589725250514494, 0.547235252341441, 
    0.587670283432171, 0.510146385710354, 0.169037347727828, 
    0.119219651092275, 0.104811166292231, 0.369092608582107, 
    0.458090402952369, 0.425199566970549, 0.414306966296783, 
    0.293973826472744, 0.318132945119474, 0.34506362927332, 0.236036906790036, 
    0.526107066406678, 0.514536911258616, 0.494679026731963, 
    0.644173648408422, 0.577262524996099, 0.590941947338372, 
    0.573706922030602, 0.584207479104045, 0.599082800695941, 
    0.660196138391623, 0.594234162286239, 0.605387349621597, 
    0.473522453920122, 0.582491482588926, 0.634477243690635, 
    0.613024542636099, 0.635836394376859, 0.66380955856688, 0.606935930414305, 
    0.623293981181731, 0.575491834795137, 0.589277366937424, 
    0.605387349621597, 0.63173374656699, 0.592594151562409, 0.603827901506372, 
    0.623293981181731, 0.0350783637283718, 0.0585003710820993, 
    0.0400291247735614, 0.120406949659012, 0.312024255660586, 
    0.329125958574745, 0.329125958574745, 0.235557836017841, 
    0.299875798966989, 0.299875798966989, 0.157911017972106, 
    0.312024255660586, 0.0756020739962579, 0.091523378507426, 
    0.44700785177329, 0.415249326331636, 0.382807597260579, 0.456691820247472, 
    0.544930205105051, 0.459831560988572, 0.469002332695363, 
    0.512569846762844, 0.218732276302281, 0.176580407607362, 
    0.185700252024063, 0.342978102747015, 0.305616961533692, 
    0.408867154152023, 0.240542679171189, 0.176580407607362, 
    0.284170677698895, 0.240542679171189, 0.528783863661652, 
    0.562072977352849, 0.594884658166908, 0.515792977199508), 
    color = c("20S", "20S", "20S", "20S", "20S", "20S", "20S", 
    "20S", "20S", "20S", "20S", "20Y", "20Y", "20Y", "20Y", "20Y", 
    "20Y", "20Y", "20Y", "20Y", "20Y", "20Y", "20S", "20S", "20S", 
    "20S", "20S", "20S", "20S", "20Y", "20Y", "20Y", "20Y", "20Y", 
    "20Y", "20Y", "28S", "28S", "28S", "28S", "28S", "28S", "28S", 
    "28S", "28S", "28S", "28S", "28S", "28S", "28S", "28S", "28S", 
    "28S", "28S", "28S", "28S", "28Y", "28Y", "28Y", "28Y", "28Y", 
    "28Y", "28Y", "28Y", "28Y", "28Y", "28Y", "28S", "28S", "28S", 
    "28S", "28S", "28S", "28S", "28Y", "28Y", "28Y", "28Y", "28Y", 
    "28Y", "28Y", "28Y", "28Y", "28Y", "28Y", "28Y", "28Y", "28Y", 
    "28Y", "28Y", "28Y", "28Y", "28Y", "28Y", "28Y", "28Y", "28Y", 
    "28Y", "28Y", "28Y", "28Y", "28Y", "28Y", "28Y", "28Y", "28S", 
    "25S", "25S", "25S", "25S", "25S", "25S", "25S", "25S", "25S", 
    "25S", "25S", "25Y", "25Y", "25Y", "25Y", "25Y", "25Y", "25Y", 
    "25Y", "25Y", "25Y", "25S", "25S", "25S", "25S", "25S", "25S", 
    "25Y", "25Y", "25Y", "25Y", "25Y", "25Y", "25Y", "25Y")), row.names = c(NA, 
-141L), class = c("tbl_df", "tbl", "data.frame"))
原文

I want to make some piecewise regression with the segmented package on R, but I'm a beginner with this package and I don't find how to do what I want.

Here, I have a plot with 6 curves that shows the performance (axis y) depending of 3 parameters (quantity on x-axis; quality, distinguished by colors ; temperature, distinguished by a gradient of color)my original plot, without piecewise regression.
(color is a combination of temperature and quality to distinguish them on my ggplot, but every variable exists independently and in combination).

I want to make a piecewise regression for each curve to compare breaking points of these curves, but with my code, I only get one the plot with my piecewise regression.
Here is the code for that :

fitJ4 = lm(growth_rate ~ quantity, data = donnees_tot_g_J4)
segmented.fitJ4 = segmented(fitJ4, seg.Z = ~quantity)
fit=numeric(length(donnees_tot_g_J4$quantity))*NA
fit[complete.cases(rowSums(cbind(donnees_tot_g_J4$growth_rate,donnees_tot_g_J4$quantity)))] = broken.line(segmented.fitJ4)$fit

ggplot(donnees_tot_g_J4, aes(x=quantity, y=growth_rate, col=color))+
  geom_point(size=3,aes(col=color))+
  geom_line (aes (x = quantity, y = fit, color= color), alpha=0.2)+
  scale_x_continuous(breaks=c(0.1,0.3,0.6,0.9,1.5), limits=c(0.1,1.5))+
  scale_y_continuous(limits=c(0,0.8))+
  scale_colour_manual(values=c("20S" = "aquamarine1","25S" = "aquamarine3","28S" = "aquamarine4","20Y" = "darkgoldenrod1","25Y" = "darkgoldenrod3", "28Y" = "darkgoldenrod4"))+
  scale_fill_manual(values=c("20S" = "aquamarine1","25S" = "aquamarine3","28S" = "aquamarine4","20Y" = "darkgoldenrod1", "25Y" = "darkgoldenrod3","28Y" = "darkgoldenrod4"))+
  theme_minimal()+
  xlab("quantity") + ylab("perf")+
  theme_grey(base_size = 22)

Here, I understood that the problem was that I only write "quantity" for the seg.z, but when I tried to write that :

segmented.fitJ4 = segmented(fitJ4, seg.Z = ~quantity+quality+temperature)

It doesn't work because "Length or names of Z and psi do not match". So, I know that I need to define the psi, but I don't know what to put in for the quality and temperature...

I hope it is clear. I give you some of my data to try. Thank you!

structure(list(name = c("J4_S03AC", "J4_S03BC", "J4_S03CC", "J4_S03DC", 
"J4_S06BC", "J4_S06CC", "J4_S06DC", "J4_S09AC", "J4_S09BC", "J4_S09CC", 
"J4_S09DC", "J4_Y03AC", "J4_Y03BC", "J4_Y03CC", "J4_Y06AC", "J4_Y06BC", 
"J4_Y06CC", "J4_Y06DC", "J4_Y09AC", "J4_Y09BC", "J4_Y09CC", "J4_Y09DC", 
"J4_S01AM", "J4_S01BM", "J4_S01CM", "J4_S01DM", "J4_S15AM", "J4_S15BM", 
"J4_S15CM", "J4_Y01AM", "J4_Y01BM", "J4_Y01CM", "J4_Y01DM", "J4_Y15AM", 
"J4_Y15BM", "J4_Y15CM", "J4_S01AC", "J4_S01CC", "J4_S01EC", "J4_S01FC", 
"J4_S03BC", "J4_S03CC", "J4_S03DC", "J4_S03EC", "J4_S03FC", "J4_S06AC", 
"J4_S06DC", "J4_S06EC", "J4_S06FC", "J4_S06KC", "J4_S06MC", "J4_S06NC", 
"J4_S09AC", "J4_S09BC", "J4_S09CC", "J4_S09DC", "J4_Y01AC", "J4_Y01BC", 
"J4_Y01CC", "J4_Y06AC", "J4_Y06BC", "J4_Y06CC", "J4_Y06DC", "J4_Y09AC", 
"J4_Y09BC", "J4_Y09CC", "J4_Y09DC", "J4_S03AM", "J4_S03BM", "J4_S03CM", 
"J4_S15AM", "J4_S15BM", "J4_S15CM", "J4_S15DM", "J4_Y03AM", "J4_Y03BM", 
"J4_Y03CM", "J4_Y03DM", "J4_Y15AM", "J4_Y15BM", "J4_Y15CM", "J4_Y15DM", 
"J4_Y15AC", "J4_Y15BC", "J4_Y15CC", "J4_Y15DC", "J4_Y15EC", "J4_Y15FC", 
"J4_Y15GC", "J4_Y15HC", "J4_Y15IC", "J4_Y15JC", "J4_Y15KC", "J4_Y15LC", 
"J4_Y15MC", "J4_Y15NC", "J4_Y15OC", "J4_Y15PC", "J4_Y15QC", "J4_Y15RC", 
"J4_Y15SC", "J4_Y15TC", "J4_Y15UC", "J4_Y15VC", "J4_Y15WC", "J4_S01AAC", 
"J4_S01AC", "J4_S01BC", "J4_S01CC", "J4_S06AC", "J4_S06BC", "J4_S06CC", 
"J4_S06DC", "J4_S09AC", "J4_S09BC", "J4_S09CC", "J4_S09DC", "J4_Y01AC", 
"J4_Y01CC", "J4_Y06AC", "J4_Y06BC", "J4_Y06CC", "J4_Y06DC", "J4_Y09AC", 
"J4_Y09BC", "J4_Y09CC", "J4_Y09DC", "J4_S03AM", "J4_S03BM", "J4_S03CM", 
"J4_S15AM", "J4_S15BM", "J4_S15CM", "J4_Y03AM", "J4_Y03BM", "J4_Y03CM", 
"J4_Y03DM", "J4_Y15AM", "J4_Y15BM", "J4_Y15CM", "J4_Y15DM"), 
    day = c("J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", "J4", 
    "J4", "J4"), quality = c("S", "S", "S", "S", "S", "S", "S", 
    "S", "S", "S", "S", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
    "Y", "Y", "Y", "S", "S", "S", "S", "S", "S", "S", "Y", "Y", 
    "Y", "Y", "Y", "Y", "Y", "S", "S", "S", "S", "S", "S", "S", 
    "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", 
    "S", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
    "S", "S", "S", "S", "S", "S", "S", "Y", "Y", "Y", "Y", "Y", 
    "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
    "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
    "Y", "Y", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", 
    "S", "S", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
    "S", "S", "S", "S", "S", "S", "Y", "Y", "Y", "Y", "Y", "Y", 
    "Y", "Y"), quantity = c(0.3, 0.3, 0.3, 0.3, 0.6, 0.6, 0.6, 
    0.9, 0.9, 0.9, 0.9, 0.3, 0.3, 0.3, 0.6, 0.6, 0.6, 0.6, 0.9, 
    0.9, 0.9, 0.9, 0.1, 0.1, 0.1, 0.1, 1.5, 1.5, 1.5, 0.1, 0.1, 
    0.1, 0.1, 1.5, 1.5, 1.5, 0.1, 0.1, 0.1, 0.1, 0.3, 0.3, 0.3, 
    0.3, 0.3, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.9, 0.9, 0.9, 
    0.9, 0.1, 0.1, 0.1, 0.6, 0.6, 0.6, 0.6, 0.9, 0.9, 0.9, 0.9, 
    0.3, 0.3, 0.3, 1.5, 1.5, 1.5, 1.5, 0.3, 0.3, 0.3, 0.3, 1.5, 
    1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 
    1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 
    1.5, 1.5, 0.1, 0.1, 0.1, 0.1, 0.6, 0.6, 0.6, 0.6, 0.9, 0.9, 
    0.9, 0.9, 0.1, 0.1, 0.6, 0.6, 0.6, 0.6, 0.9, 0.9, 0.9, 0.9, 
    0.3, 0.3, 0.3, 1.5, 1.5, 1.5, 0.3, 0.3, 0.3, 0.3, 1.5, 1.5, 
    1.5, 1.5), qual_quant = c("S03", "S03", "S03", "S03", "S06", 
    "S06", "S06", "S09", "S09", "S09", "S09", "Y03", "Y03", "Y03", 
    "Y06", "Y06", "Y06", "Y06", "Y09", "Y09", "Y09", "Y09", "S01", 
    "S01", "S01", "S01", "S15", "S15", "S15", "Y01", "Y01", "Y01", 
    "Y01", "Y15", "Y15", "Y15", "S01", "S01", "S01", "S01", "S03", 
    "S03", "S03", "S03", "S03", "S06", "S06", "S06", "S06", "S06", 
    "S06", "S06", "S09", "S09", "S09", "S09", "Y01", "Y01", "Y01", 
    "Y06", "Y06", "Y06", "Y06", "Y09", "Y09", "Y09", "Y09", "S03", 
    "S03", "S03", "S15", "S15", "S15", "S15", "Y03", "Y03", "Y03", 
    "Y03", "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", 
    "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", 
    "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", "Y15", 
    "Y15", "S01", "S01", "S01", "S01", "S06", "S06", "S06", "S06", 
    "S09", "S09", "S09", "S09", "Y01", "Y01", "Y06", "Y06", "Y06", 
    "Y06", "Y09", "Y09", "Y09", "Y09", "S03", "S03", "S03", "S15", 
    "S15", "S15", "Y03", "Y03", "Y03", "Y03", "Y15", "Y15", "Y15", 
    "Y15"), temperature = c(20, 20, 20, 20, 20, 20, 20, 20, 20, 
    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 
    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 28, 28, 28, 
    28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 
    28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 
    28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 
    28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 
    28, 28, 28, 28, 28, 28, 28, 25, 25, 25, 25, 25, 25, 25, 25, 
    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25), time = c(105, 
    105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 
    105, 105, 105, 105, 105, 105, 105, 105, 105, 112, 112, 112, 
    112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 101, 
    101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 
    101, 101, 101, 102, 102, 102, 102, 102, 102, 102, 102, 102, 
    102, 102, 102, 102, 102, 102, 107.5, 107.5, 107.5, 107.5, 
    107.5, 107.5, 107.5, 107.5, 107.5, 107.5, 107.5, 107.5, 107.5, 
    107.5, 107.5, 108, 108, 108, 108, 108, 108, 108, 108, 108, 
    108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 
    108, 108, 109, 104, 104, 104, 104, 104, 104, 104, 104, 104, 
    104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 
    112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 
    112, 112), size = c(1681.616, 1769.388, 1640.182, 1749.236, 
    1772.734, 1751.192, 1538.992, 1771.498, 1712.612, 1648.414, 
    1964.914, 1693.468, 1990.782, 1660.296, 2125.122, 2181.51, 
    1936.8, 2047.176, 1886.724, 2262.896, 2267.56, 2078.214, 
    1523.922, 1452.24, 1621.674, 1479.096, 1625.708, 1826.186, 
    1675.434, 1591.866, 1574.092, 1583.592, 1595.3, 2199.116, 
    2334.592, 2098.526, 1344.366, 1291.962, 1304.811, 1128.264, 
    1286.151, 1358.421, 1396.671, 1353.076, 1505.565, 1297.17, 
    0, 1243.029, 1323.85, 1368.364, 1506.396, 0, 1663.735, 1632.28, 
    2115.303, 1921.46, 1407.825, 1248.956, 1402.668, 1910.167, 
    2106.313, 2016.868, 2043.982, 2188.033, 1983.409, 2326.42, 
    2082.324, 1506.581, 1501.196, 1370.99, 1870.489, 1941.425, 
    1942.186, 1827.395, 1713.149, 1996.395, 2011.623, 1798.349, 
    2171.226, 2252.35, 2071.279, 2485.113, 2215.084, 2380.252, 
    2337.003, 2384.119, 2371.255, 2437.183, 2355.166, 2384.359, 
    2256.135, 2413.416, 2251.848, 2326.666, 2463.171, 2419.457, 
    2418.257, 2409.51, 2399.095, 2415.043, 2474.581, 2529.032, 
    2401.006, 2392.16, 2462.465, 1336.588, 1184.353, 1239.748, 
    1460.383, 1635.684, 1724.768, 1754.185, 1737.566, 1753.176, 
    1959.044, 1701.156, 1770.752, 1336.1, 1372.907, 2175.332, 
    1997.874, 1896.37, 2269.925, 2213.498, 2192.229, 2041.227, 
    2474.221, 1628.119, 1662.795, 1578.399, 1911.944, 2045.866, 
    2190.013, 1690.419, 1672.256, 2133.311, 1772.034, 2423.857, 
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    137, 140, 125, 18, 26, 22, 21, 47, 52, 44, 20, 19, 19, 24, 
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    20, 39, 14, 15, 70, 61, 53, 73, 107, 74, 77, 93, 28, 23, 
    24, 50, 42, 68, 31, 23, 38, 31, 119, 139, 162, 112), fecundity = c(0, 
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    0, 0, 0, 0, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
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    142.231, 142.231, 142.231, 142.231, 142.231, 142.231, 142.231, 
    142.231, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, NA, 
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    0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, NA, 
    NA, NA), jour = c(4.375035, 4.375035, 4.375035, 4.375035, 
    4.375035, 4.375035, 4.375035, 4.375035, 4.375035, 4.375035, 
    4.375035, 4.375035, 4.375035, 4.375035, 4.375035, 4.375035, 
    4.375035, 4.375035, 4.375035, 4.375035, 4.375035, 4.375035, 
    4.666704, 4.666704, 4.666704, 4.666704, 4.666704, 4.666704, 
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    4.500036, 4.500036, 4.500036, 4.500036, 4.500036, 4.541703, 
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    4.666704, 4.666704, 4.666704, 4.666704, 4.666704, 4.666704, 
    4.666704, 4.666704, 4.666704, 4.666704, 4.666704), growth_rate = c(0.303800071834841, 
    0.405269291837872, 0.316864747623708, 0.303800071834841, 
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-141L), class = c("tbl_df", "tbl", "data.frame"))

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